AI Governance Maturity Assessment Tool (AGMAT v1.0)
Institutional Self-Assessment Framework for Constitutional AI Governance
1. Purpose
The AI Governance Maturity Assessment Tool (AGMAT) enables organizations to:
- Measure the maturity of their AI governance architecture
- Identify structural gaps
- Prioritize remediation investments
- Benchmark against best practices
- Prepare for regulatory certification
- Support Board-level reporting
This tool is suitable for:
- Multinational AI enterprises
- Hybrid AI (HGAI) research initiatives
- Sovereign AI programs
- Critical infrastructure AI operators
- High-impact AI startups scaling rapidly
2. Maturity Model Overview
The model evaluates governance across 10 Core Domains, each scored from Level 0 to Level 5.
Governance Maturity Levels
| Level | Description |
|---|---|
| 0 | No formal governance |
| 1 | Policy documentation exists |
| 2 | Partial operationalization |
| 3 | Embedded controls implemented |
| 4 | Fully integrated and monitored |
| 5 | Institutionalized, audited, adaptive |
3. Core Assessment Domains
- Board Oversight & Institutional Structure
- Risk Classification & Tiering
- Constitutional Constraints Embedded in Code
- Safety Engineering & Red Teaming
- Auditability & Traceability
- Data Governance & Privacy Controls
- Hybrid AI (HGAI) Safeguards (if applicable)
- Incident Response & Rollback Readiness
- Regulatory & Jurisdictional Compliance
- Continuous Monitoring & Drift Management
4. Domain Assessment Criteria
DOMAIN 1 — Board Oversight & Institutional Structure
Level Indicators
Level 0
No board involvement in AI oversight.
Level 1
Board receives informal AI updates.
Level 2
AI oversight committee established.
Level 3
Formal AI governance charter approved; Tier 3–4 deployments require board sign-off.
Level 4
Quarterly AI risk dashboards reviewed; external audit integrated.
Level 5
Independent constitutional AI oversight structure with escalation authority and audit rotation.
Key Evaluation Questions
- Is AI risk part of enterprise risk management?
- Does the Board approve high-risk deployments?
- Are oversight members technically qualified?
- Is there documented escalation authority?
Score: ___ / 5
DOMAIN 2 — Risk Classification & Tiering
Evaluate whether AI systems are formally classified by impact.
Level Indicators
0 — No classification
1 — Informal risk tagging
2 — Internal risk model used inconsistently
3 — Mandatory tiering for all deployments
4 — Tier-based certification workflow enforced
5 — Dynamic risk reclassification and external validation
Questions:
- Is tier assignment mandatory?
- Are thresholds defined?
- Is tier escalation logged?
- Are Tier 3–4 systems externally audited?
Score: ___ / 5
DOMAIN 3 — Constitutional Constraints in Code
Assess whether ethical principles are executable.
Level Indicators
0 — Ethics are documentation only
1 — Policy statements exist
2 — Guardrails implemented but bypassable
3 — Runtime constraint layer active
4 — Constraint enforcement independently tested
5 — Immutable constitutional constraint layer with version control
Questions:
- Are prohibited actions technically blocked?
- Is refusal logic audited?
- Can optimization override constraints?
- Is constraint logic version-controlled?
Score: ___ / 5
DOMAIN 4 — Safety Engineering & Red Teaming
Level Indicators
0 — No adversarial testing
1 — Occasional internal testing
2 — Structured red-team exercises
3 — Mandatory red-team certification before release
4 — Independent external adversarial audit
5 — Continuous red-team simulation pipeline
Questions:
- Is jailbreak resistance measured?
- Are tool exploits tested?
- Is model drift tested adversarially?
- Is certification required before release?
Score: ___ / 5
DOMAIN 5 — Auditability & Traceability
Level Indicators
0 — Minimal logging
1 — Logs exist but incomplete
2 — Structured logging for high-risk systems
3 — Immutable logs with version tracking
4 — Board dashboard access
5 — Cryptographically verifiable audit trails + independent review
Questions:
- Are model versions tracked?
- Are risk scores logged?
- Is human escalation traceable?
- Are logs tamper-resistant?
Score: ___ / 5
DOMAIN 6 — Data Governance & Privacy Controls
Level Indicators
0 — No formal data governance
1 — Basic data policy
2 — Sensitive data classification exists
3 — Data minimization + purpose limitation enforced
4 — Regular privacy audits
5 — Automated data lifecycle enforcement + cross-border harmonization
Questions:
- Is sensitive data segregated?
- Are consent mechanisms auditable?
- Are deletion requests enforceable?
- Is training data provenance tracked?
Score: ___ / 5
DOMAIN 7 — Hybrid AI (HGAI) Safeguards
(If not applicable, mark N/A)
Level Indicators
0 — No special controls
1 — Consent documentation only
2 — Basic neurodata encryption
3 — Consent token enforcement in runtime
4 — Dependency & influence monitoring
5 — Real-time revocation + cognitive autonomy analytics
Questions:
- Is neurodata classified ultra-sensitive?
- Is consent revocable instantly?
- Is psychological influence monitored?
- Is dependency risk quantified?
Score: ___ / 5
DOMAIN 8 — Incident Response & Rollback Readiness
Level Indicators
0 — No formal response protocol
1 — Informal containment plan
2 — Structured incident classification
3 — Kill-switch implemented
4 — Quarterly rollback drills
5 — Full simulation + board-level crisis rehearsal
Questions:
- Is rollback tested?
- Are escalation levels defined?
- Is public reporting protocol established?
- Is containment time measured?
Score: ___ / 5
DOMAIN 9 — Regulatory & Jurisdictional Compliance
Level Indicators
0 — Reactive compliance
1 — Legal consultation only
2 — Compliance mapping for major markets
3 — Cross-jurisdiction harmonization
4 — Regulatory simulation testing
5 — Proactive policy participation + external certification
Questions:
- Is EU AI Act mapping complete?
- Are US state laws monitored?
- Is export control evaluated?
- Is highest-standard default applied globally?
Score: ___ / 5
DOMAIN 10 — Continuous Monitoring & Drift Management
Level Indicators
0 — No monitoring
1 — Basic performance monitoring
2 — Safety metrics tracked
3 — Automated drift detection
4 — Threshold-triggered escalation
5 — Predictive drift modeling + autonomous mitigation
Questions:
- Is safety degradation measured?
- Is bias drift tracked?
- Is hallucination rate monitored?
- Is there automatic freeze on threshold breach?
Score: ___ / 5
5. Scoring Model
Each domain scored 0–5.
Maximum score: 50
Maturity Classification
| Total Score | Classification |
|---|---|
| 0–10 | Foundational Risk Exposure |
| 11–20 | Basic Governance Emerging |
| 21–30 | Structured but Vulnerable |
| 31–40 | Embedded Governance |
| 41–45 | Advanced Institutional Governance |
| 46–50 | Constitutional-Grade AI Governance |
6. Risk Exposure Overlay
In addition to score, evaluate:
- Any Level 0 in Tier ≥ 3 systems → Immediate Board review required.
- Any Level < 3 in Constitutional Constraints → Critical governance gap.
- Any Level < 3 in Incident Response → Operational instability risk.
7. Board Reporting Template
Quarterly Board Report Should Include:
- Total maturity score
- Domain heatmap
- Drift index
- Red-team pass rate
- Incident count
- Rollback readiness score
- Regulatory change map
- Remediation roadmap
8. Remediation Roadmap Guidance
If Domain Score ≤ 2:
- Define ownership
- Allocate budget
- Implement structural control
- Schedule independent validation
- Report to Board within 90 days
9. Governance Stress-Test Module (Optional Advanced)
Conduct simulated catastrophic scenarios:
- Mass misinformation exploit
- Infrastructure misuse
- Model jailbreak escalation
- Data breach
- Autonomous misalignment cascade
Score resilience on:
- Detection time
- Containment time
- Decision clarity
- Documentation completeness
- Public communication readiness
10. Final Executive Interpretation
High maturity does not mean zero risk.
It means risk is:
- Measured
- Controlled
- Escalatable
- Reversible
- Defensible
The objective is not perfection.
It is structural resilience.
