Claude Sonnet 4.6
3062e435ca
AOC -> PULSAR across 10 files (engagement-model, retained-capability, modular-engagements, blue-purple-team-foundation, about-cqre, about-cqre-cs, consultant-field-guide, ai-assisted-tvm, m365-e3-hardening, sovereign-tool-stack, risk-register-example). Training-data framing corrected in: - executive-summary.md: opening paragraph and risk table - README.md: 90% solution claim -> 30-60% in 180 days - modular-engagements.md: public API data use claim - cis-controls-mapping.md: data protection framing - antifragile-risk-register.md: risk entry softened to accurate framing - azure-openai-sovereignty-bridge.md: consumer vs enterprise API distinction Co-Authored-By: Tom Kracmar <tom+claude@cat6.cz>
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. Two production-ready tools are available now; additional assessments are in active development.
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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