chore: bump version to 1.6.2

feat: clickable pills, configurable page size, CQRE.NET branding
- Service/category pills are now clickable: click to filter by that service - Result pills (Success, Failure, etc.) are now clickable: click to filter by that result - Click again to clear the filter (toggle behavior) - Change default page size from 100 to 25 - Add DEFAULT_PAGE_SIZE config (env var, default 25), exposed via /api/config/features - Change footer brand from CQRE to CQRE.NET - Add pill--clickable hover styles - Bump CSS cache-buster to v=10
2026-04-22 11:53:21 +02:00 · 2026-04-22 11:53:01 +02:00 · 2026-04-22 11:31:57 +02:00 · 2026-04-22 11:31:37 +02:00 · 2026-04-22 10:08:32 +02:00 · 2026-04-22 10:06:07 +02:00
32 changed files with 2759 additions and 120 deletions
--- a/.env.example
+++ b/.env.example
@@ -34,6 +34,10 @@ SIEM_WEBHOOK_URL=
 # Optional: enable rule-based alerting during ingestion
 ALERTS_ENABLED=false

+# Optional: enable AI/natural-language features (/api/ask, MCP server)
+# Set to false to completely disable AI endpoints and UI elements
+AI_FEATURES_ENABLED=true
+
 # Optional: LLM configuration for natural language querying (/api/ask)
 # Supports any OpenAI-compatible API (OpenAI, Azure OpenAI, Ollama, etc.)
 # For Azure OpenAI / MS Foundry, set BASE_URL to your deployment endpoint
@@ -42,6 +46,22 @@ ALERTS_ENABLED=false
 LLM_API_KEY=
 LLM_BASE_URL=https://api.openai.com/v1
 LLM_MODEL=gpt-4o-mini
-LLM_MAX_EVENTS=50
+LLM_MAX_EVENTS=200
 LLM_TIMEOUT_SECONDS=30
 LLM_API_VERSION=
+
+# Valkey (caching + async job queue for LLM calls)
+# In Docker Compose, this is set automatically to redis://redis:6379/0
+# For local dev, start Valkey with: docker run -d -p 6379:6379 valkey/valkey:8-alpine
+REDIS_URL=redis://localhost:6379/0
+
+# UI default page size (number of events shown per page)
+DEFAULT_PAGE_SIZE=25
+
+# Optional: privacy / access control
+# Hide entire services from users without PRIVACY_SERVICE_ROLES
+# PRIVACY_SERVICES=Exchange,Teams
+# Hide specific operations across all services from users without PRIVACY_SERVICE_ROLES
+# PRIVACY_SENSITIVE_OPERATIONS=MailItemsAccessed,Search-Mailbox,Send,ChatMessageRead
+# Comma-separated list of Entra roles that can access privacy-sensitive data
+# PRIVACY_SERVICE_ROLES=SecurityAdministrator,ComplianceAdministrator
--- a/.gitea/workflows/release.yml
+++ b/.gitea/workflows/release.yml
@@ -16,7 +16,13 @@ jobs:
        run: echo "${{ secrets.REGISTRY_TOKEN }}" | docker login git.cqre.net -u ${{ github.actor }} --password-stdin 2>&1 | grep -v "WARNING! Your credentials are stored unencrypted"

      - name: Build Docker image
-        run: docker build ./backend --tag git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }}
+        run: docker build ./backend --build-arg VERSION=${{ gitea.ref_name }} --tag git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }}

-      - name: Push Docker image
+      - name: Tag as latest
+        run: docker tag git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }} git.cqre.net/cqrenet/aoc-backend:latest
+
+      - name: Push version tag
        run: docker push git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }}
+
+      - name: Push latest tag
+        run: docker push git.cqre.net/cqrenet/aoc-backend:latest
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -6,28 +6,34 @@ AOC is a FastAPI microservice that ingests Microsoft Entra (Azure AD) audit logs

 ## Technology Stack

- **Runtime**: Python 3.11
- **Web Framework**: FastAPI + Uvicorn
+- **Runtime**: Python 3.11 (3.14 for tests)
+- **Web Framework**: FastAPI + Uvicorn (Gunicorn in production)
 - **Database**: MongoDB (PyMongo)
- **Frontend**: Vanilla HTML/CSS/JS (served as static files from `backend/frontend/`)
+- **Frontend**: Alpine.js + HTML/CSS (served as static files from `backend/frontend/`)
 - **Authentication**: Optional OIDC Bearer token validation against Microsoft Entra (using `python-jose` and MSAL.js on the frontend)
- **External APIs**: Microsoft Graph API, Office 365 Management Activity API
- **Deployment**: Docker Compose
+- **External APIs**: Microsoft Graph API, Office 365 Management Activity API, Azure OpenAI / MS Foundry
+- **Deployment**: Docker Compose (dev), Docker Compose + nginx (prod)
+- **CI/CD**: Gitea Actions (lint + test + Docker build + release)

 ## Project Structure

 ```
 backend/
  main.py              # FastAPI app, router registration, background periodic fetch
-  config.py            # Environment-based configuration (loads .env)
+  config.py            # Pydantic Settings configuration (loads .env)
  database.py          # MongoClient setup (db = micro_soc, collection = events)
  auth.py              # OIDC Bearer token validation, JWKS caching, role/group checks
  requirements.txt     # Python dependencies
-  Dockerfile           # python:3.11-slim image
+  Dockerfile           # python:3.11-slim image, non-root user, version baked at build
+  mcp_server.py        # Standalone MCP server for Claude Desktop / Cursor integration
  routes/
    fetch.py           # GET /api/fetch-audit-logs, run_fetch()
-    events.py          # GET /api/events, GET /api/filter-options
-    config.py          # GET /api/config/auth
+    events.py          # GET /api/events, GET /api/filter-options, PATCH tags, POST comments
+    config.py          # GET /api/config/auth, GET /api/config/features
+    ask.py             # POST /api/ask — natural language query with LLM
+    health.py          # GET /health, GET /metrics
+    rules.py           # Rule-based alerting endpoints
+    webhooks.py        # Microsoft Graph change notification webhooks
  graph/
    auth.py            # Client credentials token acquisition for Graph
    audit_logs.py      # Fetch and enrich directory audit logs from Graph
@@ -41,7 +47,7 @@ backend/
  mappings.yml         # User-editable category labels and summary templates
  maintenance.py       # CLI for re-normalization and deduplication of stored events
  frontend/
-    index.html         # Single-page UI with filters, pagination, raw-event modal
+    index.html         # Single-page UI with filters, pagination, ask panel, raw-event modal
    style.css          # Dark-themed stylesheet
 ```

@@ -60,6 +66,9 @@ Key variables:
 - `AUTH_ALLOWED_ROLES`, `AUTH_ALLOWED_GROUPS` — comma-separated access control lists
 - `ENABLE_PERIODIC_FETCH`, `FETCH_INTERVAL_MINUTES` — background ingestion scheduler
 - `MONGO_ROOT_USERNAME`, `MONGO_ROOT_PASSWORD`, `MONGO_PORT` — used by Docker Compose for MongoDB
+- `AI_FEATURES_ENABLED` — set `false` to completely disable AI endpoints and UI (default `true`)
+- `LLM_API_KEY`, `LLM_BASE_URL`, `LLM_MODEL`, `LLM_MAX_EVENTS`, `LLM_TIMEOUT_SECONDS` — LLM provider settings
+- `LLM_API_VERSION` — required for Azure OpenAI / MS Foundry endpoints

 ## Build and Run Commands

@@ -87,35 +96,81 @@ uvicorn main:app --reload --host 0.0.0.0 --port 8000
 ## API Endpoints

 - `GET /api/fetch-audit-logs?hours=168` — pulls last N hours (capped at 720 / 30 days) from all sources, normalizes, dedupes, and upserts into MongoDB
- `GET /api/events` — list stored events with filters (`service`, `actor`, `operation`, `result`, `start`, `end`, `search`) and pagination (`page`, `page_size`)
+- `GET /api/events` — list stored events with filters (`service`, `actor`, `operation`, `result`, `start`, `end`, `search`) and cursor-based pagination
 - `GET /api/filter-options` — best-effort distinct values for UI dropdowns
 - `GET /api/config/auth` — auth configuration exposed to the frontend
+- `GET /api/config/features` — feature flags (`ai_features_enabled`)
+- `POST /api/ask` — natural language query; returns LLM narrative + referenced events (only when `AI_FEATURES_ENABLED=true`)
+- `GET /health` — liveness probe with DB connectivity
+- `GET /metrics` — Prometheus metrics
+
+## MCP Server
+
+A standalone MCP server (`backend/mcp_server.py`) exposes audit log tools for Claude Desktop, Cursor, and other MCP clients.
+
+Available tools:
+- `search_events` — Search by entity, service, operation, result, time range
+- `get_event` — Retrieve a single event by ID (raw JSON)
+- `get_summary` — Aggregated counts by service, operation, result, actor
+- `ask` — Natural language question (returns recent events + guidance)
+
+**Claude Desktop config** (`~/.config/claude/claude_desktop_config.json`):
+```json
+{
+  "mcpServers": {
+    "aoc": {
+      "command": "python",
+      "args": ["/path/to/aoc/backend/mcp_server.py"],
+      "env": {"MONGO_URI": "mongodb://root:example@localhost:27017/"}
+    }
+  }
+}
+```
+
+The MCP server imports `database.py` directly and does not go through the FastAPI layer, so it shares the same MongoDB connection but bypasses auth.
+
+## AI Feature Flag
+
+Set `AI_FEATURES_ENABLED=false` in `.env` to:
+- Prevent the `ask` router from being registered in FastAPI
+- Hide the "Ask a question" panel in the frontend
+- Return `ai_features_enabled: false` from `/api/config/features`
+
+This is intended for the open-core monetization split: core features (ingestion, filtering, search, export) are always available; premium AI features (NLQ, MCP) can be disabled.

 ## Code Conventions

 - Python modules use absolute imports within the `backend/` package (e.g., `from graph.auth import get_access_token`). When running locally, ensure the working directory is `backend/` so these resolve correctly.
- No formal formatter or linter is configured. Keep changes consistent with the existing style: simple functions, explicit exception handling, and informative docstrings.
- The frontend is a single HTML file with inline JavaScript. It relies on the MSAL.js CDN (`https://alcdn.msauth.net/browser/2.37.0/js/msal-browser.min.js`).
+- The project uses `ruff` for linting and formatting. Run `ruff check . && ruff format .` before committing.
+- Keep changes consistent with the existing style: simple functions, explicit exception handling, and informative docstrings.
+- The frontend is a single HTML file with inline JavaScript and Alpine.js.

 ## Testing

-There are currently **no automated tests** in this repository. When adding new features or bug fixes, verify behavior manually:
+Tests run with pytest and mongomock (no real MongoDB required):

-1. Start the server (Docker Compose or local uvicorn).
-2. Run a smoke test:
-   ```bash
-   curl http://localhost:8000/api/events
-   curl http://localhost:8000/api/fetch-audit-logs
-   ```
-3. Open http://localhost:8000 in a browser, apply filters, paginate, and click "View raw event".
+```bash
+cd backend
+python -m venv .venv_test
+source .venv_test/bin/activate
+pip install -r requirements.txt
+pytest tests/ -q
+```
+
+When adding new features or bug fixes, add or update tests in `backend/tests/`. The test suite covers:
+- Event normalization and deduplication
+- Auth middleware and token validation
+- API endpoints (`/api/events`, `/api/fetch-audit-logs`, `/api/ask`)
+- NLQ time range extraction, entity extraction, query building

 ## Security Considerations

- **Secrets**: `CLIENT_SECRET` and other credentials come from `.env`. Never commit `.env`.
+- **Secrets**: `CLIENT_SECRET`, `LLM_API_KEY`, and other credentials come from `.env`. Never commit `.env`.
 - **Auth validation**: When `AUTH_ENABLED=true`, the backend fetches JWKS from `https://login.microsoftonline.com/{AUTH_TENANT_ID}/v2.0/.well-known/openid-configuration`, caches keys for 1 hour, and validates tenant/issuer claims. Tokens are decoded without strict signature verification (`jwt.get_unverified_claims`), so the tenant and issuer checks are the primary gate.
 - **Role/Group gating**: Access is allowed if the token’s `roles` intersect `AUTH_ALLOWED_ROLES` or `groups` intersect `AUTH_ALLOWED_GROUPS`. If neither list is configured, all authenticated users are allowed.
 - **Pagination limits**: `page_size` is clamped to a maximum of 500 to prevent large queries.
 - **Fetch window cap**: `hours` is clamped to 720 (30 days) to avoid runaway API calls.
+- **MCP server**: The MCP server bypasses auth entirely. Only run it in trusted environments or behind a VPN.

 ## Maintenance and Operations

--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@ FastAPI microservice that ingests Microsoft Entra (Azure AD) and other admin aud
 - Office 365 Management Activity API client for Exchange/SharePoint/Teams admin audit logs.
 - Frontend served from the backend for filtering/searching events and viewing raw entries.
 - Optional OIDC bearer auth (Entra) to protect the API/UI and gate access by roles/groups.
+- Natural language query (`/api/ask`) powered by LLM (OpenAI, Azure OpenAI, or any compatible API).
+- MCP server for Claude Desktop / Cursor integration.

 ## Prerequisites (macOS)
 - Python 3.11
@@ -38,6 +40,15 @@ cp .env.example .env

 # Optional: CORS origins if the frontend is served separately
 # CORS_ORIGINS=http://localhost:3000,https://app.example.com
+
+# Optional: enable AI/natural-language features (/api/ask, MCP server)
+# AI_FEATURES_ENABLED=true
+
+# Optional: LLM configuration for natural language querying
+# LLM_API_KEY=...
+# LLM_BASE_URL=https://api.openai.com/v1
+# LLM_MODEL=gpt-4o-mini
+# LLM_TIMEOUT_SECONDS=30
 ```

 ## Run with Docker Compose (recommended)
@@ -66,6 +77,7 @@ uvicorn main:app --reload --host 0.0.0.0 --port 8000
 ## API
 - `GET /health` — health check with MongoDB connectivity status.
 - `GET /metrics` — Prometheus metrics for request latency, fetch volume, and errors.
+- `GET /api/version` — running version (baked into the Docker image at build time).
 - `GET /api/fetch-audit-logs` — pulls the last 7 days by default (override with `?hours=N`, capped to 30 days) of:
  - Entra directory audit logs (`/auditLogs/directoryAudits`)
  - Exchange/SharePoint/Teams admin audits (via Office 365 Management Activity API)
@@ -82,11 +94,34 @@ uvicorn main:app --reload --host 0.0.0.0 --port 8000
 - `GET /api/source-health` — last fetch status for each ingestion source (`directory`, `unified`, `intune`).
 - `PATCH /api/events/{id}/tags` — update tags on an event (e.g., `investigating`, `false_positive`).
 - `POST /api/events/{id}/comments` — add a comment to an event.
+- `POST /api/events/{id}/explain` — AI explanation of a single audit event with security context (requires `LLM_API_KEY`).
+- `POST /api/ask` — natural language query. Returns a narrative answer + referenced events. Supports time ranges, entity names, and respects active UI filters. Only available when `AI_FEATURES_ENABLED=true`.
+- `GET /api/config/features` — feature flags (`ai_features_enabled`).
 - `GET /api/rules` — list alert rules.
 - `POST /api/rules` — create an alert rule.
 - `PUT /api/rules/{id}` — update an alert rule.
 - `DELETE /api/rules/{id}` — delete an alert rule.

+### MCP Server
+AOC exposes an MCP interface in two forms:
+
+**1. HTTP/SSE (production)** — mounted at `/mcp` inside the FastAPI app, behind OIDC auth:
+- `GET /mcp/sse` — establish SSE stream (requires Bearer token if `AUTH_ENABLED=true`)
+- `POST /mcp/messages/?session_id=...` — send tool calls
+
+This is the recommended way to use MCP against a remote deployment like `aoc.cqre.net`. Any MCP client that supports SSE transport (e.g. Cursor, Claude Desktop with an SSE bridge, or custom scripts) can connect using the same Entra token as the web UI.
+
+**2. stdio (local development)** — `python backend/mcp_server.py`:
+- Runs as a local subprocess for Claude Desktop
+- Connects directly to MongoDB (bypasses FastAPI auth)
+- Useful for local development when you have the repo cloned and MongoDB running locally
+
+Available tools (both transports):
+- `search_events` — filter by entity, service, operation, result, time range.
+- `get_event` — retrieve raw event JSON by ID.
+- `get_summary` — aggregated summary (service, operation, result, actor counts) for the last N days.
+- `ask` — natural language query returning recent events.
+
 Stored document shape (collection `micro_soc.events`):
 ```json
 {
--- a/RELEASE_NOTES_v1.1.0.md
+++ b/RELEASE_NOTES_v1.1.0.md
@@ -0,0 +1,56 @@
+# AOC v1.1.0 Release Notes
+
+**Release date:** 2026-04-20
+
+## What's new
+
+### Natural language query (`/api/ask`)
+Ask questions in plain English and get AI-generated answers backed by your audit logs.
+
+- **Regex-based parsing** extracts time ranges (`last 3 days`, `yesterday`, `today`) and entities (`device ABC123`, `user bob@example.com`) without calling an LLM — fast and deterministic.
+- **AI narrative summarisation** via any OpenAI-compatible API (OpenAI, Azure OpenAI, MS Foundry, Ollama). The LLM reads the matching events and writes a concise story for non-expert admins.
+- **Graceful fallback** when no LLM is configured — returns a structured bullet list instead of a narrative.
+- **Cited evidence** — every answer includes the raw events that back it up, so admins can verify claims.
+
+### Azure OpenAI / MS Foundry support
+- Automatic `api-key` header detection for Azure endpoints.
+- `LLM_API_VERSION` config for Azure `api-version` query parameters.
+- `max_completion_tokens` support for newer model deployments.
+
+### Production hardening
+- **Dockerfile:** runs as non-root user, uses Gunicorn + Uvicorn workers.
+- **docker-compose.prod.yml:** MongoDB is internal-only (no host port exposure), health checks on all services, nginx reverse proxy with security headers.
+- **nginx config:** gzip, security headers (`X-Frame-Options`, `X-Content-Type-Options`), ready for TLS.
+
+### Frontend
+- New **"Ask a question"** panel at the top of the page.
+- Markdown rendering for LLM answers (bold, italic, code).
+- Orange warning banner when LLM is not configured or fails.
+
+### Tests
+- 29 new tests covering ask parsing, query building, and endpoint behaviour.
+- 62 tests total, all passing.
+
+## Configuration
+
+Add to your `.env`:
+
+```bash
+# Required for AI narrative summarisation
+LLM_API_KEY=your-key
+LLM_BASE_URL=https://api.openai.com/v1
+LLM_MODEL=gpt-4o-mini
+LLM_MAX_EVENTS=50
+LLM_TIMEOUT_SECONDS=30
+LLM_API_VERSION=                 # set for Azure OpenAI, e.g. 2024-12-01-preview
+```
+
+## Upgrade notes
+
+No breaking changes. Existing `/api/events`, filters, pagination, tags, and comments work unchanged.
+
+## Docker image
+
+```
+git.cqre.net/cqrenet/aoc-backend:v1.1.0
+```
--- a/RELEASE_NOTES_v1.2.5.md
+++ b/RELEASE_NOTES_v1.2.5.md
@@ -0,0 +1,78 @@
+# AOC v1.2.5 Release Notes
+
+**Release date:** 2026-04-20
+
+---
+
+## What's new
+
+### Natural language query (`/api/ask`)
+Ask questions in plain English and get AI-generated answers backed by your audit logs.
+
+- **Regex-based parsing** extracts time ranges (`last 3 days`, `yesterday`, `today`) and entities (`device ABC123`, `user bob@example.com`) without calling an LLM.
+- **AI narrative summarisation** via any OpenAI-compatible API (OpenAI, Azure OpenAI, MS Foundry, Ollama).
+- **Graceful fallback** when no LLM is configured — returns a structured bullet list with a clear error banner.
+- **Cited evidence** — every answer includes the raw events that back it up.
+
+### Filter-aware queries
+The ask endpoint now respects the filter panel. When you set **Service = Exchange**, **Result = failure** and ask *"What happened to device X?"*, the LLM only sees failed Exchange events for that device.
+
+### Scales to thousands of events
+For large result sets (>50 events), the LLM receives an **aggregated overview** instead of a raw dump:
+- Counts by service, action, result, and actor
+- Failure highlights
+- The 50 most recent raw events as samples
+
+This keeps token usage low while preserving accuracy.
+
+### Azure OpenAI / MS Foundry support
+- Automatic `api-key` header detection for Azure endpoints.
+- `LLM_API_VERSION` config for Azure `api-version` query parameters.
+- `max_completion_tokens` support for newer model deployments.
+
+### Version display
+- `GET /api/version` endpoint reads the `VERSION` file.
+- Frontend shows a version badge in the header (e.g., **1.2.5**).
+
+### Production hardening (from v1.1.0)
+- Dockerfile runs as non-root user with Gunicorn + Uvicorn workers.
+- `docker-compose.prod.yml` with internal-only MongoDB, health checks, and nginx reverse proxy.
+- Security headers (`X-Frame-Options`, `X-Content-Type-Options`, etc.).
+
+---
+
+## Configuration
+
+Add to your `.env`:
+
+```bash
+# Required for AI narrative summarisation
+LLM_API_KEY=your-key
+LLM_BASE_URL=https://api.openai.com/v1
+LLM_MODEL=gpt-4o-mini
+LLM_MAX_EVENTS=200
+LLM_TIMEOUT_SECONDS=30
+LLM_API_VERSION=                 # set for Azure OpenAI, e.g. 2024-12-01-preview
+```
+
+For Azure OpenAI / MS Foundry:
+```bash
+LLM_BASE_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment
+LLM_API_KEY=your-azure-key
+LLM_API_VERSION=2024-12-01-preview
+LLM_MODEL=your-deployment-name
+```
+
+---
+
+## Upgrade notes
+
+No breaking changes. Existing `/api/events`, filters, pagination, tags, and comments work unchanged.
+
+---
+
+## Docker image
+
+```
+git.cqre.net/cqrenet/aoc-backend:v1.2.5
+```
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -59,5 +59,16 @@ Goal: evolve from a polling dashboard into a full security operations tool.

 ---

+## Phase 5: Intelligence
+Goal: add AI-powered analysis and external tool integration.
+
+- [x] AI feature flag (`AI_FEATURES_ENABLED`) to gate LLM-dependent features
+- [x] Natural language query endpoint (`/api/ask`) with intent extraction and smart sampling
+- [x] MCP (Model Context Protocol) server for Claude Desktop / Cursor integration
+- [x] Valkey caching for LLM responses and frequent queries
+- [x] Async queue (arq) for LLM requests to prevent timeout/cost explosions at scale
+- [ ] Advanced analytics dashboard (trending operations, anomaly detection)
+
 ## Completed in this PR
-All Phase 1 items were implemented in the latest changes.
+All Phase 5 items marked done were implemented in v1.3.0–v1.5.0.
+Redis caching + async queue implemented in v1.6.0, switched to Valkey.
--- a/2
+++ b/2
@@ -1 +1 @@
-1.1.0
+1.6.2
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -1,5 +1,9 @@
 FROM python:3.11-slim

+# Bake the version into the image at build time
+ARG VERSION=unknown
+ENV VERSION=${VERSION}
+
 # Security: run as non-root
 RUN groupadd -r aoc && useradd -r -g aoc aoc

--- a/backend/auth.py
+++ b/backend/auth.py
@@ -8,6 +8,8 @@ from config import (
    AUTH_CLIENT_ID,
    AUTH_ENABLED,
    AUTH_TENANT_ID,
+    PRIVACY_SERVICE_ROLES,
+    PRIVACY_SERVICES,
 )
 from fastapi import Header, HTTPException
 from jwt import ExpiredSignatureError, InvalidTokenError, decode
@@ -82,6 +84,14 @@ def _decode_token(token: str, jwks):
        raise HTTPException(status_code=401, detail=f"Invalid token ({type(exc).__name__})") from None


+def user_can_access_privacy_services(claims: dict) -> bool:
+    """Check if the user has roles that grant access to privacy-sensitive services."""
+    if not PRIVACY_SERVICES or not PRIVACY_SERVICE_ROLES:
+        return True
+    user_roles = set(claims.get("roles", []) or claims.get("role", []) or [])
+    return bool(user_roles.intersection(PRIVACY_SERVICE_ROLES))
+
+
 def require_auth(authorization: str | None = Header(None)):
    if not AUTH_ENABLED:
        return {"sub": "anonymous"}
--- a/backend/config.py
+++ b/backend/config.py
@@ -42,14 +42,27 @@ class Settings(BaseSettings):
    # Alerting
    ALERTS_ENABLED: bool = False

-    # LLM / Natural Language Query
+    # AI / Natural Language Query
+    AI_FEATURES_ENABLED: bool = True
    LLM_API_KEY: str = ""
    LLM_BASE_URL: str = "https://api.openai.com/v1"
    LLM_MODEL: str = "gpt-4o-mini"
-    LLM_MAX_EVENTS: int = 50
+    LLM_MAX_EVENTS: int = 200
    LLM_TIMEOUT_SECONDS: int = 30
    LLM_API_VERSION: str = ""  # e.g. 2025-01-01-preview for Azure OpenAI

+    # Privacy / access control
+    # Entire services can be hidden, or specific operations can be gated.
+    PRIVACY_SERVICES: str = ""  # comma-separated, e.g. "Exchange,Teams"
+    PRIVACY_SENSITIVE_OPERATIONS: str = ""  # comma-separated, e.g. "MailItemsAccessed,Search-Mailbox,Send"
+    PRIVACY_SERVICE_ROLES: str = ""  # comma-separated, e.g. "SecurityAdministrator,ComplianceAdministrator"
+
+    # Redis (caching + async job queue)
+    REDIS_URL: str = "redis://localhost:6379/0"
+
+    # UI defaults
+    DEFAULT_PAGE_SIZE: int = 25
+

 _settings = Settings()

@@ -77,9 +90,17 @@ SIEM_ENABLED = _settings.SIEM_ENABLED
 SIEM_WEBHOOK_URL = _settings.SIEM_WEBHOOK_URL
 ALERTS_ENABLED = _settings.ALERTS_ENABLED

+AI_FEATURES_ENABLED = _settings.AI_FEATURES_ENABLED
 LLM_API_KEY = _settings.LLM_API_KEY
 LLM_BASE_URL = _settings.LLM_BASE_URL
 LLM_MODEL = _settings.LLM_MODEL
 LLM_MAX_EVENTS = _settings.LLM_MAX_EVENTS
 LLM_TIMEOUT_SECONDS = _settings.LLM_TIMEOUT_SECONDS
 LLM_API_VERSION = _settings.LLM_API_VERSION
+
+PRIVACY_SERVICES = {s.strip() for s in _settings.PRIVACY_SERVICES.split(",") if s.strip()}
+PRIVACY_SENSITIVE_OPERATIONS = {o.strip() for o in _settings.PRIVACY_SENSITIVE_OPERATIONS.split(",") if o.strip()}
+PRIVACY_SERVICE_ROLES = {r.strip() for r in _settings.PRIVACY_SERVICE_ROLES.split(",") if r.strip()}
+
+REDIS_URL = _settings.REDIS_URL
+DEFAULT_PAGE_SIZE = _settings.DEFAULT_PAGE_SIZE
--- a/backend/database.py
+++ b/backend/database.py
@@ -7,6 +7,7 @@ from pymongo import ASCENDING, DESCENDING, TEXT, MongoClient
 client = MongoClient(MONGO_URI or "mongodb://localhost:27017")
 db = client[DB_NAME]
 events_collection = db["events"]
+saved_searches_collection = db["saved_searches"]
 logger = structlog.get_logger("aoc.database")


@@ -20,6 +21,7 @@ def setup_indexes(max_retries: int = 5, delay: float = 2.0):
            events_collection.create_index([("timestamp", DESCENDING)])
            events_collection.create_index([("service", ASCENDING), ("timestamp", DESCENDING)])
            events_collection.create_index("id")
+            saved_searches_collection.create_index([("created_by", ASCENDING), ("created_at", DESCENDING)])
            events_collection.create_index(
                [("actor_display", TEXT), ("raw_text", TEXT), ("operation", TEXT)],
                name="text_search_index",
--- a/backend/frontend/index.html
+++ b/backend/frontend/index.html
@@ -3,23 +3,49 @@
 <head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>AOC Events</title>
-  <link rel="stylesheet" href="/style.css?v=8" />
+  <title>Admin Operations Center</title>
+  <link rel="stylesheet" href="/style.css?v=10" />
  <script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.x.x/dist/cdn.min.js"></script>
  <script src="https://alcdn.msauth.net/browser/2.37.0/js/msal-browser.min.js" crossorigin="anonymous"></script>
 </head>
 <body>
  <div class="page" x-data="aocApp()" x-init="initApp()">
+    <nav class="topbar">
+      <div class="topbar__brand">
+        <span class="topbar__logo">🔍</span>
+        <span class="topbar__name">AOC</span>
+        <span class="version-badge" x-text="appVersion"></span>
+      </div>
+      <div class="topbar__links">
+        <a :href="repoUrl" target="_blank" rel="noopener">Repository</a>
+        <a :href="docsUrl" target="_blank" rel="noopener">Docs</a>
+      </div>
+      <div class="topbar__meta">
+        <template x-if="account">
+          <div class="user-chip">
+            <div class="user-avatar" x-text="(account.name || account.username || '?').charAt(0).toUpperCase()"></div>
+            <div class="user-details">
+              <span class="user-name" x-text="account.name || account.username || ''"></span>
+              <span class="user-email" x-text="account.username || ''"></span>
+            </div>
+          </div>
+        </template>
+        <template x-if="!account && authConfig?.auth_enabled">
+          <span class="login-hint">Not signed in</span>
+        </template>
+      </div>
+      <div class="topbar__actions">
+        <button id="fetchBtn" class="ghost btn--compact" aria-label="Fetch latest audit logs" @click="fetchLogs()">Fetch</button>
+        <button id="refreshBtn" class="ghost btn--compact" aria-label="Refresh events" @click="loadEvents(currentCursor)">Refresh</button>
+        <button id="authBtn" class="ghost btn--compact" aria-label="Login" x-text="authBtnText" @click="toggleAuth()"></button>
+      </div>
+    </nav>
+
    <header class="hero">
      <div>
        <p class="eyebrow">Admin Operations Center</p>
-        <h1>Directory Audit Explorer</h1>
-        <p class="lede">Filter Microsoft Entra audit events by user, app, time, action, and action type.</p>
-      </div>
-      <div class="cta">
-        <button id="authBtn" class="ghost" aria-label="Login" x-text="authBtnText" @click="toggleAuth()"></button>
-        <button id="fetchBtn" aria-label="Fetch latest audit logs" @click="fetchLogs()">Fetch new</button>
-        <button id="refreshBtn" aria-label="Refresh events" @click="loadEvents(currentCursor)">Refresh</button>
+        <h1>Audit Log Explorer</h1>
+        <p class="lede">Search and review Microsoft audit events from Entra, Intune, Exchange, SharePoint, and Teams.</p>
      </div>
    </header>

@@ -38,46 +64,6 @@
      </div>
    </section>

-    <section class="panel">
-      <h3>Ask a question</h3>
-      <form class="ask-form" @submit.prevent="askQuestion()">
-        <div class="ask-row">
-          <input
-            type="text"
-            placeholder="What happened to device ABC123 in the last 3 days?"
-            x-model="askQuestionText"
-            class="ask-input"
-          />
-          <button type="submit" :disabled="askLoading" x-text="askLoading ? 'Thinking…' : 'Ask'">Ask</button>
-        </div>
-      </form>
-      <template x-if="askAnswer">
-        <div class="ask-result">
-          <div x-show="askLlmError" class="ask-error" x-text="askLlmError"></div>
-          <div class="ask-answer" x-html="askAnswerHtml"></div>
-          <template x-if="askEvents.length">
-            <div class="ask-events">
-              <h4>Referenced events</h4>
-              <template x-for="(evt, idx) in askEvents" :key="evt.id || idx">
-                <article class="event event--compact">
-                  <div class="event__meta">
-                    <span class="pill" x-text="evt.display_category || evt.service || '—'"></span>
-                    <span class="pill" :class="['success','succeeded','ok','passed'].includes((evt.result || '').toLowerCase()) ? 'pill--ok' : 'pill--warn'" x-text="evt.result || '—'"></span>
-                  </div>
-                  <h3 x-text="evt.operation || '—'"></h3>
-                  <p class="event__detail" x-show="evt.display_summary"><strong>Summary:</strong> <span x-text="evt.display_summary"></span></p>
-                  <p class="event__detail"><strong>Actor:</strong> <span x-text="evt.actor_display || '—'"></span></p>
-                  <p class="event__detail"><strong>Target:</strong> <span x-text="Array.isArray(evt.target_displays) ? evt.target_displays.join(', ') : '—'"></span></p>
-                  <p class="event__detail"><strong>When:</strong> <span x-text="evt.timestamp ? new Date(evt.timestamp).toLocaleString() : '—'"></span></p>
-                </article>
-              </template>
-            </div>
-          </template>
-          <button type="button" class="ghost" @click="clearAsk()">Clear</button>
-        </div>
-      </template>
-    </section>
-
    <section class="panel">
      <form id="filters" class="filters" @submit.prevent="resetPagination(); loadEvents()">
        <div class="filter-row">
@@ -152,14 +138,69 @@
          <div class="actions">
            <button type="submit">Apply filters</button>
            <button type="button" id="clearBtn" class="ghost" @click="clearFilters()">Clear</button>
+            <button type="button" class="ghost" @click="saveCurrentFilters()">Save filters</button>
            <button type="button" class="ghost" @click="bulkTagMatching()">Bulk tag matching</button>
            <button type="button" class="ghost" @click="exportJSON()">Export JSON</button>
            <button type="button" class="ghost" @click="exportCSV()">Export CSV</button>
          </div>
        </div>
+        <div class="filter-row" x-show="savedSearches.length">
+          <div class="saved-searches">
+            <span>Saved:</span>
+            <template x-for="ss in savedSearches" :key="ss.id">
+              <span class="pill pill--tag" style="cursor:pointer;" @click="applySavedSearch(ss)">
+                <span x-text="ss.name"></span>
+                <button type="button" class="link" style="margin-left:4px;" @click.stop="deleteSavedSearch(ss.id)">×</button>
+              </span>
+            </template>
+          </div>
+        </div>
      </form>
    </section>

+    <section class="panel" x-show="aiFeaturesEnabled">
+      <h3>Ask a question</h3>
+      <form class="ask-form" @submit.prevent="askQuestion()">
+        <div class="ask-row">
+          <input
+            type="text"
+            placeholder="What happened to device ABC123 in the last 3 days?"
+            x-model="askQuestionText"
+            class="ask-input"
+          />
+          <button type="submit" :disabled="askLoading" x-text="askLoading ? 'Thinking…' : 'Ask'">Ask</button>
+        </div>
+        <div x-show="hasActiveFilters()" class="ask-filter-hint">
+          <small>Respecting active filters: <span x-text="activeFilterSummary()"></span></small>
+        </div>
+      </form>
+      <template x-if="askAnswer">
+        <div class="ask-result">
+          <div x-show="askLlmError" class="ask-error" x-text="askLlmError"></div>
+          <div class="ask-answer" x-html="askAnswerHtml"></div>
+          <template x-if="askEvents.length">
+            <div class="ask-events">
+              <h4>Referenced events</h4>
+              <template x-for="(evt, idx) in askEvents" :key="evt.id || idx">
+                <article class="event event--compact">
+                  <div class="event__meta">
+                    <span class="pill pill--clickable" x-text="evt.display_category || evt.service || '—'" @click="filterByService(evt.service || evt.display_category)" title="Filter by this service"></span>
+                    <span class="pill pill--clickable" :class="['success','succeeded','ok','passed','true'].includes((evt.result || '').toLowerCase()) ? 'pill--ok' : 'pill--warn'" x-text="evt.result || '—'" @click="filterByResult(evt.result)" title="Filter by this result"></span>
+                  </div>
+                  <h3 x-text="evt.operation || '—'"></h3>
+                  <p class="event__detail" x-show="evt.display_summary"><strong>Summary:</strong> <span x-text="evt.display_summary"></span></p>
+                  <p class="event__detail"><strong>Actor:</strong> <span x-text="evt.actor_display || '—'"></span></p>
+                  <p class="event__detail"><strong>Target:</strong> <span x-text="Array.isArray(evt.target_displays) ? evt.target_displays.join(', ') : '—'"></span></p>
+                  <p class="event__detail"><strong>When:</strong> <span x-text="evt.timestamp ? new Date(evt.timestamp).toLocaleString() : '—'"></span></p>
+                </article>
+              </template>
+            </div>
+          </template>
+          <button type="button" class="ghost" @click="clearAsk()">Clear</button>
+        </div>
+      </template>
+    </section>
+
    <section class="panel">
      <div class="panel-header">
        <h2>Events</h2>
@@ -170,8 +211,8 @@
        <template x-for="(evt, idx) in events" :key="evt._id || evt.id || idx">
          <article class="event">
            <div class="event__meta">
-              <span class="pill" x-text="evt.display_category || evt.service || '—'"></span>
-              <span class="pill" :class="['success','succeeded','ok','passed'].includes((evt.result || '').toLowerCase()) ? 'pill--ok' : 'pill--warn'" x-text="evt.result || '—'"></span>
+              <span class="pill pill--clickable" x-text="evt.display_category || evt.service || '—'" @click="filterByService(evt.service || evt.display_category)" title="Filter by this service"></span>
+              <span class="pill pill--clickable" :class="['success','succeeded','ok','passed','true'].includes((evt.result || '').toLowerCase()) ? 'pill--ok' : 'pill--warn'" x-text="evt.result || '—'" @click="filterByResult(evt.result)" title="Filter by this result"></span>
            </div>
            <h3 x-text="evt.operation || '—'"></h3>
            <p class="event__detail" x-show="evt.display_summary"><strong>Summary:</strong> <span x-text="evt.display_summary"></span></p>
@@ -211,11 +252,34 @@
      <div class="modal__content">
        <div class="modal__header">
          <h3 id="modalTitle">Raw Event</h3>
-          <button type="button" id="closeModal" class="ghost" @click="modalOpen = false">Close</button>
+          <div class="modal__actions">
+            <button type="button" class="ghost" @click="copyRawEvent()">Copy</button>
+            <button type="button" class="ghost" x-show="aiFeaturesEnabled" :disabled="modalExplainLoading" @click="explainEvent()" x-text="modalExplainLoading ? 'Explaining…' : 'Explain'">Explain</button>
+            <button type="button" id="closeModal" class="ghost" @click="modalOpen = false">Close</button>
+          </div>
+        </div>
+        <div x-show="modalExplanation || modalExplainError" class="modal__explanation">
+          <div x-show="modalExplainError" class="ask-error" x-text="modalExplainError"></div>
+          <div x-show="modalExplanation" class="ask-answer" x-html="_mdToHtml(modalExplanation)"></div>
        </div>
        <pre id="modalBody" x-text="modalBody"></pre>
      </div>
    </div>
+
+    <footer class="footer">
+      <div class="footer__left">
+        <span class="footer__brand">Admin Operations Center</span>
+        <span class="footer__version" x-text="'v' + appVersion"></span>
+      </div>
+      <div class="footer__center">
+        <a :href="repoUrl + '/issues/new'" target="_blank" rel="noopener">🐛 Report an issue</a>
+        <a :href="repoUrl" target="_blank" rel="noopener">💻 Source code</a>
+        <a :href="docsUrl" target="_blank" rel="noopener">📖 Documentation</a>
+      </div>
+      <div class="footer__right">
+        <span>Built with ❤️ by CQRE.NET</span>
+      </div>
+    </footer>
  </div>

  <script>
@@ -230,6 +294,10 @@
        currentCursor: null,
        modalOpen: false,
        modalBody: '',
+        modalEventId: '',
+        modalExplanation: '',
+        modalExplainLoading: false,
+        modalExplainError: '',
        authBtnText: 'Login',
        authConfig: null,
        msalInstance: null,
@@ -237,9 +305,14 @@
        accessToken: null,
        authScopes: [],
        filters: {
-          actor: '', selectedServices: [], search: '', operation: '', result: '', start: '', end: '', limit: 100, includeTags: '', excludeTags: '',
+          actor: '', selectedServices: [], search: '', operation: '', result: '', start: '', end: '', limit: 25, includeTags: '', excludeTags: '',
        },
        options: { actors: [], services: [], operations: [], results: [] },
+        savedSearches: [],
+        appVersion: '',
+        repoUrl: 'https://git.cqre.net/cqrenet/aoc',
+        docsUrl: 'https://git.cqre.net/cqrenet/aoc/src/branch/main/README.md',
+        aiFeaturesEnabled: true,
        askQuestionText: '',
        askLoading: false,
        askAnswer: '',
@@ -249,14 +322,45 @@
        askLlmError: '',

        async initApp() {
+          await this.loadVersion();
          await this.initAuth();
+          this.loadSavedFilters();
          if (!this.authConfig?.auth_enabled || this.accessToken) {
            await this.loadFilterOptions();
+            await this.loadSavedSearches();
            await this.loadSourceHealth();
            await this.loadEvents();
          }
        },

+        loadSavedFilters() {
+          try {
+            const saved = localStorage.getItem('aoc_filters');
+            if (!saved) return;
+            const parsed = JSON.parse(saved);
+            const fields = ['actor', 'selectedServices', 'search', 'operation', 'result', 'start', 'end', 'limit', 'includeTags', 'excludeTags'];
+            fields.forEach((f) => {
+              if (parsed[f] !== undefined) this.filters[f] = parsed[f];
+            });
+          } catch {}
+        },
+
+        saveFilters() {
+          try {
+            localStorage.setItem('aoc_filters', JSON.stringify(this.filters));
+          } catch {}
+        },
+
+        async loadVersion() {
+          try {
+            const res = await fetch('/api/version');
+            if (res.ok) {
+              const body = await res.json();
+              this.appVersion = body.version || '';
+            }
+          } catch {}
+        },
+
        authHeader() {
          return this.accessToken ? { Authorization: `Bearer ${this.accessToken}` } : {};
        },
@@ -287,6 +391,21 @@
            this.authConfig = { auth_enabled: false };
          }

+          try {
+            const featRes = await fetch('/api/config/features');
+            if (featRes.ok) {
+              const featBody = await featRes.json();
+              this.aiFeaturesEnabled = featBody.ai_features_enabled !== false;
+              if (featBody.default_page_size) {
+                this.filters.limit = featBody.default_page_size;
+              }
+            } else {
+              this.aiFeaturesEnabled = true;
+            }
+          } catch {
+            this.aiFeaturesEnabled = true;
+          }
+
          if (!this.authConfig?.auth_enabled) {
            this.authBtnText = '';
            return;
@@ -409,6 +528,7 @@
            this.nextCursor = body.next_cursor || null;
            this.countText = body.total >= 0 ? `${body.total} event${body.total === 1 ? '' : 's'}` : '';
            this.statusText = this.events.length ? '' : 'No events found for these filters.';
+            this.saveFilters();
          } catch (err) {
            this.statusText = err.message || 'Failed to load events.';
          }
@@ -444,8 +564,19 @@
            this.options.services = (opts.services || []).slice(0, 200);
            this.options.operations = (opts.operations || []).slice(0, 200);
            this.options.results = (opts.results || []).slice(0, 200);
-            if (!this.filters.selectedServices.length && this.options.services.length) {
-              this.filters.selectedServices = [...this.options.services];
+
+            const saved = localStorage.getItem('aoc_filters');
+            if (!saved && this.options.services.length) {
+              // Default: exclude noisy high-volume services
+              const noisy = ['Exchange', 'SharePoint', 'Teams'];
+              this.filters.selectedServices = this.options.services.filter((s) => !noisy.includes(s));
+            } else if (saved) {
+              try {
+                const parsed = JSON.parse(saved);
+                if (parsed.selectedServices) {
+                  this.filters.selectedServices = parsed.selectedServices.filter((s) => this.options.services.includes(s));
+                }
+              } catch {}
            }
          } catch {}
        },
@@ -458,6 +589,59 @@
          } catch {}
        },

+        async loadSavedSearches() {
+          try {
+            const res = await fetch('/api/saved-searches', { headers: this.authHeader() });
+            if (!res.ok) return;
+            this.savedSearches = await res.json();
+          } catch {}
+        },
+
+        async saveCurrentFilters() {
+          const name = prompt('Name this saved filter:');
+          if (!name || !name.trim()) return;
+          try {
+            const res = await fetch('/api/saved-searches', {
+              method: 'POST',
+              headers: { 'Content-Type': 'application/json', ...this.authHeader() },
+              body: JSON.stringify({ name: name.trim(), filters: { ...this.filters } }),
+            });
+            if (!res.ok) throw new Error(await res.text());
+            const created = await res.json();
+            this.savedSearches.unshift(created);
+            this.statusText = 'Filters saved.';
+            setTimeout(() => { if (this.statusText === 'Filters saved.') this.statusText = ''; }, 2000);
+          } catch (err) {
+            this.statusText = err.message || 'Failed to save filters.';
+          }
+        },
+
+        applySavedSearch(ss) {
+          if (!ss || !ss.filters) return;
+          const fields = ['actor', 'selectedServices', 'search', 'operation', 'result', 'start', 'end', 'limit', 'includeTags', 'excludeTags'];
+          fields.forEach((f) => {
+            if (ss.filters[f] !== undefined) this.filters[f] = ss.filters[f];
+          });
+          // Validate selectedServices against current options
+          this.filters.selectedServices = this.filters.selectedServices.filter((s) => this.options.services.includes(s));
+          this.resetPagination();
+          this.loadEvents();
+        },
+
+        async deleteSavedSearch(id) {
+          if (!confirm('Delete this saved search?')) return;
+          try {
+            const res = await fetch(`/api/saved-searches/${id}`, {
+              method: 'DELETE',
+              headers: this.authHeader(),
+            });
+            if (!res.ok) throw new Error(await res.text());
+            this.savedSearches = this.savedSearches.filter((s) => s.id !== id);
+          } catch (err) {
+            this.statusText = err.message || 'Failed to delete saved search.';
+          }
+        },
+
        resetPagination() {
          this.cursorStack = [];
          this.nextCursor = null;
@@ -479,7 +663,29 @@
        },

        clearFilters() {
-          this.filters = { actor: '', selectedServices: [...this.options.services], search: '', operation: '', result: '', start: '', end: '', limit: 100, includeTags: '', excludeTags: '' };
+          const noisy = ['Exchange', 'SharePoint', 'Teams'];
+          this.filters = { actor: '', selectedServices: this.options.services.filter((s) => !noisy.includes(s)), search: '', operation: '', result: '', start: '', end: '', limit: 25, includeTags: '', excludeTags: '' };
+          this.saveFilters();
+          this.resetPagination();
+          this.loadEvents();
+        },
+
+        filterByService(service) {
+          if (!service) return;
+          if (!this.filters.selectedServices.includes(service)) {
+            this.filters.selectedServices = [service];
+          } else {
+            this.filters.selectedServices = this.filters.selectedServices.filter((s) => s !== service);
+          }
+          this.saveFilters();
+          this.resetPagination();
+          this.loadEvents();
+        },
+
+        filterByResult(result) {
+          if (!result) return;
+          this.filters.result = this.filters.result === result ? '' : result;
+          this.saveFilters();
          this.resetPagination();
          this.loadEvents();
        },
@@ -491,11 +697,29 @@
          this.askAnswer = '';
          this.askAnswerHtml = '';
          this.askEvents = [];
+          this.askLlmError = '';
+
+          const payload = { question: q };
+          if (this.filters.selectedServices && this.filters.selectedServices.length) {
+            payload.services = this.filters.selectedServices;
+          }
+          if (this.filters.actor) payload.actor = this.filters.actor;
+          if (this.filters.operation) payload.operation = this.filters.operation;
+          if (this.filters.result) payload.result = this.filters.result;
+          if (this.filters.start) payload.start = new Date(this.filters.start).toISOString();
+          if (this.filters.end) payload.end = new Date(this.filters.end).toISOString();
+          if (this.filters.includeTags) {
+            payload.include_tags = this.filters.includeTags.split(/[,;]+/).map(t => t.trim()).filter(Boolean);
+          }
+          if (this.filters.excludeTags) {
+            payload.exclude_tags = this.filters.excludeTags.split(/[,;]+/).map(t => t.trim()).filter(Boolean);
+          }
+
          try {
            const res = await fetch('/api/ask', {
              method: 'POST',
              headers: { 'Content-Type': 'application/json', ...this.authHeader() },
-              body: JSON.stringify({ question: q }),
+              body: JSON.stringify(payload),
            });
            if (!res.ok) throw new Error(await res.text());
            const body = await res.json();
@@ -532,6 +756,27 @@
            .replace(/\n/g, '<br>');
        },

+        hasActiveFilters() {
+          return this.filters.actor || this.filters.operation || this.filters.result ||
+            this.filters.start || this.filters.end || this.filters.includeTags ||
+            this.filters.excludeTags ||
+            (this.filters.selectedServices && this.filters.selectedServices.length &&
+             this.filters.selectedServices.length < this.options.services.length);
+        },
+
+        activeFilterSummary() {
+          const parts = [];
+          if (this.filters.actor) parts.push('actor');
+          if (this.filters.operation) parts.push('action');
+          if (this.filters.result) parts.push('result');
+          if (this.filters.start || this.filters.end) parts.push('time');
+          if (this.filters.includeTags || this.filters.excludeTags) parts.push('tags');
+          const svcCount = this.filters.selectedServices?.length || 0;
+          const allCount = this.options.services?.length || 0;
+          if (svcCount && svcCount < allCount) parts.push(`${svcCount} service${svcCount === 1 ? '' : 's'}`);
+          return parts.join(', ') || 'none';
+        },
+
        async bulkTagMatching() {
          const tag = prompt('Enter tag to apply to all matching events:');
          if (!tag || !tag.trim()) return;
@@ -605,9 +850,44 @@
          } catch (err) {
            this.modalBody = `Error serializing event:\n${err.message}\n\nEvent ID: ${e.id || 'N/A'}`;
          }
+          this.modalEventId = e.id || '';
+          this.modalExplanation = '';
+          this.modalExplainError = '';
          this.modalOpen = true;
        },

+        async copyRawEvent() {
+          if (!this.modalBody) return;
+          try {
+            await navigator.clipboard.writeText(this.modalBody);
+            this.statusText = 'Raw event copied to clipboard.';
+            setTimeout(() => { if (this.statusText === 'Raw event copied to clipboard.') this.statusText = ''; }, 2000);
+          } catch (err) {
+            this.statusText = 'Failed to copy to clipboard.';
+          }
+        },
+
+        async explainEvent() {
+          if (!this.modalEventId) return;
+          this.modalExplainLoading = true;
+          this.modalExplanation = '';
+          this.modalExplainError = '';
+          try {
+            const res = await fetch(`/api/events/${this.modalEventId}/explain`, {
+              method: 'POST',
+              headers: { 'Content-Type': 'application/json', ...this.authHeader() },
+            });
+            if (!res.ok) throw new Error(await res.text());
+            const body = await res.json();
+            this.modalExplanation = body.explanation;
+            this.modalExplainError = body.llm_error || '';
+          } catch (err) {
+            this.modalExplainError = err.message || 'Failed to explain event.';
+          } finally {
+            this.modalExplainLoading = false;
+          }
+        },
+
        async addTag(e, tag) {
          if (!tag.trim()) return;
          const tags = [...(e.tags || []), tag.trim()];
--- a/backend/frontend/style.css
+++ b/backend/frontend/style.css
@@ -28,7 +28,115 @@ body {
 .page {
  max-width: 1100px;
  margin: 0 auto;
-  padding: 32px 20px 60px;
+  padding: 0 20px 40px;
+  display: flex;
+  flex-direction: column;
+  min-height: 100vh;
+}
+
+.topbar {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+  padding: 12px 0;
+  margin-bottom: 8px;
+  border-bottom: 1px solid var(--border);
+  flex-wrap: wrap;
+}
+
+.topbar__brand {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-weight: 700;
+  font-size: 16px;
+}
+
+.topbar__logo {
+  font-size: 20px;
+}
+
+.topbar__links {
+  display: flex;
+  gap: 16px;
+  margin-right: auto;
+}
+
+.topbar__links a {
+  color: var(--muted);
+  font-size: 13px;
+  text-decoration: none;
+  font-weight: 500;
+  transition: color 0.15s ease;
+}
+
+.topbar__links a:hover {
+  color: var(--accent-strong);
+}
+
+.topbar__meta {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.user-chip {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  background: rgba(255, 255, 255, 0.04);
+  border: 1px solid var(--border);
+  border-radius: 999px;
+  padding: 4px 12px 4px 4px;
+}
+
+.user-avatar {
+  width: 26px;
+  height: 26px;
+  border-radius: 50%;
+  background: linear-gradient(135deg, var(--accent), var(--accent-strong));
+  color: #0b1220;
+  font-size: 12px;
+  font-weight: 700;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex-shrink: 0;
+}
+
+.user-details {
+  display: flex;
+  flex-direction: column;
+  line-height: 1.2;
+}
+
+.user-name {
+  font-size: 12px;
+  font-weight: 600;
+  color: var(--text);
+}
+
+.user-email {
+  font-size: 11px;
+  color: var(--muted);
+}
+
+.login-hint {
+  font-size: 12px;
+  color: var(--muted);
+  font-style: italic;
+}
+
+.topbar__actions {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+}
+
+.btn--compact {
+  padding: 8px 14px;
+  font-size: 13px;
+  border-radius: 8px;
 }

 .hero {
@@ -37,6 +145,7 @@ body {
  justify-content: space-between;
  gap: 16px;
  margin-bottom: 20px;
+  padding-top: 16px;
 }

 .eyebrow {
@@ -246,6 +355,27 @@ input {
  border-color: rgba(239, 68, 68, 0.5);
 }

+.pill--clickable {
+  cursor: pointer;
+  transition: transform 0.1s ease, box-shadow 0.15s ease, background 0.15s ease;
+}
+
+.pill--clickable:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 2px 8px rgba(125, 211, 252, 0.2);
+  background: rgba(125, 211, 252, 0.2);
+}
+
+.pill--clickable.pill--ok:hover {
+  box-shadow: 0 2px 8px rgba(34, 197, 94, 0.2);
+  background: rgba(34, 197, 94, 0.25);
+}
+
+.pill--clickable.pill--warn:hover {
+  box-shadow: 0 2px 8px rgba(249, 115, 22, 0.2);
+  background: rgba(249, 115, 22, 0.25);
+}
+
 .event h3 {
  margin: 0 0 6px;
  font-size: 17px;
@@ -364,6 +494,30 @@ input {
  margin-bottom: 10px;
 }

+.modal__actions {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+}
+
+.saved-searches {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  align-items: center;
+  font-size: 13px;
+}
+
+.modal__explanation {
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--border);
+  border-radius: 10px;
+  padding: 12px;
+  margin-bottom: 10px;
+  font-size: 14px;
+  line-height: 1.6;
+}
+
 .modal pre {
  background: rgba(255, 255, 255, 0.02);
  color: var(--text);
@@ -428,6 +582,25 @@ input {
  margin-bottom: 10px;
 }

+.ask-filter-hint {
+  margin-top: 6px;
+  color: var(--muted);
+}
+
+.version-badge {
+  display: inline-block;
+  margin-left: 8px;
+  padding: 2px 8px;
+  border-radius: 999px;
+  background: rgba(125, 211, 252, 0.15);
+  border: 1px solid rgba(125, 211, 252, 0.3);
+  color: var(--accent-strong);
+  font-size: 11px;
+  font-weight: 600;
+  letter-spacing: 0.05em;
+  vertical-align: middle;
+}
+
 .ask-events {
  margin-bottom: 14px;
 }
@@ -465,7 +638,70 @@ input {
  gap: 4px;
 }

+.footer {
+  margin-top: auto;
+  padding: 20px 0;
+  border-top: 1px solid var(--border);
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 16px;
+  flex-wrap: wrap;
+  font-size: 13px;
+  color: var(--muted);
+}
+
+.footer__left {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.footer__brand {
+  font-weight: 600;
+  color: var(--text);
+}
+
+.footer__version {
+  font-size: 11px;
+  padding: 2px 8px;
+  border-radius: 999px;
+  background: rgba(125, 211, 252, 0.1);
+  border: 1px solid rgba(125, 211, 252, 0.2);
+  color: var(--accent-strong);
+}
+
+.footer__center {
+  display: flex;
+  gap: 16px;
+  align-items: center;
+}
+
+.footer__center a {
+  color: var(--muted);
+  text-decoration: none;
+  transition: color 0.15s ease;
+}
+
+.footer__center a:hover {
+  color: var(--accent-strong);
+}
+
+.footer__right {
+  font-size: 12px;
+}
+
@media (max-width: 640px) {
+  .topbar {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 10px;
+  }
+
+  .topbar__links {
+    margin-right: 0;
+  }
+
  .hero {
    flex-direction: column;
  }
@@ -479,4 +715,10 @@ input {
    flex-direction: column;
    align-items: stretch;
  }
+
+  .footer {
+    flex-direction: column;
+    text-align: center;
+    gap: 10px;
+  }
 }
--- a/backend/jobs.py
+++ b/backend/jobs.py
@@ -0,0 +1,117 @@
+"""arq job functions for async LLM processing."""
+
+import hashlib
+import json
+
+import structlog
+from arq.connections import RedisSettings
+from config import REDIS_URL
+
+logger = structlog.get_logger("aoc.jobs")
+
+# ---------------------------------------------------------------------------
+# Cache helpers
+# ---------------------------------------------------------------------------
+
+CACHE_TTL_ASK = 3600  # 1 hour
+CACHE_TTL_EXPLAIN = 86400  # 24 hours
+
+
+def _ask_cache_key(question: str, filters: dict, events: list) -> str:
+    payload = json.dumps({"q": question, "f": filters, "e": [e.get("id") for e in events]}, sort_keys=True)
+    return f"aoc:cache:ask:{hashlib.md5(payload.encode()).hexdigest()}"
+
+
+def _explain_cache_key(event_id: str) -> str:
+    return f"aoc:cache:explain:{event_id}"
+
+
+async def get_cached_ask(redis, question: str, filters: dict, events: list) -> dict | None:
+    key = _ask_cache_key(question, filters, events)
+    raw = await redis.get(key)
+    if raw:
+        return json.loads(raw)
+    return None
+
+
+async def set_cached_ask(redis, question: str, filters: dict, events: list, result: dict):
+    key = _ask_cache_key(question, filters, events)
+    await redis.setex(key, CACHE_TTL_ASK, json.dumps(result, default=str))
+
+
+async def get_cached_explain(redis, event_id: str) -> dict | None:
+    key = _explain_cache_key(event_id)
+    raw = await redis.get(key)
+    if raw:
+        return json.loads(raw)
+    return None
+
+
+async def set_cached_explain(redis, event_id: str, result: dict):
+    key = _explain_cache_key(event_id)
+    await redis.setex(key, CACHE_TTL_EXPLAIN, json.dumps(result, default=str))
+
+
+# ---------------------------------------------------------------------------
+# arq job functions
+# ---------------------------------------------------------------------------
+
+
+async def process_ask_question(
+    ctx, question: str, filters: dict, events: list, total: int, excluded_services: list | None
+):
+    """Background job: call LLM for /api/ask and cache result."""
+    from routes.ask import _call_llm
+
+    redis = ctx["redis"]
+    try:
+        answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
+        result = {"status": "completed", "answer": answer, "llm_used": True, "llm_error": None}
+    except Exception as exc:
+        logger.warning("Async ask LLM failed", error=str(exc))
+        result = {"status": "failed", "answer": "", "llm_used": False, "llm_error": str(exc)}
+
+    await set_cached_ask(redis, question, filters, events, result)
+    return result
+
+
+async def process_explain_event(ctx, event_id: str, event: dict, related: list):
+    """Background job: call LLM for /api/events/{id}/explain and cache result."""
+    from routes.ask import _explain_event
+
+    redis = ctx["redis"]
+    try:
+        explanation = await _explain_event(event, related)
+        result = {"status": "completed", "explanation": explanation, "llm_used": True, "llm_error": None}
+    except Exception as exc:
+        logger.warning("Async explain LLM failed", error=str(exc))
+        result = {"status": "failed", "explanation": "", "llm_used": False, "llm_error": str(exc)}
+
+    await set_cached_explain(redis, event_id, result)
+    return result
+
+
+# ---------------------------------------------------------------------------
+# arq worker configuration
+# ---------------------------------------------------------------------------
+
+
+async def startup(ctx):
+    from redis.asyncio import Redis
+
+    ctx["redis"] = Redis.from_url(REDIS_URL, decode_responses=True)
+
+
+async def shutdown(ctx):
+    await ctx["redis"].close()
+
+
+class WorkerSettings:
+    functions = [process_ask_question, process_explain_event]
+    redis_settings = RedisSettings.from_dsn(REDIS_URL)
+    on_startup = startup
+    on_shutdown = shutdown
+    max_jobs = 10
+    job_timeout = 120
+    keep_result = 3600
+    keep_result_forever = False
--- a/backend/main.py
+++ b/backend/main.py
@@ -6,7 +6,7 @@ from pathlib import Path

 import structlog
 from audit_trail import log_action
-from config import CORS_ORIGINS, ENABLE_PERIODIC_FETCH, FETCH_INTERVAL_MINUTES
+from config import AI_FEATURES_ENABLED, CORS_ORIGINS, ENABLE_PERIODIC_FETCH, FETCH_INTERVAL_MINUTES
 from database import setup_indexes
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
@@ -14,13 +14,14 @@ from fastapi.responses import Response
 from fastapi.staticfiles import StaticFiles
 from metrics import observe_request, prometheus_metrics
 from middleware import CorrelationIdMiddleware
-from routes.ask import router as ask_router
 from routes.config import router as config_router
 from routes.events import router as events_router
 from routes.fetch import router as fetch_router
 from routes.fetch import run_fetch
 from routes.health import router as health_router
+from routes.jobs import router as jobs_router
 from routes.rules import router as rules_router
+from routes.saved_searches import router as saved_searches_router
 from routes.webhooks import router as webhooks_router


@@ -113,8 +114,16 @@ app.include_router(events_router, prefix="/api")
 app.include_router(config_router, prefix="/api")
 app.include_router(webhooks_router, prefix="/api")
 app.include_router(health_router, prefix="/api")
-app.include_router(ask_router, prefix="/api")
+if AI_FEATURES_ENABLED:
+    from routes.ask import router as ask_router
+
+    app.include_router(ask_router, prefix="/api")
+    from routes.mcp import mcp_asgi
+
+    app.mount("/mcp", mcp_asgi)
+app.include_router(saved_searches_router, prefix="/api")
 app.include_router(rules_router, prefix="/api")
+app.include_router(jobs_router, prefix="/api")


@app.get("/health")
@@ -134,6 +143,13 @@ async def metrics():
    return Response(content=prometheus_metrics(), media_type="text/plain")


+@app.get("/api/version")
+async def version():
+    import os
+
+    return {"version": os.environ.get("VERSION", "unknown")}
+
+
 frontend_dir = Path(__file__).parent / "frontend"
 app.mount("/", StaticFiles(directory=frontend_dir, html=True), name="frontend")

@@ -162,3 +178,6 @@ async def stop_periodic_fetch():
        task.cancel()
        with suppress(Exception):
            await task
+    from redis_client import close_redis_connections
+
+    await close_redis_connections()
--- a/backend/mcp_common.py
+++ b/backend/mcp_common.py
@@ -0,0 +1,187 @@
+"""Shared MCP tool handlers used by both stdio and SSE transports."""
+
+import json
+from datetime import UTC, datetime, timedelta
+
+from database import events_collection
+from mcp.types import TextContent
+
+
+async def handle_search_events(arguments: dict) -> list[TextContent]:
+    days = arguments.get("days", 7)
+    limit = min(arguments.get("limit", 20), 100)
+    since = (datetime.now(UTC) - timedelta(days=days)).isoformat().replace("+00:00", "Z")
+
+    filters = [{"timestamp": {"$gte": since}}]
+
+    services = arguments.get("services")
+    if services:
+        filters.append({"service": {"$in": services}})
+
+    operation = arguments.get("operation")
+    if operation:
+        filters.append({"operation": {"$regex": operation, "$options": "i"}})
+
+    result = arguments.get("result")
+    if result:
+        filters.append({"result": {"$regex": result, "$options": "i"}})
+
+    entity = arguments.get("entity")
+    if entity:
+        entity_safe = entity.replace(".", "\\.").replace("(", "\\(").replace(")", "\\)")
+        filters.append(
+            {
+                "$or": [
+                    {"target_displays": {"$elemMatch": {"$regex": entity_safe, "$options": "i"}}},
+                    {"actor_display": {"$regex": entity_safe, "$options": "i"}},
+                    {"actor_upn": {"$regex": entity_safe, "$options": "i"}},
+                    {"raw_text": {"$regex": entity_safe, "$options": "i"}},
+                ]
+            }
+        )
+
+    query = {"$and": filters}
+    cursor = events_collection.find(query).sort("timestamp", -1).limit(limit)
+    events = list(cursor)
+
+    if not events:
+        return [TextContent(type="text", text="No matching events found.")]
+
+    lines = [f"Found {len(events)} event(s):\n"]
+    for e in events:
+        ts = e.get("timestamp", "?")[:16].replace("T", " ")
+        svc = e.get("service", "?")
+        op = e.get("operation", "?")
+        actor = e.get("actor_display", "?")
+        result_str = e.get("result", "?")
+        lines.append(f"{ts} | {svc} | {op} | {actor} | {result_str}")
+
+    return [TextContent(type="text", text="\n".join(lines))]
+
+
+async def handle_get_event(arguments: dict) -> list[TextContent]:
+    event_id = arguments["event_id"]
+    event = events_collection.find_one({"id": event_id})
+    if not event:
+        return [TextContent(type="text", text=f"Event {event_id} not found.")]
+    event.pop("_id", None)
+    return [TextContent(type="text", text=json.dumps(event, indent=2, default=str))]
+
+
+async def handle_get_summary(arguments: dict) -> list[TextContent]:
+    days = arguments.get("days", 7)
+    since = (datetime.now(UTC) - timedelta(days=days)).isoformat().replace("+00:00", "Z")
+    query = {"timestamp": {"$gte": since}}
+
+    total = events_collection.count_documents(query)
+    if total == 0:
+        return [TextContent(type="text", text="No events in the specified period.")]
+
+    svc_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$service", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+    op_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$operation", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+    result_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$result", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+    ]
+    actor_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$actor_display", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+
+    svc_counts = list(events_collection.aggregate(svc_pipeline))
+    op_counts = list(events_collection.aggregate(op_pipeline))
+    result_counts = list(events_collection.aggregate(result_pipeline))
+    actor_counts = list(events_collection.aggregate(actor_pipeline))
+
+    lines = [f"Summary for the last {days} days ({total} total events)\n"]
+
+    lines.append("By service:")
+    for row in svc_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nBy action:")
+    for row in op_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nBy result:")
+    for row in result_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nTop actors:")
+    for row in actor_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    return [TextContent(type="text", text="\n".join(lines))]
+
+
+async def handle_ask(arguments: dict) -> list[TextContent]:
+    """For now, returns recent events + guidance. In the future this could call the LLM backend."""
+    question = arguments["question"]
+    days = arguments.get("days", 7)
+
+    result = await handle_search_events({"entity": "", "days": days, "limit": 50})
+    base_text = result[0].text if result else ""
+
+    text = (
+        f"You asked: '{question}'\n\n"
+        f"Here are the most recent events from the last {days} days:\n\n"
+        f"{base_text}\n\n"
+        f"Tip: Use the 'search_events' tool with specific filters "
+        f"to narrow down the dataset before asking follow-up questions."
+    )
+    return [TextContent(type="text", text=text)]
+
+
+# JSON schemas for tool definitions
+SEARCH_EVENTS_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "entity": {"type": "string", "description": "Device name, user UPN, or email to search for"},
+        "services": {
+            "type": "array",
+            "items": {"type": "string"},
+            "description": "Filter by service (e.g. Intune, Directory, Exchange)",
+        },
+        "operation": {"type": "string", "description": "Filter by operation name"},
+        "result": {"type": "string", "description": "Filter by result (success, failure)"},
+        "days": {"type": "integer", "description": "Number of days to look back (default 7)"},
+        "limit": {"type": "integer", "description": "Max events to return (default 20)"},
+    },
+}
+
+GET_EVENT_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "event_id": {"type": "string", "description": "The event ID to retrieve"},
+    },
+    "required": ["event_id"],
+}
+
+GET_SUMMARY_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "days": {"type": "integer", "description": "Number of days to summarise (default 7)"},
+    },
+}
+
+ASK_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "question": {"type": "string", "description": "Natural language question about audit logs"},
+        "days": {"type": "integer", "description": "Number of days to look back (default 7)"},
+    },
+    "required": ["question"],
+}
--- a/backend/mcp_server.py
+++ b/backend/mcp_server.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""
+AOC MCP Server — stdio transport
+
+Standalone MCP server for local use (Claude Desktop, Cursor, etc.).
+For the HTTP/SSE version (production, behind auth), see routes/mcp.py.
+
+Usage:
+    python mcp_server.py
+
+Claude Desktop config (~/.config/claude/claude_desktop_config.json):
+    {
+      "mcpServers": {
+        "aoc": {
+          "command": "python",
+          "args": ["/path/to/aoc/backend/mcp_server.py"],
+          "env": {"MONGO_URI": "mongodb://..."}
+        }
+      }
+    }
+"""
+
+import asyncio
+import os
+import sys
+
+# Ensure backend modules are importable when run standalone
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from mcp.server import Server
+from mcp.server.stdio import stdio_server
+from mcp.types import TextContent, Tool
+from mcp_common import (
+    ASK_SCHEMA,
+    GET_EVENT_SCHEMA,
+    GET_SUMMARY_SCHEMA,
+    SEARCH_EVENTS_SCHEMA,
+    handle_ask,
+    handle_get_event,
+    handle_get_summary,
+    handle_search_events,
+)
+
+app = Server("aoc")
+
+
+@app.list_tools()
+async def list_tools() -> list[Tool]:
+    return [
+        Tool(
+            name="search_events",
+            description="Search audit events by entity, service, operation, or result.",
+            inputSchema=SEARCH_EVENTS_SCHEMA,
+        ),
+        Tool(name="get_event", description="Retrieve a single audit event by its ID.", inputSchema=GET_EVENT_SCHEMA),
+        Tool(
+            name="get_summary",
+            description="Get an aggregated summary of audit activity for the last N days.",
+            inputSchema=GET_SUMMARY_SCHEMA,
+        ),
+        Tool(
+            name="ask",
+            description="Ask a natural language question about audit logs. Returns a narrative answer.",
+            inputSchema=ASK_SCHEMA,
+        ),
+    ]
+
+
+@app.call_tool()
+async def call_tool(name: str, arguments: dict) -> list[TextContent]:
+    if name == "search_events":
+        return await handle_search_events(arguments)
+    if name == "get_event":
+        return await handle_get_event(arguments)
+    if name == "get_summary":
+        return await handle_get_summary(arguments)
+    if name == "ask":
+        return await handle_ask(arguments)
+    raise ValueError(f"Unknown tool: {name}")
+
+
+async def main():
+    async with stdio_server() as (read_stream, write_stream):
+        await app.run(read_stream, write_stream, app.create_initialization_options())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
--- a/backend/models/api.py
+++ b/backend/models/api.py
@@ -74,6 +74,15 @@ class AlertRuleResponse(BaseModel):

 class AskRequest(BaseModel):
    question: str
+    services: list[str] | None = None
+    actor: str | None = None
+    operation: str | None = None
+    result: str | None = None
+    start: str | None = None
+    end: str | None = None
+    include_tags: list[str] | None = None
+    exclude_tags: list[str] | None = None
+    async_mode: bool = False  # enqueue async job instead of waiting


 class AskEventRef(BaseModel):
@@ -93,3 +102,4 @@ class AskResponse(BaseModel):
    query_info: dict
    llm_used: bool
    llm_error: str | None = None
+    job_id: str | None = None
--- a/backend/redis_client.py
+++ b/backend/redis_client.py
@@ -0,0 +1,36 @@
+"""Async Redis client singleton for caching and job queue."""
+
+import redis.asyncio as aioredis
+from arq import create_pool
+from arq.connections import ArqRedis, RedisSettings
+from config import REDIS_URL
+
+_arq_pool: ArqRedis | None = None
+_plain_redis: aioredis.Redis | None = None
+
+
+async def get_arq_pool() -> ArqRedis:
+    """Return a shared arq pool (ArqRedis extends redis.asyncio.Redis)."""
+    global _arq_pool
+    if _arq_pool is None:
+        _arq_pool = await create_pool(RedisSettings.from_dsn(REDIS_URL))
+    return _arq_pool
+
+
+async def get_redis() -> aioredis.Redis:
+    """Return a shared plain async Redis client."""
+    global _plain_redis
+    if _plain_redis is None:
+        _plain_redis = aioredis.from_url(REDIS_URL, decode_responses=True)
+    return _plain_redis
+
+
+async def close_redis_connections():
+    """Close all Redis connections (call on shutdown)."""
+    global _arq_pool, _plain_redis
+    if _arq_pool:
+        await _arq_pool.close()
+        _arq_pool = None
+    if _plain_redis:
+        await _plain_redis.close()
+        _plain_redis = None
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -13,3 +13,6 @@ tenacity
 prometheus-client
 httpx
 gunicorn
+mcp
+redis
+arq
--- a/backend/routes/ask.py
+++ b/backend/routes/ask.py
@@ -1,18 +1,153 @@
+import asyncio
 import json
 import re
 from datetime import UTC, datetime, timedelta

 import httpx
 import structlog
-from auth import require_auth
-from config import LLM_API_KEY, LLM_API_VERSION, LLM_BASE_URL, LLM_MAX_EVENTS, LLM_MODEL, LLM_TIMEOUT_SECONDS
+from auth import require_auth, user_can_access_privacy_services
+from config import (
+    LLM_API_KEY,
+    LLM_API_VERSION,
+    LLM_BASE_URL,
+    LLM_MAX_EVENTS,
+    LLM_MODEL,
+    LLM_TIMEOUT_SECONDS,
+    PRIVACY_SENSITIVE_OPERATIONS,
+    PRIVACY_SERVICES,
+)
 from database import events_collection
 from fastapi import APIRouter, Depends, HTTPException
+from jobs import get_cached_ask, get_cached_explain, set_cached_ask, set_cached_explain
 from models.api import AskRequest, AskResponse
+from redis_client import get_arq_pool

 router = APIRouter(dependencies=[Depends(require_auth)])
 logger = structlog.get_logger("aoc.ask")

+# ---------------------------------------------------------------------------
+# Intent extraction — map question keywords to relevant audit services
+# ---------------------------------------------------------------------------
+
+_SERVICE_INTENTS = {
+    "intune": ["Intune"],
+    "device": ["Intune", "Device"],
+    "laptop": ["Intune", "Device"],
+    "mobile": ["Intune", "Device"],
+    "phone": ["Intune", "Device"],
+    "ipad": ["Intune", "Device"],
+    "app": ["Intune", "ApplicationManagement"],
+    "application": ["Intune", "ApplicationManagement"],
+    "policy": ["Intune", "Policy"],
+    "compliance": ["Intune", "Policy"],
+    "user": ["Directory", "UserManagement"],
+    "group": ["Directory", "GroupManagement"],
+    "role": ["Directory", "RoleManagement"],
+    "permission": ["Directory", "RoleManagement"],
+    "license": ["Directory", "License"],
+    "email": ["Exchange"],
+    "mailbox": ["Exchange"],
+    "mail": ["Exchange"],
+    "message": ["Exchange", "Teams"],
+    "file": ["SharePoint"],
+    "sharepoint": ["SharePoint"],
+    "site": ["SharePoint"],
+    "document": ["SharePoint"],
+    "team": ["Teams"],
+    "channel": ["Teams"],
+    "meeting": ["Teams"],
+    "call": ["Teams"],
+}
+
+# Services that are extremely noisy for typical admin questions.
+# We exclude them by default on broad questions unless the user explicitly mentions them.
+_NOISY_SERVICES = {"Exchange", "SharePoint", "Teams"}
+
+# Services that are generally admin-relevant and kept by default.
+_DEFAULT_ADMIN_SERVICES = {
+    "Directory",
+    "UserManagement",
+    "GroupManagement",
+    "RoleManagement",
+    "ApplicationManagement",
+    "Intune",
+    "Device",
+    "Policy",
+    "Teams",
+    "License",
+}
+
+
+def _extract_intent_services(question: str) -> tuple[list[str] | None, bool]:
+    """
+    Extract relevant services from the question.
+
+    Returns:
+        (services, is_explicit):
+        - services: list of service names to query, or None for default admin set
+        - is_explicit: True if the user explicitly mentioned a noisy service
+    """
+    q_lower = question.lower()
+    tokens = set(re.findall(r"\b[a-z]+\b", q_lower))
+
+    matched_services = set()
+    for token, services in _SERVICE_INTENTS.items():
+        if token in tokens:
+            matched_services.update(services)
+
+    if matched_services:
+        # User asked something specific — return exactly what they asked for
+        is_explicit = not matched_services.isdisjoint(_NOISY_SERVICES)
+        return sorted(matched_services), is_explicit
+
+    # Broad question with no clear intent — default to admin-relevant services only
+    return None, False
+
+
+# ---------------------------------------------------------------------------
+# Smart sampling — stratified by importance so the LLM sees signal, not noise
+# ---------------------------------------------------------------------------
+
+
+def _smart_sample(events: list[dict], max_events: int = 200) -> list[dict]:
+    """
+    Return a curated subset that preserves diversity and prioritises signal.
+
+    Tiers:
+      1. Failures (always valuable)
+      2. High-admin-value services (Intune, Device, Directory, etc.)
+      3. Everything else
+    """
+    if len(events) <= max_events:
+        return events
+
+    high_value = {
+        "Directory",
+        "UserManagement",
+        "GroupManagement",
+        "RoleManagement",
+        "Intune",
+        "Device",
+        "Policy",
+        "ApplicationManagement",
+    }
+
+    failures = [e for e in events if str(e.get("result") or "").lower() in ("failure", "failed")]
+    high_val = [e for e in events if e.get("service") in high_value and e not in failures]
+    rest = [e for e in events if e not in failures and e not in high_val]
+
+    # Allocate slots: half to failures+high-value, half to rest (but never let rest dominate)
+    slots = max_events
+    failure_cap = min(len(failures), max(10, slots // 4))
+    high_cap = min(len(high_val), max(20, slots // 4))
+    rest_cap = slots - failure_cap - high_cap
+
+    sampled = failures[:failure_cap] + high_val[:high_cap] + rest[:rest_cap]
+    # Sort back to chronological order
+    sampled.sort(key=lambda e: e.get("timestamp") or "", reverse=True)
+    return sampled
+
+
 # ---------------------------------------------------------------------------
 # Time-range extraction
 # ---------------------------------------------------------------------------
@@ -104,7 +239,17 @@ def _extract_entity(question: str) -> str | None:
 # ---------------------------------------------------------------------------


-def _build_event_query(entity: str | None, start: str | None, end: str | None) -> dict:
+def _build_event_query(
+    entity: str | None,
+    start: str | None,
+    end: str | None,
+    services: list[str] | None = None,
+    actor: str | None = None,
+    operation: str | None = None,
+    result: str | None = None,
+    include_tags: list[str] | None = None,
+    exclude_tags: list[str] | None = None,
+) -> dict:
    filters = []

    if start or end:
@@ -128,6 +273,28 @@ def _build_event_query(entity: str | None, start: str | None, end: str | None) -
            }
        )

+    if services:
+        filters.append({"service": {"$in": services}})
+    if actor:
+        actor_safe = re.escape(actor)
+        filters.append(
+            {
+                "$or": [
+                    {"actor_display": {"$regex": actor_safe, "$options": "i"}},
+                    {"actor_upn": {"$regex": actor_safe, "$options": "i"}},
+                    {"actor.user.userPrincipalName": {"$regex": actor_safe, "$options": "i"}},
+                ]
+            }
+        )
+    if operation:
+        filters.append({"operation": {"$regex": re.escape(operation), "$options": "i"}})
+    if result:
+        filters.append({"result": {"$regex": re.escape(result), "$options": "i"}})
+    if include_tags:
+        filters.append({"tags": {"$all": include_tags}})
+    if exclude_tags:
+        filters.append({"tags": {"$not": {"$all": exclude_tags}}})
+
    return {"$and": filters} if filters else {}


@@ -136,22 +303,80 @@ def _build_event_query(entity: str | None, start: str | None, end: str | None) -
 # ---------------------------------------------------------------------------

 _SYSTEM_PROMPT = """You are an IT operations assistant. An administrator has asked a question about audit logs.
-Your job is to read the list of audit events below and write a concise, plain-language answer.
+Your job is to read the data below and write a concise, plain-language answer.
+
+The input may be either:
+- A small list of individual audit events (numbered Event #1, #2, etc.), or
+- An aggregated overview with counts by service, action, result, and actor, plus sample events.

 Rules:
 - Assume the reader is a non-expert admin.
- Group related events together and tell a coherent story.
+- For aggregated overviews: summarise the scale, top patterns, and highlight anomalies or failures.
+- For small event lists: group related events together and tell a coherent story.
 - Highlight anything unusual, failed actions, or privilege escalations.
 - Reference specific event numbers (e.g., "Event #3") when making claims so the user can verify.
+- If the data is an aggregated subset of a larger result set, acknowledge the scale (e.g., "847 events occurred — the top pattern was...").
 - If there are no events, say so clearly.
 - Keep the answer under 300 words.
- Do not invent events that are not in the list.
+- Do not invent events or patterns that are not supported by the data.
 """


-def _format_events_for_llm(events: list[dict]) -> str:
+def _aggregate_counts(events: list[dict]) -> dict:
+    """Build lightweight aggregation tables for large result sets."""
+    from collections import Counter
+
+    svc_counts = Counter(e.get("service") or "Unknown" for e in events)
+    op_counts = Counter(e.get("operation") or "Unknown" for e in events)
+    result_counts = Counter(e.get("result") or "Unknown" for e in events)
+    actor_counts = Counter(e.get("actor_display") or "Unknown" for e in events)
+    return {
+        "services": svc_counts.most_common(10),
+        "operations": op_counts.most_common(10),
+        "results": result_counts.most_common(5),
+        "actors": actor_counts.most_common(10),
+    }
+
+
+def _format_events_for_llm(
+    events: list[dict], total: int | None = None, excluded_services: list[str] | None = None
+) -> str:
    lines = []
-    for i, e in enumerate(events, 1):
+
+    # If we have a large result set, send aggregation + samples instead of raw dump
+    if total is not None and total > len(events) and len(events) >= 50:
+        lines.append(f"Result set overview: {total} total events (showing a curated sample of {len(events)}).\n")
+        if excluded_services:
+            lines.append(f"Note: high-volume services excluded by default: {', '.join(excluded_services)}.\n")
+        agg = _aggregate_counts(events)
+        lines.append("Breakdown by service:")
+        for svc, cnt in agg["services"]:
+            lines.append(f"  {svc}: {cnt}")
+        lines.append("\nBreakdown by action:")
+        for op, cnt in agg["operations"]:
+            lines.append(f"  {op}: {cnt}")
+        lines.append("\nBreakdown by result:")
+        for res, cnt in agg["results"]:
+            lines.append(f"  {res}: {cnt}")
+        lines.append("\nTop actors:")
+        for actor, cnt in agg["actors"]:
+            lines.append(f"  {actor}: {cnt}")
+        # Include failures and a few recent samples
+        failures = [e for e in events if str(e.get("result") or "").lower() in ("failure", "failed")]
+        if failures:
+            lines.append(f"\nFailures ({len(failures)}):")
+            for e in failures[:10]:
+                ts = e.get("timestamp", "?")[:16].replace("T", " ")
+                op = e.get("operation", "unknown")
+                actor = e.get("actor_display", "unknown")
+                lines.append(f"  {ts} — {op} by {actor}")
+        lines.append("\nMost recent sample events:")
+    else:
+        if total is not None and total > len(events):
+            lines.append(f"Showing {len(events)} of {total} total matching events (most recent first):\n")
+
+    # Always include the first N raw events as detail (up to 50)
+    for i, e in enumerate(events[:50], 1):
        ts = e.get("timestamp") or "unknown time"
        op = e.get("operation") or "unknown action"
        actor = e.get("actor_display") or "unknown actor"
@@ -181,11 +406,16 @@ def _build_chat_url(base_url: str, api_version: str) -> str:
    return url


-async def _call_llm(question: str, events: list[dict]) -> str:
+async def _call_llm(
+    question: str,
+    events: list[dict],
+    total: int | None = None,
+    excluded_services: list[str] | None = None,
+) -> str:
    if not LLM_API_KEY:
        raise RuntimeError("LLM_API_KEY not configured")

-    context = _format_events_for_llm(events)
+    context = _format_events_for_llm(events, total=total, excluded_services=excluded_services)
    messages = [
        {"role": "system", "content": _SYSTEM_PROMPT},
        {
@@ -238,6 +468,207 @@ def _to_event_ref(e: dict) -> dict:
    }


+_EXPLAIN_SYSTEM_PROMPT = """You are a Microsoft 365 security and compliance expert.
+An administrator needs help understanding an audit event.
+
+Your task:
+1. Explain what happened in plain language (1-2 sentences).
+2. Identify who performed the action and what was the target.
+3. Assess whether this is typical admin activity or something to investigate.
+4. Highlight any security implications (privilege escalation, unusual actor, after-hours activity, etc.).
+5. Suggest what the admin should do next, if anything.
+
+Keep the answer under 200 words. Use bullet points for readability.
+Do not invent facts that are not in the data.
+"""
+
+
+_GUID_RE = re.compile(r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$")
+
+
+def _extract_guids(obj: dict | list | str) -> set[str]:
+    """Recursively extract UUID-like strings from a JSON structure."""
+    guids = set()
+    if isinstance(obj, dict):
+        for k, v in obj.items():
+            if k.lower() in ("id", "groupid", "userid", "targetid") and isinstance(v, str) and _GUID_RE.match(v):
+                guids.add(v)
+            guids.update(_extract_guids(v))
+    elif isinstance(obj, list):
+        for item in obj:
+            guids.update(_extract_guids(item))
+    elif isinstance(obj, str) and _GUID_RE.match(obj):
+        guids.add(obj)
+    return guids
+
+
+async def _resolve_guids_for_event(event: dict) -> dict[str, str]:
+    """Try to resolve GUIDs in an event to human-readable names via Graph API."""
+    raw = event.get("raw") or {}
+    guids = _extract_guids(raw)
+    # Also include any GUIDs in targetResources that might not have displayName
+    for tr in raw.get("targetResources") or []:
+        tid = tr.get("id")
+        if tid and _GUID_RE.match(tid):
+            guids.add(tid)
+    for tr in raw.get("modifiedProperties") or []:
+        for key in ("oldValue", "newValue"):
+            val = tr.get(key)
+            if val and _GUID_RE.match(val):
+                guids.add(val)
+
+    if not guids:
+        return {}
+
+    try:
+        from graph.auth import get_access_token
+        from graph.resolve import resolve_directory_object
+
+        token = await asyncio.to_thread(get_access_token)
+        cache: dict[str, dict] = {}
+        resolved = {}
+        for gid in guids:
+            result = await asyncio.to_thread(resolve_directory_object, gid, token, cache)
+            if result:
+                resolved[gid] = result["name"]
+        return resolved
+    except Exception as exc:
+        logger.warning("GUID resolution failed", error=str(exc))
+        return {}
+
+
+async def _explain_event(event: dict, related: list[dict]) -> str:
+    if not LLM_API_KEY:
+        raise RuntimeError("LLM_API_KEY not configured")
+
+    # Resolve GUIDs to names before sending to LLM
+    resolved = await _resolve_guids_for_event(event)
+
+    event_text = json.dumps(event, indent=2, default=str)
+    resolution_text = ""
+    if resolved:
+        resolution_text = "\nResolved GUIDs:\n"
+        for gid, name in resolved.items():
+            resolution_text += f"  {gid} → {name}\n"
+
+    related_text = ""
+    if related:
+        related_text = "\n\nRelated events in the last 24 hours:\n"
+        for i, e in enumerate(related[:10], 1):
+            ts = e.get("timestamp", "?")[:16].replace("T", " ")
+            op = e.get("operation", "unknown")
+            actor = e.get("actor_display", "unknown")
+            targets = ", ".join(e.get("target_displays") or []) or "—"
+            result = e.get("result", "—")
+            related_text += f"{i}. {ts} — {op} by {actor} on {targets} ({result})\n"
+
+    messages = [
+        {"role": "system", "content": _EXPLAIN_SYSTEM_PROMPT},
+        {
+            "role": "user",
+            "content": f"Audit event:\n{event_text}{resolution_text}{related_text}\n\nPlease explain this event.",
+        },
+    ]
+
+    url = _build_chat_url(LLM_BASE_URL, LLM_API_VERSION)
+    headers = {"Content-Type": "application/json"}
+    if "azure" in LLM_BASE_URL.lower() or "cognitiveservices" in LLM_BASE_URL.lower():
+        headers["api-key"] = LLM_API_KEY
+    else:
+        headers["Authorization"] = f"Bearer {LLM_API_KEY}"
+
+    payload = {
+        "model": LLM_MODEL,
+        "messages": messages,
+        "max_completion_tokens": 600,
+    }
+
+    async with httpx.AsyncClient(timeout=LLM_TIMEOUT_SECONDS) as client:
+        resp = await client.post(url, headers=headers, json=payload)
+        if resp.status_code >= 400:
+            body = resp.text
+            logger.error("LLM API error", status_code=resp.status_code, url=url, response_body=body)
+            raise RuntimeError(f"LLM API error {resp.status_code}: {body[:500]}")
+        data = resp.json()
+        return data["choices"][0]["message"]["content"].strip()
+
+
+@router.post("/events/{event_id}/explain")
+async def explain_event(event_id: str, user: dict = Depends(require_auth)):
+    event = events_collection.find_one({"id": event_id})
+    if not event:
+        raise HTTPException(status_code=404, detail="Event not found")
+
+    if (
+        event.get("service") in PRIVACY_SERVICES or event.get("operation") in PRIVACY_SENSITIVE_OPERATIONS
+    ) and not user_can_access_privacy_services(user):
+        raise HTTPException(status_code=403, detail="Access to this event is restricted")
+
+    event.pop("_id", None)
+
+    # Fetch related events for context (same actor or target in last 24h)
+    related = []
+    since = (datetime.now(UTC) - timedelta(hours=24)).isoformat().replace("+00:00", "Z")
+    actor = event.get("actor_upn") or event.get("actor_display")
+    target = event.get("target_displays", [None])[0] if event.get("target_displays") else None
+
+    or_filters = [{"timestamp": {"$gte": since}}, {"id": {"$ne": event_id}}]
+    if actor:
+        or_filters.append(
+            {
+                "$or": [
+                    {"actor_upn": actor},
+                    {"actor_display": actor},
+                ]
+            }
+        )
+    if target:
+        or_filters.append({"target_displays": target})
+
+    if len(or_filters) > 2:
+        try:
+            rel_cursor = events_collection.find({"$and": or_filters}).sort("timestamp", -1).limit(10)
+            related = list(rel_cursor)
+            for r in related:
+                r.pop("_id", None)
+                r.pop("raw", None)
+        except Exception as exc:
+            logger.warning("Failed to fetch related events", error=str(exc))
+
+    if not LLM_API_KEY:
+        return {
+            "explanation": "LLM is not configured. Set LLM_API_KEY in your environment to enable event explanations.",
+            "llm_used": False,
+            "llm_error": "LLM_API_KEY not configured",
+        }
+
+    # Check cache first
+    redis = await get_arq_pool()
+    cached = await get_cached_explain(redis, event_id)
+    if cached:
+        cached["related_count"] = len(related)
+        return cached
+
+    try:
+        explanation = await _explain_event(event, related)
+        result = {
+            "explanation": explanation,
+            "llm_used": True,
+            "llm_error": None,
+            "related_count": len(related),
+        }
+        await set_cached_explain(redis, event_id, result)
+        return result
+    except Exception as exc:
+        logger.warning("Event explanation failed", error=str(exc))
+        return {
+            "explanation": "Unable to generate an explanation at this time. Please check the raw event details.",
+            "llm_used": False,
+            "llm_error": str(exc),
+            "related_count": len(related),
+        }
+
+
@router.post("/ask", response_model=AskResponse)
 async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
    question = body.question.strip()
@@ -246,6 +677,7 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):

    start, end = _extract_time_range(question)
    entity = _extract_entity(question)
+    intent_services, explicit_noisy = _extract_intent_services(question)

    # Default to last 7 days if no time range detected
    if not start:
@@ -253,47 +685,158 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
        start = (now - timedelta(days=7)).isoformat().replace("+00:00", "Z")
        end = now.isoformat().replace("+00:00", "Z")

-    query = _build_event_query(entity, start, end)
+    # -----------------------------------------------------------------------
+    # Decide which services to query
+    # -----------------------------------------------------------------------
+    excluded_services: list[str] = []
+    if body.services:
+        # User explicitly filtered via UI — respect that exactly
+        query_services = body.services
+    elif intent_services is not None:
+        # NL question implies specific services
+        query_services = intent_services
+    else:
+        # Broad question with no intent — exclude noisy services by default
+        query_services = sorted(_DEFAULT_ADMIN_SERVICES)
+        excluded_services = sorted(_NOISY_SERVICES)
+
+    # -----------------------------------------------------------------------
+    # Build and run query
+    # -----------------------------------------------------------------------
+    privacy_excluded_services = [] if user_can_access_privacy_services(user) else list(PRIVACY_SERVICES)
+    privacy_excluded_ops = [] if user_can_access_privacy_services(user) else list(PRIVACY_SENSITIVE_OPERATIONS)
+    query = _build_event_query(
+        entity,
+        start,
+        end,
+        services=query_services,
+        actor=body.actor,
+        operation=body.operation,
+        result=body.result,
+        include_tags=body.include_tags,
+        exclude_tags=body.exclude_tags,
+    )
+    extra_filters = []
+    if privacy_excluded_services:
+        extra_filters.append({"service": {"$nin": privacy_excluded_services}})
+    if privacy_excluded_ops:
+        extra_filters.append({"operation": {"$nin": privacy_excluded_ops}})
+    if extra_filters:
+        query["$and"] = query.get("$and", []) + extra_filters

    try:
-        cursor = events_collection.find(query).sort([("timestamp", -1)]).limit(LLM_MAX_EVENTS)
-        events = list(cursor)
+        total = events_collection.count_documents(query)
+        # Fetch a generous window so we can apply smart sampling in Python
+        cursor = events_collection.find(query).sort([("timestamp", -1)]).limit(1000)
+        raw_events = list(cursor)
    except Exception as exc:
        logger.error("Failed to query events for ask", error=str(exc))
        raise HTTPException(status_code=500, detail=f"Database query failed: {exc}") from exc

-    for e in events:
+    for e in raw_events:
        e["_id"] = str(e.get("_id", ""))

+    # Apply smart sampling (preserves failures, prioritises admin-relevant services)
+    events = _smart_sample(raw_events, max_events=LLM_MAX_EVENTS)
+
    # If no events, return early
    if not events:
        return AskResponse(
            answer="I couldn't find any audit events matching your question. Try broadening the time range or checking the spelling of the device/user name.",
            events=[],
-            query_info={"entity": entity, "start": start, "end": end, "event_count": 0},
+            query_info={
+                "entity": entity,
+                "start": start,
+                "end": end,
+                "event_count": 0,
+                "total_matched": total,
+                "services_queried": query_services,
+                "excluded_services": excluded_services,
+            },
            llm_used=False,
            llm_error="LLM not used — no events found." if not LLM_API_KEY else None,
        )

-    # Try LLM summarisation
+    # Try LLM summarisation (with caching + optional async)
    answer = ""
    llm_used = False
    llm_error = None
-    if not LLM_API_KEY:
-        llm_error = "LLM_API_KEY is not configured. Set it in your .env to enable AI narrative summarisation."
+    job_id = None
+
+    filters_snapshot = {
+        "services": body.services,
+        "actor": body.actor,
+        "operation": body.operation,
+        "result": body.result,
+        "start": body.start,
+        "end": body.end,
+        "include_tags": body.include_tags,
+        "exclude_tags": body.exclude_tags,
+    }
+
+    if LLM_API_KEY:
+        redis = await get_arq_pool()
+        cached = await get_cached_ask(redis, question, filters_snapshot, events)
+        if cached:
+            answer = cached.get("answer", "")
+            llm_used = cached.get("llm_used", False)
+            llm_error = cached.get("llm_error")
+        elif body.async_mode:
+            pool = await get_arq_pool()
+            job = await pool.enqueue_job(
+                "process_ask_question",
+                question,
+                filters_snapshot,
+                events,
+                total,
+                excluded_services,
+            )
+            job_id = job.job_id if job else None
+            return AskResponse(
+                answer="Your question is being processed. Poll /api/jobs/{job_id} for the result.",
+                events=[_to_event_ref(e) for e in events],
+                query_info={
+                    "entity": entity,
+                    "start": start,
+                    "end": end,
+                    "event_count": len(events),
+                    "total_matched": total,
+                    "services_queried": query_services,
+                    "excluded_services": excluded_services,
+                    "mongo_query": json.dumps(query, default=str),
+                },
+                llm_used=False,
+                llm_error=None,
+                job_id=job_id,
+            )
+        else:
+            try:
+                answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
+                llm_used = True
+                await set_cached_ask(
+                    redis,
+                    question,
+                    filters_snapshot,
+                    events,
+                    {
+                        "answer": answer,
+                        "llm_used": True,
+                        "llm_error": None,
+                    },
+                )
+            except Exception as exc:
+                llm_error = f"LLM call failed: {exc}"
+                logger.warning("LLM call failed, falling back to structured summary", error=str(exc))
    else:
-        try:
-            answer = await _call_llm(question, events)
-            llm_used = True
-        except Exception as exc:
-            llm_error = f"LLM call failed: {exc}"
-            logger.warning("LLM call failed, falling back to structured summary", error=str(exc))
+        llm_error = "LLM_API_KEY is not configured. Set it in your .env to enable AI narrative summarisation."

    # Fallback: structured summary if LLM unavailable or failed
    if not answer:
-        parts = [f"Found {len(events)} event(s)"]
+        parts = [f"Found {total} event(s)"]
        if entity:
            parts.append(f"related to **{entity}**")
+        if excluded_services:
+            parts.append(f"(excluding {', '.join(excluded_services)})")
        parts.append(f"between {start[:10]} and {end[:10]}.\n")

        for i, e in enumerate(events[:10], 1):
@@ -317,8 +860,12 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
            "start": start,
            "end": end,
            "event_count": len(events),
+            "total_matched": total,
+            "services_queried": query_services,
+            "excluded_services": excluded_services,
            "mongo_query": json.dumps(query, default=str),
        },
        llm_used=llm_used,
        llm_error=llm_error,
+        job_id=job_id,
    )
--- a/backend/routes/config.py
+++ b/backend/routes/config.py
@@ -1,8 +1,10 @@
 from config import (
+    AI_FEATURES_ENABLED,
    AUTH_CLIENT_ID,
    AUTH_ENABLED,
    AUTH_SCOPE,
    AUTH_TENANT_ID,
+    DEFAULT_PAGE_SIZE,
 )
 from fastapi import APIRouter

@@ -18,3 +20,11 @@ def auth_config():
        "scope": AUTH_SCOPE,
        "redirect_uri": None,  # frontend uses window.location.origin by default
    }
+
+
+@router.get("/config/features")
+def features_config():
+    return {
+        "ai_features_enabled": AI_FEATURES_ENABLED,
+        "default_page_size": DEFAULT_PAGE_SIZE,
+    }
--- a/backend/routes/events.py
+++ b/backend/routes/events.py
@@ -3,8 +3,9 @@ import re
 from datetime import UTC, datetime

 from audit_trail import log_action
-from auth import require_auth
+from auth import require_auth, user_can_access_privacy_services
 from bson import ObjectId
+from config import PRIVACY_SENSITIVE_OPERATIONS, PRIVACY_SERVICES
 from database import events_collection
 from fastapi import APIRouter, Depends, HTTPException, Query
 from models.api import (
@@ -44,6 +45,7 @@ def _build_query(
    cursor: str | None = None,
    include_tags: list[str] | None = None,
    exclude_tags: list[str] | None = None,
+    exclude_operations: list[str] | None = None,
 ) -> dict:
    filters = []

@@ -51,6 +53,8 @@ def _build_query(
        filters.append({"service": service})
    if services:
        filters.append({"service": {"$in": services}})
+    if exclude_operations:
+        filters.append({"operation": {"$nin": exclude_operations}})
    if actor:
        actor_safe = re.escape(actor)
        filters.append(
@@ -125,6 +129,8 @@ def list_events(
    exclude_tags: list[str] | None = Query(default=None),
    user: dict = Depends(require_auth),
 ):
+    privacy_excluded_services = [] if user_can_access_privacy_services(user) else list(PRIVACY_SERVICES)
+    privacy_excluded_ops = [] if user_can_access_privacy_services(user) else list(PRIVACY_SENSITIVE_OPERATIONS)
    query = _build_query(
        service=service,
        services=services,
@@ -137,7 +143,13 @@ def list_events(
        cursor=cursor,
        include_tags=include_tags,
        exclude_tags=exclude_tags,
+        exclude_operations=privacy_excluded_ops,
    )
+    if privacy_excluded_services:
+        query = query if query else {}
+        if "$and" not in query:
+            query = {"$and": [query]} if query else {"$and": []}
+        query["$and"].append({"service": {"$nin": privacy_excluded_services}})

    safe_page_size = max(1, min(page_size, 500))

@@ -202,6 +214,8 @@ def bulk_tags(
    exclude_tags: list[str] | None = Query(default=None),
    user: dict = Depends(require_auth),
 ):
+    privacy_excluded_services = [] if user_can_access_privacy_services(user) else list(PRIVACY_SERVICES)
+    privacy_excluded_ops = [] if user_can_access_privacy_services(user) else list(PRIVACY_SENSITIVE_OPERATIONS)
    query = _build_query(
        service=service,
        services=services,
@@ -213,7 +227,13 @@ def bulk_tags(
        search=search,
        include_tags=include_tags,
        exclude_tags=exclude_tags,
+        exclude_operations=privacy_excluded_ops,
    )
+    if privacy_excluded_services:
+        query = query if query else {}
+        if "$and" not in query:
+            query = {"$and": [query]} if query else {"$and": []}
+        query["$and"].append({"service": {"$nin": privacy_excluded_services}})
    tags = [t.strip() for t in body.tags if t.strip()]
    if not tags:
        raise HTTPException(status_code=400, detail="No tags provided")
@@ -235,7 +255,10 @@ def bulk_tags(


@router.get("/filter-options", response_model=FilterOptionsResponse)
-def filter_options(limit: int = Query(default=200, ge=1, le=1000)):
+def filter_options(
+    limit: int = Query(default=200, ge=1, le=1000),
+    user: dict = Depends(require_auth),
+):
    safe_limit = max(1, min(limit, 1000))
    try:
        services = sorted(events_collection.distinct("service"))[:safe_limit]
@@ -247,6 +270,10 @@ def filter_options(limit: int = Query(default=200, ge=1, le=1000)):
    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"Failed to load filter options: {exc}") from exc

+    if not user_can_access_privacy_services(user):
+        services = [s for s in services if s not in PRIVACY_SERVICES]
+        operations = [o for o in operations if o not in PRIVACY_SENSITIVE_OPERATIONS]
+
    return {
        "services": services,
        "operations": operations,
--- a/backend/routes/jobs.py
+++ b/backend/routes/jobs.py
@@ -0,0 +1,43 @@
+"""Job status endpoints for async LLM operations."""
+
+from arq.jobs import Job, JobStatus
+from auth import require_auth
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel
+from redis_client import get_redis
+
+router = APIRouter(dependencies=[Depends(require_auth)])
+
+
+class JobStatusResponse(BaseModel):
+    job_id: str
+    status: str  # queued, in_progress, complete, not_found, deferred
+    result: dict | None = None
+    error: str | None = None
+
+
+@router.get("/jobs/{job_id}", response_model=JobStatusResponse)
+async def get_job_status(job_id: str, user: dict = Depends(require_auth)):
+    """Poll for the result of an async LLM job."""
+    redis = await get_redis()
+    job = Job(job_id, redis)
+    status = await job.status()
+
+    if status == JobStatus.not_found:
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    result = None
+    error = None
+    if status == JobStatus.complete:
+        try:
+            result_data = await job.result(timeout=0)
+            result = result_data if isinstance(result_data, dict) else {"data": str(result_data)}
+        except Exception as exc:
+            error = str(exc)
+
+    return JobStatusResponse(
+        job_id=job_id,
+        status=status.value,
+        result=result,
+        error=error,
+    )
--- a/backend/routes/mcp.py
+++ b/backend/routes/mcp.py
@@ -0,0 +1,124 @@
+"""MCP server over SSE (HTTP) transport, mounted inside FastAPI with OIDC auth."""
+
+import structlog
+from auth import (
+    AUTH_ALLOWED_GROUPS,
+    AUTH_ALLOWED_ROLES,
+    AUTH_ENABLED,
+    _allowed,
+    _decode_token,
+    _get_jwks,
+)
+from mcp.server import Server
+from mcp.server.sse import SseServerTransport
+from mcp.types import TextContent, Tool
+from mcp_common import (
+    ASK_SCHEMA,
+    GET_EVENT_SCHEMA,
+    GET_SUMMARY_SCHEMA,
+    SEARCH_EVENTS_SCHEMA,
+    handle_ask,
+    handle_get_event,
+    handle_get_summary,
+    handle_search_events,
+)
+from starlette.requests import Request
+from starlette.responses import Response
+
+logger = structlog.get_logger("aoc.mcp")
+
+mcp_app = Server("aoc")
+transport = SseServerTransport("/messages/")
+
+
+@mcp_app.list_tools()
+async def list_tools() -> list[Tool]:
+    return [
+        Tool(
+            name="search_events",
+            description="Search audit events by entity, service, operation, or result.",
+            inputSchema=SEARCH_EVENTS_SCHEMA,
+        ),
+        Tool(name="get_event", description="Retrieve a single audit event by its ID.", inputSchema=GET_EVENT_SCHEMA),
+        Tool(
+            name="get_summary",
+            description="Get an aggregated summary of audit activity for the last N days.",
+            inputSchema=GET_SUMMARY_SCHEMA,
+        ),
+        Tool(
+            name="ask",
+            description="Ask a natural language question about audit logs. Returns a narrative answer.",
+            inputSchema=ASK_SCHEMA,
+        ),
+    ]
+
+
+@mcp_app.call_tool()
+async def call_tool(name: str, arguments: dict) -> list[TextContent]:
+    if name == "search_events":
+        return await handle_search_events(arguments)
+    if name == "get_event":
+        return await handle_get_event(arguments)
+    if name == "get_summary":
+        return await handle_get_summary(arguments)
+    if name == "ask":
+        return await handle_ask(arguments)
+    raise ValueError(f"Unknown tool: {name}")
+
+
+async def _validate_auth(request: Request) -> dict | None:
+    """Validate Bearer token. Returns claims dict or None on failure."""
+    if not AUTH_ENABLED:
+        return {"sub": "anonymous"}
+
+    auth_header = request.headers.get("authorization", "")
+    if not auth_header or not auth_header.lower().startswith("bearer "):
+        return None
+
+    token = auth_header.split(" ", 1)[1]
+    try:
+        jwks = _get_jwks()
+        claims = _decode_token(token, jwks)
+    except Exception as exc:
+        logger.warning("MCP auth failed", error=str(exc))
+        return None
+
+    if not _allowed(claims, AUTH_ALLOWED_ROLES, AUTH_ALLOWED_GROUPS):
+        logger.warning("MCP auth forbidden", sub=claims.get("sub"))
+        return None
+
+    return claims
+
+
+async def mcp_asgi(scope: dict, receive, send):
+    """ASGI application for MCP over SSE, mounted under /mcp in FastAPI."""
+    if scope["type"] != "http":
+        return
+
+    request = Request(scope, receive)
+
+    # Auth check
+    claims = await _validate_auth(request)
+    if claims is None:
+        response = Response("Unauthorized", status_code=401)
+        await response(scope, receive, send)
+        return
+
+    path = scope.get("path", "")
+    root_path = scope.get("root_path", "")
+    relative_path = path[len(root_path) :] if path.startswith(root_path) else path
+    method = scope.get("method", "")
+
+    if relative_path == "/sse" and method == "GET":
+        logger.info("MCP SSE connection established", sub=claims.get("sub", "unknown"))
+        async with transport.connect_sse(scope, receive, send) as (read_stream, write_stream):
+            await mcp_app.run(
+                read_stream,
+                write_stream,
+                mcp_app.create_initialization_options(),
+            )
+    elif relative_path == "/messages/" and method == "POST":
+        await transport.handle_post_message(scope, receive, send)
+    else:
+        response = Response("Not found", status_code=404)
+        await response(scope, receive, send)
--- a/backend/routes/saved_searches.py
+++ b/backend/routes/saved_searches.py
@@ -0,0 +1,60 @@
+"""CRUD for saved filter searches (bookmarks)."""
+
+import uuid
+from datetime import UTC, datetime
+
+import structlog
+from auth import require_auth
+from database import saved_searches_collection
+from fastapi import APIRouter, Depends, HTTPException
+
+router = APIRouter(dependencies=[Depends(require_auth)])
+logger = structlog.get_logger("aoc.saved_searches")
+
+
+def _user_sub(user: dict) -> str:
+    return user.get("sub", "anonymous")
+
+
+@router.get("/saved-searches")
+async def list_saved_searches(user: dict = Depends(require_auth)):
+    """Return saved searches for the current user."""
+    sub = _user_sub(user)
+    cursor = saved_searches_collection.find({"created_by": sub}).sort("created_at", -1)
+    items = []
+    for doc in cursor:
+        doc["id"] = doc.pop("_id")
+        items.append(doc)
+    return items
+
+
+@router.post("/saved-searches")
+async def create_saved_search(body: dict, user: dict = Depends(require_auth)):
+    """Save the current filter set."""
+    name = (body.get("name") or "").strip()
+    if not name:
+        raise HTTPException(status_code=400, detail="Name is required")
+
+    filters = body.get("filters") or {}
+    doc = {
+        "_id": str(uuid.uuid4()),
+        "name": name,
+        "filters": filters,
+        "created_at": datetime.now(UTC).isoformat().replace("+00:00", "Z"),
+        "created_by": _user_sub(user),
+    }
+    saved_searches_collection.insert_one(doc)
+    logger.info("Saved search created", name=name, user=doc["created_by"])
+    doc["id"] = doc.pop("_id")
+    return doc
+
+
+@router.delete("/saved-searches/{search_id}")
+async def delete_saved_search(search_id: str, user: dict = Depends(require_auth)):
+    """Delete a saved search (only if owned by current user)."""
+    sub = _user_sub(user)
+    result = saved_searches_collection.delete_one({"_id": search_id, "created_by": sub})
+    if result.deleted_count == 0:
+        raise HTTPException(status_code=404, detail="Saved search not found")
+    logger.info("Saved search deleted", search_id=search_id, user=sub)
+    return {"status": "deleted"}
--- a/backend/tests/conftest.py
+++ b/backend/tests/conftest.py
@@ -22,14 +22,23 @@ def mock_watermarks_collection():
@pytest.fixture(scope="function")
 def client(mock_events_collection, mock_watermarks_collection, monkeypatch):
    monkeypatch.setattr("database.events_collection", mock_events_collection)
+    monkeypatch.setattr("database.saved_searches_collection", mock_events_collection)
    monkeypatch.setattr("routes.fetch.events_collection", mock_events_collection)
    monkeypatch.setattr("routes.events.events_collection", mock_events_collection)
    monkeypatch.setattr("routes.ask.events_collection", mock_events_collection)
+    monkeypatch.setattr("routes.saved_searches.saved_searches_collection", mock_events_collection)
    monkeypatch.setattr("watermark.watermarks_collection", mock_watermarks_collection)
    monkeypatch.setattr("routes.health.watermarks_collection", mock_watermarks_collection)
    monkeypatch.setattr("routes.fetch.get_watermark", lambda source: None)
    monkeypatch.setattr("routes.fetch.set_watermark", lambda source, ts: None)
    monkeypatch.setattr("auth.AUTH_ENABLED", False)
+    monkeypatch.setattr("routes.mcp.AUTH_ENABLED", False)
+    monkeypatch.setattr("config.PRIVACY_SERVICES", set())
+    monkeypatch.setattr("config.PRIVACY_SENSITIVE_OPERATIONS", set())
+    monkeypatch.setattr("routes.events.PRIVACY_SERVICES", set())
+    monkeypatch.setattr("routes.events.PRIVACY_SENSITIVE_OPERATIONS", set())
+    monkeypatch.setattr("routes.ask.PRIVACY_SERVICES", set())
+    monkeypatch.setattr("routes.ask.PRIVACY_SENSITIVE_OPERATIONS", set())
    monkeypatch.setattr("database.db.command", lambda cmd: {"ok": 1} if cmd == "ping" else {})

    # Mock audit trail and rules collections so tests don't wait on real MongoDB
@@ -40,6 +49,21 @@ def client(mock_events_collection, mock_watermarks_collection, monkeypatch):
    monkeypatch.setattr("rules.rules_collection", audit_db["alert_rules"])
    monkeypatch.setattr("routes.rules.rules_collection", audit_db["alert_rules"])

+    # Mock Redis so tests don't require a running Redis server
+    class FakeRedis:
+        async def get(self, key):
+            return None
+
+        async def setex(self, key, ttl, value):
+            pass
+
+    async def fake_get_arq_pool():
+        return FakeRedis()
+
+    monkeypatch.setattr("redis_client.get_arq_pool", fake_get_arq_pool)
+    monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+    monkeypatch.setattr("routes.jobs.get_redis", fake_get_arq_pool)
+
    from main import app

    return TestClient(app)
--- a/backend/tests/test_api.py
+++ b/backend/tests/test_api.py
@@ -1,6 +1,264 @@
 from datetime import UTC, datetime


+def test_config_features(client):
+    response = client.get("/api/config/features")
+    assert response.status_code == 200
+    data = response.json()
+    assert "ai_features_enabled" in data
+    assert isinstance(data["ai_features_enabled"], bool)
+
+
+def test_ask_disabled_when_ai_features_off():
+    import subprocess
+    import sys
+
+    code = """
+import sys
+sys.path.insert(0, '.')
+import os
+os.environ['AI_FEATURES_ENABLED'] = 'false'
+
+# Re-import config with the env override
+import importlib
+import config
+importlib.reload(config)
+
+# Now import main; it will pick up the new AI_FEATURES_ENABLED
+import main
+ask_paths = [r.path for r in main.app.routes if hasattr(r, 'path') and 'ask' in r.path]
+print('ASK_PATHS:', ask_paths)
+assert len(ask_paths) == 0, f"Expected no ask routes, found: {ask_paths}"
+print('OK')
+"""
+    result = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True, cwd=".")
+    assert result.returncode == 0, f"Subprocess failed: {result.stdout}\n{result.stderr}"
+    assert "OK" in result.stdout
+
+
+def test_mcp_sse_mount_exists():
+    from main import app
+
+    mcp_mounts = [r for r in app.routes if getattr(r, "path", "") == "/mcp"]
+    assert len(mcp_mounts) == 1, "MCP mount not found in app routes"
+
+
+def test_mcp_messages_no_session(client):
+    response = client.post("/mcp/messages/")
+    # MCP transport returns 400 when session_id is missing, 404 when session not found
+    assert response.status_code in (400, 404)
+
+
+def test_mcp_sse_auth_required_when_enabled(client, monkeypatch):
+    monkeypatch.setattr("routes.mcp.AUTH_ENABLED", True)
+    response = client.get("/mcp/sse")
+    assert response.status_code == 401
+
+
+def test_explain_event_not_found(client):
+    response = client.post("/api/events/nonexistent/explain")
+    assert response.status_code == 404
+
+
+def test_explain_event_no_llm_key(client, mock_events_collection, monkeypatch):
+    monkeypatch.setattr("routes.ask.LLM_API_KEY", "")
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-explain",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Directory",
+            "operation": "Add user",
+            "result": "success",
+            "actor_display": "Alice",
+            "raw_text": "",
+        }
+    )
+    response = client.post("/api/events/evt-explain/explain")
+    assert response.status_code == 200
+    data = response.json()
+    assert "explanation" in data
+    assert data["llm_used"] is False
+    assert "LLM_API_KEY" in (data.get("llm_error") or "")
+
+
+def test_explain_event_with_llm_mock(client, mock_events_collection, monkeypatch):
+    monkeypatch.setattr("routes.ask.LLM_API_KEY", "test-key")
+
+    async def fake_explain(event, related):
+        return "This is a test explanation."
+
+    monkeypatch.setattr("routes.ask._explain_event", fake_explain)
+
+    class FakeRedis:
+        async def get(self, key):
+            return None
+
+        async def setex(self, key, ttl, value):
+            pass
+
+    async def fake_get_arq_pool():
+        return FakeRedis()
+
+    monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-explain2",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Directory",
+            "operation": "Add user",
+            "result": "success",
+            "actor_display": "Alice",
+            "raw_text": "",
+        }
+    )
+    response = client.post("/api/events/evt-explain2/explain")
+    assert response.status_code == 200
+    data = response.json()
+    assert data["explanation"] == "This is a test explanation."
+    assert data["llm_used"] is True
+
+
+def test_saved_searches_crud(client, monkeypatch):
+    monkeypatch.setattr("auth.AUTH_ENABLED", False)
+
+    # Create
+    response = client.post(
+        "/api/saved-searches", json={"name": "Test search", "filters": {"actor": "alice", "result": "success"}}
+    )
+    assert response.status_code == 200
+    created = response.json()
+    assert created["name"] == "Test search"
+    assert created["filters"]["actor"] == "alice"
+    search_id = created["id"]
+
+    # List
+    response2 = client.get("/api/saved-searches")
+    assert response2.status_code == 200
+    items = response2.json()
+    assert len(items) == 1
+    assert items[0]["name"] == "Test search"
+
+    # Delete
+    response3 = client.delete(f"/api/saved-searches/{search_id}")
+    assert response3.status_code == 200
+
+    # List empty
+    response4 = client.get("/api/saved-searches")
+    assert response4.status_code == 200
+    assert len(response4.json()) == 0
+
+
+def test_saved_searches_delete_not_found(client, monkeypatch):
+    monkeypatch.setattr("auth.AUTH_ENABLED", False)
+    response = client.delete("/api/saved-searches/nonexistent")
+    assert response.status_code == 404
+
+
+def test_saved_searches_create_validation(client, monkeypatch):
+    monkeypatch.setattr("auth.AUTH_ENABLED", False)
+    response = client.post("/api/saved-searches", json={"name": "  ", "filters": {}})
+    assert response.status_code == 400
+
+
+def test_privacy_filtering_events_by_operation(client, mock_events_collection, monkeypatch):
+    monkeypatch.setattr("config.PRIVACY_SENSITIVE_OPERATIONS", {"MailItemsAccessed", "Send"})
+    monkeypatch.setattr("routes.events.PRIVACY_SENSITIVE_OPERATIONS", {"MailItemsAccessed", "Send"})
+    monkeypatch.setattr("auth.PRIVACY_SERVICE_ROLES", {"SecurityAdmin"})
+    monkeypatch.setattr("auth.user_can_access_privacy_services", lambda claims: False)
+    monkeypatch.setattr("routes.events.user_can_access_privacy_services", lambda claims: False)
+
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-safe",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Exchange",
+            "operation": "Add-MailboxPermission",
+            "result": "success",
+            "actor_display": "Alice",
+            "raw_text": "",
+        }
+    )
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-priv",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Exchange",
+            "operation": "Send",
+            "result": "success",
+            "actor_display": "Bob",
+            "raw_text": "",
+        }
+    )
+
+    response = client.get("/api/events")
+    assert response.status_code == 200
+    data = response.json()
+    ids = [e["id"] for e in data["items"]]
+    assert "evt-safe" in ids
+    assert "evt-priv" not in ids
+
+
+def test_privacy_filter_options_shows_service_hides_ops(client, mock_events_collection, monkeypatch):
+    monkeypatch.setattr("config.PRIVACY_SENSITIVE_OPERATIONS", {"MailItemsAccessed"})
+    monkeypatch.setattr("routes.events.PRIVACY_SENSITIVE_OPERATIONS", {"MailItemsAccessed"})
+    monkeypatch.setattr("auth.PRIVACY_SERVICE_ROLES", {"SecurityAdmin"})
+    monkeypatch.setattr("auth.user_can_access_privacy_services", lambda claims: False)
+    monkeypatch.setattr("routes.events.user_can_access_privacy_services", lambda claims: False)
+
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-1",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Exchange",
+            "operation": "MailItemsAccessed",
+            "result": "success",
+            "actor_display": "Alice",
+            "raw_text": "",
+        }
+    )
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-2",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Exchange",
+            "operation": "Add-MailboxPermission",
+            "result": "success",
+            "actor_display": "Bob",
+            "raw_text": "",
+        }
+    )
+
+    response = client.get("/api/filter-options")
+    assert response.status_code == 200
+    data = response.json()
+    assert "Exchange" in data["services"]
+    assert "MailItemsAccessed" not in data["operations"]
+    assert "Add-MailboxPermission" in data["operations"]
+
+
+def test_privacy_explain_forbidden_by_operation(client, mock_events_collection, monkeypatch):
+    monkeypatch.setattr("config.PRIVACY_SENSITIVE_OPERATIONS", {"Send"})
+    monkeypatch.setattr("routes.ask.PRIVACY_SENSITIVE_OPERATIONS", {"Send"})
+    monkeypatch.setattr("auth.PRIVACY_SERVICE_ROLES", {"SecurityAdmin"})
+    monkeypatch.setattr("auth.user_can_access_privacy_services", lambda claims: False)
+    monkeypatch.setattr("routes.ask.user_can_access_privacy_services", lambda claims: False)
+
+    mock_events_collection.insert_one(
+        {
+            "id": "evt-send",
+            "timestamp": datetime.now(UTC).isoformat(),
+            "service": "Exchange",
+            "operation": "Send",
+            "result": "success",
+            "actor_display": "Bob",
+            "raw_text": "",
+        }
+    )
+    response = client.post("/api/events/evt-send/explain")
+    assert response.status_code == 403
+
+
 def test_health(client):
    response = client.get("/health")
    assert response.status_code == 200
--- a/backend/tests/test_ask.py
+++ b/backend/tests/test_ask.py
@@ -1,5 +1,7 @@
+import asyncio
 from datetime import UTC, datetime, timedelta

+from jobs import set_cached_ask
 from routes.ask import _build_event_query, _extract_entity, _extract_time_range

 # ---------------------------------------------------------------------------
@@ -236,7 +238,7 @@ class TestAskEndpoint:
            }
        )

-        async def fake_llm(question, events):
+        async def fake_llm(question, events, total=None, excluded_services=None):
            return "The device had a failed wipe attempt."

        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
@@ -265,7 +267,7 @@ class TestAskEndpoint:
            }
        )

-        async def failing_llm(question, events):
+        async def failing_llm(question, events, total=None):
            raise RuntimeError("LLM service down")

        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
@@ -277,3 +279,204 @@ class TestAskEndpoint:
        assert data["llm_used"] is False  # Falls back
        assert len(data["events"]) == 1
        assert "Found 1 event" in data["answer"]
+
+    def test_ask_with_explicit_filters(self, client, mock_events_collection):
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-exchange",
+                "timestamp": now.isoformat(),
+                "service": "Exchange",
+                "operation": "Update",
+                "result": "failure",
+                "actor_display": "Alice",
+                "target_displays": ["LAPTOP-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-directory",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Alice",
+                "target_displays": ["LAPTOP-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+        response = client.post(
+            "/api/ask",
+            json={"question": "What happened to LAPTOP-001?", "services": ["Exchange"], "result": "failure"},
+        )
+        assert response.status_code == 200
+        data = response.json()
+        assert data["query_info"]["event_count"] == 1
+        assert data["events"][0]["id"] == "evt-exchange"
+
+    def test_ask_with_explicit_actor_filter(self, client, mock_events_collection):
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-bob",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Bob",
+                "actor_upn": "bob@example.com",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-alice",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Remove user",
+                "result": "success",
+                "actor_display": "Alice",
+                "actor_upn": "alice@example.com",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+        response = client.post("/api/ask", json={"question": "What happened to USER-001?", "actor": "bob"})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["query_info"]["event_count"] == 1
+        assert data["events"][0]["id"] == "evt-bob"
+
+
+class TestAskCaching:
+    def test_ask_cache_hit_returns_cached_answer(self, client, mock_events_collection, monkeypatch):
+        """If the answer is cached, the LLM should not be called."""
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-cache",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Alice",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+
+        llm_called = False
+
+        async def fake_llm(question, events, total=None, excluded_services=None):
+            nonlocal llm_called
+            llm_called = True
+            return "This should NOT appear."
+
+        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
+        monkeypatch.setattr("routes.ask._call_llm", fake_llm)
+
+        # Pre-populate cache with a specific answer
+        class CachingFakeRedis:
+            def __init__(self):
+                self.store = {}
+
+            async def get(self, key):
+                return self.store.get(key)
+
+            async def setex(self, key, ttl, value):
+                self.store[key] = value
+
+        redis = CachingFakeRedis()
+        # Seed cache with the exact filters the endpoint will generate
+        filters_snapshot = {
+            "services": None,
+            "actor": None,
+            "operation": None,
+            "result": None,
+            "start": None,
+            "end": None,
+            "include_tags": None,
+            "exclude_tags": None,
+        }
+        asyncio.run(
+            set_cached_ask(
+                redis,
+                "What happened to USER-001?",
+                filters_snapshot,
+                [{"id": "evt-cache"}],
+                {"answer": "Cached answer!", "llm_used": True, "llm_error": None},
+            )
+        )
+
+        async def fake_get_arq_pool():
+            return redis
+
+        monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
+        response = client.post("/api/ask", json={"question": "What happened to USER-001?"})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["answer"] == "Cached answer!"
+        assert data["llm_used"] is True
+        assert llm_called is False
+
+    def test_ask_async_mode_returns_job_id(self, client, mock_events_collection, monkeypatch):
+        """Async mode should return immediately with a job_id."""
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-async",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Alice",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+
+        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
+
+        # Mock arq pool to capture enqueue_job call
+        class FakeArqPool:
+            def __init__(self):
+                self.enqueued = []
+
+            async def get(self, key):
+                return None
+
+            async def setex(self, key, ttl, value):
+                pass
+
+            async def enqueue_job(self, func, *args, **kwargs):
+                from unittest.mock import MagicMock
+
+                job = MagicMock()
+                job.job_id = "job-12345"
+                self.enqueued.append((func, args, kwargs))
+                return job
+
+        pool = FakeArqPool()
+
+        async def fake_get_arq_pool():
+            return pool
+
+        monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
+        response = client.post("/api/ask", json={"question": "What happened to USER-001?", "async_mode": True})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["job_id"] == "job-12345"
+        assert data["llm_used"] is False
+        assert "being processed" in data["answer"]
+        assert len(pool.enqueued) == 1
+        assert pool.enqueued[0][0] == "process_ask_question"
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,4 +1,19 @@
 services:
+  redis:
+    image: valkey/valkey:8-alpine
+    container_name: aoc-redis
+    restart: always
+    volumes:
+      - redis_data:/data
+    networks:
+      - aoc-internal
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 3s
+      retries: 5
+      start_period: 5s
+
  mongo:
    image: mongo:7
    container_name: aoc-mongo
@@ -27,9 +42,12 @@ services:
      - .env
    environment:
      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:27017/
+      REDIS_URL: redis://redis:6379/0
    depends_on:
      mongo:
        condition: service_healthy
+      redis:
+        condition: service_healthy
    networks:
      - aoc-internal
    healthcheck:
@@ -39,6 +57,24 @@ services:
      retries: 3
      start_period: 10s

+  worker:
+    image: git.cqre.net/cqrenet/aoc-backend:${AOC_VERSION:-latest}
+    container_name: aoc-worker
+    restart: always
+    env_file:
+      - .env
+    environment:
+      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:27017/
+      REDIS_URL: redis://redis:6379/0
+    command: ["arq", "jobs.WorkerSettings"]
+    depends_on:
+      redis:
+        condition: service_healthy
+      mongo:
+        condition: service_healthy
+    networks:
+      - aoc-internal
+
  nginx:
    image: nginx:alpine
    container_name: aoc-nginx
@@ -58,6 +94,7 @@ services:

 volumes:
  mongo_data:
+  redis_data:

 networks:
  aoc-internal:
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,4 +1,13 @@
 services:
+  redis:
+    image: valkey/valkey:8-alpine
+    container_name: aoc-redis
+    restart: always
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis_data:/data
+
  mongo:
    image: mongo:7
    container_name: aoc-mongo
@@ -14,17 +23,34 @@ services:
  backend:
    build: ./backend
    # For production, use the pre-built image instead:
-    # image: git.cqre.net/cqrenet/aoc-backend:v1.1.0
+    # image: git.cqre.net/cqrenet/aoc-backend:v1.2.5
    container_name: aoc-backend
    restart: always
    env_file:
      - .env
    environment:
      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:${MONGO_PORT}/
+      REDIS_URL: redis://redis:6379/0
    depends_on:
      - mongo
+      - redis
    ports:
      - "8000:8000"

+  worker:
+    build: ./backend
+    container_name: aoc-worker
+    restart: always
+    env_file:
+      - .env
+    environment:
+      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:${MONGO_PORT}/
+      REDIS_URL: redis://redis:6379/0
+    command: ["arq", "jobs.WorkerSettings"]
+    depends_on:
+      - redis
+      - mongo
+
 volumes:
  mongo_data:
+  redis_data:
Author	SHA1	Message	Date
Tomas Kracmar	ed7465f5cd	chore: bump version to 1.6.2 All checks were successful Release / build-and-push (push) Successful in 1m33s Details CI / lint-and-test (push) Successful in 33s Details	2026-04-22 11:53:21 +02:00
Tomas Kracmar	0eebcd0765	feat: clickable pills, configurable page size, CQRE.NET branding - Service/category pills are now clickable: click to filter by that service - Result pills (Success, Failure, etc.) are now clickable: click to filter by that result - Click again to clear the filter (toggle behavior) - Change default page size from 100 to 25 - Add DEFAULT_PAGE_SIZE config (env var, default 25), exposed via /api/config/features - Change footer brand from CQRE to CQRE.NET - Add pill--clickable hover styles - Bump CSS cache-buster to v=10	2026-04-22 11:53:01 +02:00
Tomas Kracmar	67f3c28e82	chore: bump version to 1.6.1 All checks were successful CI / lint-and-test (push) Successful in 32s Details Release / build-and-push (push) Successful in 1m30s Details	2026-04-22 11:31:57 +02:00
Tomas Kracmar	04c41ee740	style: UI polish — topbar, footer, user info, product feel - Add sticky top navigation bar with brand, repo/docs links, user chip - Show logged-in user name + email from MSAL account - Add footer with version, issue link, repo link, docs link - Move action buttons (Fetch/Refresh/Login) to compact topbar - Clean up hero section (removed buttons, just title + tagline) - Bump CSS cache-buster to v=9 - Responsive stacking for mobile	2026-04-22 11:31:37 +02:00
Tomas Kracmar	cbd46adaa6	style: ruff format All checks were successful CI / lint-and-test (push) Successful in 25s Details	2026-04-22 10:08:32 +02:00
Tomas Kracmar	e4bafbc4b0	chore: fix ruff import order in test_ask.py Some checks failed CI / lint-and-test (push) Failing after 19s Details	2026-04-22 10:06:07 +02:00
Tomas Kracmar	f75f165911	feat: Redis caching + async queue for LLM scaling (v1.6.0) Some checks failed Release / build-and-push (push) Successful in 1m24s Details CI / lint-and-test (push) Failing after 29s Details - Add async Redis client singleton (redis_client.py) for caching and arq pool - Add arq job functions (jobs.py) for background LLM processing - Cache ask/explain LLM responses with TTL (1h ask, 24h explain) - Add async mode to /api/ask: enqueue job, return job_id, poll /api/jobs/{id} - Add GET /api/jobs/{job_id} endpoint for job status polling - Add arq worker service to docker-compose (dev + prod) - Switch from Redis to Valkey (BSD fork) in Docker Compose - Add REDIS_URL config setting - Add tests for cache hit, async mode, and job status	2026-04-22 09:55:05 +02:00
Tomas Kracmar	47e0dfc2ca	chore: bump version to 1.5.0 All checks were successful CI / lint-and-test (push) Successful in 37s Details Release / build-and-push (push) Successful in 1m51s Details	2026-04-22 08:30:20 +02:00
Tomas Kracmar	2fffe3aec2	feat: operation-level privacy gating instead of broad service-level All checks were successful CI / lint-and-test (push) Successful in 21s Details - Replace broad service-level hiding with fine-grained operation-level gating - PRIVACY_SENSITIVE_OPERATIONS config: hide specific operations across ALL services - PRIVACY_SERVICES still works for broad service-level blocking (optional) - Users without PRIVACY_SERVICE_ROLES: * Don't see sensitive operations in /api/filter-options * Can't query sensitive operations via /api/events or /api/ask * Get 403 on /api/events/{id}/explain for sensitive events - Exchange/Teams services remain visible; only privacy ops are hidden - Update .env.example with new operation-level config docs	2026-04-22 08:23:46 +02:00
Tomas Kracmar	b2f4cabef4	feat: service-level role gating for privacy-sensitive services (Option A) All checks were successful CI / lint-and-test (push) Successful in 25s Details - Add PRIVACY_SERVICES and PRIVACY_SERVICE_ROLES config variables - Add user_can_access_privacy_services(claims) helper in auth.py - /api/events filters out privacy services for users without required roles - /api/filter-options excludes privacy services from dropdown options - /api/ask excludes privacy services from NLQ queries - /api/events/{id}/explain returns 403 for privacy events if unauthorized - Teams added to default noisy service exclusion (frontend + backend) - Update .env.example with privacy config documentation - Add tests for event filtering, filter-options exclusion, and explain 403	2026-04-22 07:26:21 +02:00
Tomas Kracmar	e069869a94	feat: exclude Teams from defaults + GUID resolution in explain All checks were successful CI / lint-and-test (push) Successful in 26s Details - Add Teams to noisy services excluded by default (frontend + backend ask) - Exchange, SharePoint, and Teams now unchecked by default in filters - Enhance explain endpoint with GUID resolution: * Extract UUIDs from raw event JSON recursively * Resolve directory objects via Graph API (user, group, SP, device) * Include resolved names in LLM prompt so explanations reference human-readable names instead of raw GUIDs - Add asyncio import for to_thread wrapper around sync Graph calls	2026-04-22 07:12:10 +02:00
Tomas Kracmar	fb2386e190	feat: saved searches (bookmarks) All checks were successful CI / lint-and-test (push) Successful in 23s Details - Add saved_searches_collection to database.py with index on created_by+created_at - New routes/saved_searches.py: GET /api/saved-searches, POST, DELETE - Saved searches are scoped per user (created_by = token sub) - Mount router in main.py - Frontend: Save filters button, saved search pills with load/delete - loadSavedSearches called on initApp - applySavedSearch restores filters and validates services against current options - Add CSS for saved-searches row - Add tests for CRUD, delete 404, and name validation	2026-04-22 07:04:07 +02:00
Tomas Kracmar	05f5f07e7b	chore: bump version to 1.4.0 All checks were successful CI / lint-and-test (push) Successful in 30s Details Release / build-and-push (push) Successful in 1m24s Details	2026-04-22 06:48:47 +02:00
Tomas Kracmar	681f7d468a	ui: persist filters, default exclude noisy services, true=green All checks were successful CI / lint-and-test (push) Successful in 32s Details - Add 'true' to result pill success keywords (pill--ok) - Persist filter state to localStorage (loadSavedFilters/saveFilters) - Default service selection excludes Exchange and SharePoint - clearFilters resets to default selection (excluding noisy services) - Restore saved services only if they still exist in current options	2026-04-22 06:41:33 +02:00
Tomas Kracmar	fb5d45dfb3	chore: bump version to 1.3.2 All checks were successful CI / lint-and-test (push) Successful in 23s Details Release / build-and-push (push) Successful in 1m38s Details	2026-04-21 22:28:52 +02:00
Tomas Kracmar	658ddd0aac	feat: copy raw event and AI explain in modal All checks were successful CI / lint-and-test (push) Successful in 32s Details - Add POST /api/events/{id}/explain endpoint that fetches event + related events and asks the LLM for a plain-language explanation with security context - Add 'Copy' button to raw event modal (uses navigator.clipboard) - Add 'Explain' button to raw event modal (only when AI_FEATURES_ENABLED) - Show explanation in modal with markdown rendering - Add CSS for modal actions and explanation panel - Add tests for explain endpoint (404, no LLM key, mocked LLM success)	2026-04-21 22:26:26 +02:00
Tomas Kracmar	a5db0d363d	chore: bump version to 1.3.1 All checks were successful Release / build-and-push (push) Successful in 1m16s Details CI / lint-and-test (push) Successful in 25s Details	2026-04-21 11:28:32 +02:00
Tomas Kracmar	43582692ba	ui: fix page title and hero text to match product name All checks were successful CI / lint-and-test (push) Successful in 38s Details	2026-04-21 07:41:41 +02:00
Tomas Kracmar	5122739c01	feat: MCP server over SSE with OIDC auth All checks were successful CI / lint-and-test (push) Successful in 36s Details - Extract shared MCP tool handlers to mcp_common.py - mcp_server.py now uses shared handlers (stdio transport for local dev) - New routes/mcp.py: SSE transport behind existing OIDC Bearer auth - Mount MCP ASGI app at /mcp in main.py when AI_FEATURES_ENABLED - /mcp/sse -> establishes SSE stream (requires valid token when auth enabled) - /mcp/messages/ -> receives MCP client messages - Update README with SSE MCP docs - Add tests for mount existence, auth, and message routing	2026-04-21 07:38:12 +02:00
Tomas Kracmar	6cf5c0a28b	ui: move filters section before ask section All checks were successful CI / lint-and-test (push) Successful in 27s Details	2026-04-20 18:17:09 +02:00
Tomas Kracmar	6aa47e9b1e	docs: update README and ROADMAP for v1.3.0 All checks were successful CI / lint-and-test (push) Successful in 27s Details	2026-04-20 18:14:28 +02:00
Tomas Kracmar	60b6ad15c4	Release v1.3.0: AI feature flag and MCP server All checks were successful CI / lint-and-test (push) Successful in 45s Details Release / build-and-push (push) Successful in 1m34s Details - Add AI_FEATURES_ENABLED config flag to gate AI/natural-language features - Conditionally register /api/ask router based on AI_FEATURES_ENABLED - Add GET /api/config/features endpoint for frontend feature detection - Update frontend to hide Ask panel when AI features are disabled - Implement standalone MCP server (backend/mcp_server.py) with tools: * search_events, get_event, get_summary, ask - Add mcp dependency to requirements.txt - Update .env.example, AGENTS.md, and ROADMAP.md - Bump VERSION to 1.3.0	2026-04-20 18:11:26 +02:00
Tomas Kracmar	b4e504a87b	feat: intent-aware querying + smart sampling for large audit datasets All checks were successful Release / build-and-push (push) Successful in 1m31s Details CI / lint-and-test (push) Successful in 34s Details - Add keyword-based intent extraction: 'device' → Intune, 'user' → Directory, etc. - Broad questions without intent auto-exclude noisy services (Exchange, SharePoint) - Smart stratified sampling: failures always included, high-value services prioritised - Fetch up to 1000 events from MongoDB, then curate best 200 for the LLM - Excluded services noted in LLM prompt and query_info so the admin knows the scope	2026-04-20 17:41:21 +02:00
Tomas Kracmar	b728abb5ee	ci: also tag and push 'latest' on every release All checks were successful CI / lint-and-test (push) Successful in 22s Details	2026-04-20 17:31:27 +02:00
Tomas Kracmar	d100388c7d	chore(release): bump version to 1.2.6 All checks were successful CI / lint-and-test (push) Successful in 31s Details Release / build-and-push (push) Successful in 1m17s Details	2026-04-20 17:29:10 +02:00
Tomas Kracmar	11fd87411d	fix: bake version into Docker image at build time All checks were successful Release / build-and-push (push) Successful in 1m18s Details CI / lint-and-test (push) Successful in 20s Details - Add VERSION build arg to Dockerfile - Pass --build-arg VERSION in release workflow - Remove VERSION env override from docker-compose files - Version is now immutable inside the image, no runtime env var needed	2026-04-20 17:24:20 +02:00
Tomas Kracmar	6a80bf4eb9	fix: read version from env var so it works inside Docker All checks were successful Release / build-and-push (push) Successful in 28s Details CI / lint-and-test (push) Successful in 21s Details	2026-04-20 17:15:55 +02:00
Tomas Kracmar	5e02f5a402	docs: add v1.2.5 release notes All checks were successful CI / lint-and-test (push) Successful in 25s Details	2026-04-20 17:12:43 +02:00
Tomas Kracmar	0c3e5ec57b	feat: add version display to frontend and /api/version endpoint (v1.2.5) All checks were successful Release / build-and-push (push) Successful in 40s Details CI / lint-and-test (push) Successful in 22s Details - Add GET /api/version endpoint that reads VERSION file - Frontend fetches version on init and displays it as a badge in the header - Add version-badge CSS styling - Update docker-compose.yml comment to v1.2.5	2026-04-20 17:09:02 +02:00
Tomas Kracmar	a255be93fe	feat: aggregate large event sets before sending to LLM All checks were successful CI / lint-and-test (push) Successful in 18s Details Release / build-and-push (push) Successful in 29s Details When a query matches >50 events, the LLM now receives: - Aggregated counts by service, operation, result, and actor - A list of failures (up to 10) - The 50 most recent raw events as samples This scales to thousands of events without blowing the token budget or losing signal. The LLM gets a bird's-eye view plus concrete examples. Also updates the system prompt to handle both individual event lists and aggregated overviews correctly.	2026-04-20 16:23:55 +02:00
Tomas Kracmar	cfe9397cc5	feat: raise LLM event limit to 200 and show total count awareness All checks were successful CI / lint-and-test (push) Successful in 23s Details Release / build-and-push (push) Successful in 27s Details - Bump LLM_MAX_EVENTS default from 50 to 200 - Add total_matched count to /api/ask response - Include 'Showing X of Y total' header in LLM prompt so the model knows when its view is a subset and avoids false certainty - Update system prompt to instruct acknowledging scale when truncated - Update test mocks to accept new total parameter	2026-04-20 16:13:52 +02:00
Tomas Kracmar	cf0283b20b	feat: natural language queries respect UI filters (v1.2.0) All checks were successful CI / lint-and-test (push) Successful in 22s Details Release / build-and-push (push) Successful in 36s Details - AskRequest now accepts optional filter fields: services, actor, operation, result, start, end, include_tags, exclude_tags - ask_question merges NL-extracted constraints with explicit UI filters - Frontend sends active filter state with every ask request - Show filter hint below ask input when filters are active - Add tests for service+result filtering and actor filtering in /api/ask Bump version to 1.2.0	2026-04-20 16:07:35 +02:00
Tomas Kracmar	28542f7b80	docs: add v1.1.0 release notes All checks were successful CI / lint-and-test (push) Successful in 27s Details	2026-04-20 16:04:24 +02:00
@@ -1 +1 @@
 .1.0
 .6.2