Assessment Templates
"What gets measured gets managed. What gets managed honestly becomes antifragile."
This directory contains diagnostic tools, maturity models, and assessment resources for evaluating organizational antifragility.
Production-Ready Templates
| Template | Purpose |
|---|---|
| Engagement Checklist | Point-in-time, regularly updated. Controls to inspect on every M365+AD engagement, organized by domain. Not scored — a structured inspection list. Review January 2027. |
| Adversarial Validation Checklist | Phase 2 — mature estates. Every item is a test, not an inspection. Opening/closing metrics, eight detection simulations, CA ghost policy tests, attack path verification. Review January 2027. |
| Self-Service Cadence | Client leave-behind. Monthly portal checks and quarterly tool runs (PingCastle, Purple Knight, CAExporter, PowerShell scripts) an admin can run between engagements. Includes "call us" triggers. Customise per client before handing over. |
| Assessment Team Guide | Technical execution guide for the Brownhat Diagnostic: tool sequence (ASTRAL, PULSAR, BloodHound, Elysium, Purple Knight, CAExporter), what to look for, kill chain synthesis, report structure, common mistakes. |
| Findings Backlog | Single source of truth for all findings across every module and diagnostic. The input queue for the housekeeping stream. Pragmatic alternative to a formal risk register for organisations that do not have one. |
| NIST CSF 2.0 Baseline Assessment | The Brownhat Diagnostic: structured 2-half-day workshop, gap analysis, kill chain identification |
| Module Completion Report | Completion package template for every module; includes backlog update |
| Antifragile Risk Register | Formal risk register template; the backlog feeds into this for organisations with mature risk management |
| Risk Register Example | 8 fully populated entries from a realistic engagement — calibration reference |
| M365 Project Risk Register | M365-specific risk register with phase gates |
Planned Assessments
1. Antifragile Maturity Model (AF-MM)
A five-level maturity model covering:
- Level 1: Fragile — Reactive, undocumented, dependent on single vendors
- Level 2: Robust — Documented, monitored, but static
- Level 3: Resilient — Automated recovery, tested backups, incident response operational
- Level 4: Adaptive — Chaos engineering, continuous learning, structural improvement from failure
- Level 5: Antifragile — Volatility is exploited for gain, optionality is strategic, intelligence is sovereign
2. AI Sovereignty Readiness Assessment
Evaluates:
- Current AI usage inventory completeness
- Data classification and leakage risk
- Local infrastructure readiness
- Vendor dependency and exit feasibility
- Regulatory compliance posture
3. T0 Asset Discovery Scanner
Planned scripted assessment to:
- Enumerate critical assets across on-premises and cloud environments
- Classify assets by tier based on dependency mapping
- Identify gaps in protection, monitoring, and recovery
- Generate prioritized remediation roadmap
4. Dependency Risk Mapper
Planned tool to:
- Map vendor and technology dependencies
- Calculate coupling depth and exit difficulty
- Identify hidden single points of failure
- Simulate failure cascades
5. Incident Learning Index
Measures the organization's ability to convert incidents into structural improvements:
- Mean time to structural fix
- Post-mortem completion rate
- Structural changes implemented per incident
- Repeat incident rate
Development Roadmap
Phases are sequenced by client impact, not calendar quarter. Dates are assigned at the start of each development cycle.
| Phase | Deliverable | Format | Status |
|---|---|---|---|
| 1 | AF-MM v1.0 — Antifragile Maturity Model questionnaire and scoring guide | Markdown + spreadsheet | Planned |
| 2 | AI Sovereignty Readiness Assessment v1.0 | Interactive web form or CLI tool | Planned |
| 3 | T0 Asset Discovery Scanner v0.1 — cloud APIs + on-premises enumeration | Python script | Planned |
| 4 | Dependency Risk Mapper v0.1 — vendor coupling depth and failure cascade simulation | Python + network analysis | Planned |
Contributing
When adding new assessments:
- Document the purpose, methodology, and limitations
- Include scoring rubrics with clear criteria
- Provide sample outputs and interpretation guidance
- Version assessments and maintain changelogs
- Test on at least two different organizational profiles before release
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