AI Governance Readiness Assessment

Section 1 of 8

Governance Framework & Policy

Foundational policies and frameworks that guide AI decision-making and accountability.

1. Does your organization have documented policies specifically for autonomous AI systems?

Consider policies covering deployment criteria, approval workflows, and accountability structures.

No formal policies exist 0 pts Policies are in draft or under development 1 pt Basic policies exist but aren't specific to autonomous systems 2 pts Comprehensive, AI-specific policies are documented and approved 3 pts Policies are comprehensive, actively enforced, and regularly updated 4 pts

2. Are there clear accountability structures defining who is responsible when AI systems make decisions?

Think about decision ownership, escalation paths, and liability frameworks.

No clear accountability defined 0 pts General ownership but not specific to AI decisions 1 pt Accountability defined for some AI systems 2 pts Clear accountability for all AI systems with documented roles 3 pts Full accountability chain with legal review and enforcement mechanisms 4 pts

3. How does your organization define and communicate acceptable AI behavior?

Consider whether you have explicit intent definitions, constraints, and behavioral boundaries.

No formal definition of acceptable behavior 0 pts Informal or implied expectations 1 pt High-level guidelines documented 2 pts Detailed behavioral constraints and intent definitions 3 pts Machine-readable intent specifications enforced at runtime 4 pts

Section 2 of 8

Runtime Monitoring & Observability

Your ability to observe and understand AI system behavior as it's happening.

4. Can you observe your AI agents' reasoning process in real-time?

Think about visibility into chain-of-thought, decision steps, and intermediate reasoning.

No visibility into reasoning—black box operation 0 pts Can view outputs only, no reasoning visibility 1 pt Limited logging of some decision steps 2 pts Comprehensive logging of reasoning with some real-time visibility 3 pts Full real-time reasoning transparency with semantic analysis 4 pts

5. Do you track when AI decisions deviate from intended behavior or constraints?

Consider deviation detection, drift monitoring, and anomaly identification.

No deviation tracking capability 0 pts Manual review after-the-fact only 1 pt Basic automated detection of obvious errors 2 pts Systematic deviation detection with alerting 3 pts Real-time semantic deviation analysis against intent specifications 4 pts

6. How quickly can your team identify when an AI agent is behaving unexpectedly?

Think about time-to-detection for anomalous behavior or policy violations.

Only discovered through user complaints or incidents 0 pts Detected in periodic reviews (days/weeks later) 1 pt Within hours through automated monitoring 2 pts Within minutes through real-time dashboards 3 pts Instant detection with automated analysis and categorization 4 pts

Section 3 of 8

Intervention Capabilities

Your ability to prevent, modify, or halt AI actions before they execute.

7. Can you prevent AI agents from taking actions before they execute?

Consider pre-execution review, approval gates, and intervention mechanisms.

No intervention capability—actions execute automatically 0 pts Can only stop system entirely (kill switch) 1 pt Manual review possible for flagged actions 2 pts Automated intervention with human oversight for high-risk actions 3 pts Graded intervention (allow/warn/escalate/block) based on risk assessment 4 pts

8. Are intervention decisions proportional to the level of risk?

Think about whether you can apply different levels of control based on action severity.

No risk-based intervention—all or nothing 0 pts Manual assessment of risk after the fact 1 pt Basic categorization (high/low risk) 2 pts Multi-level risk assessment with corresponding interventions 3 pts Dynamic, context-aware risk scoring with automated proportional response 4 pts

9. How quickly can interventions be applied when needed?

Consider the time between detecting an issue and preventing the problematic action.

Cannot intervene before action completes 0 pts Manual intervention requires hours or days 1 pt Semi-automated with minutes of delay 2 pts Automated intervention within seconds 3 pts Real-time intervention before action execution 4 pts

Section 4 of 8

Audit Trail & Evidence

Quality and completeness of records for AI decision-making and actions.

10. How complete is your audit trail for AI decisions and actions?

Consider what gets recorded: inputs, reasoning, outputs, context, and outcomes.

Minimal or no logging of AI activity 0 pts Basic logging of inputs and outputs only 1 pt Logging includes reasoning steps but incomplete 2 pts Comprehensive logging of decision chain with context 3 pts Complete, timestamped, cryptographically-verified audit trail 4 pts

11. Can you demonstrate compliance with regulatory requirements (e.g., EU AI Act)?

Think about evidence quality, traceability, and ability to prove human oversight.

Cannot demonstrate compliance—insufficient evidence 0 pts Documentation exists but not audit-ready 1 pt Can demonstrate basic compliance with gaps 2 pts Strong compliance posture with documented evidence 3 pts Audit-grade evidence automatically generated and maintained 4 pts

12. Can you reconstruct what happened and why for any AI decision?

Consider your ability to replay, explain, and justify past AI actions.

Cannot reconstruct past decisions 0 pts Partial reconstruction possible with significant manual effort 1 pt Can reconstruct major decisions with some gaps 2 pts Full reconstruction possible for all decisions 3 pts Automated reconstruction with full causal chain and justification 4 pts

Section 5 of 8

Compliance & Risk Management

Your readiness for regulatory requirements and risk mitigation strategies.

13. How well do you understand the regulatory landscape for your AI systems?

Consider EU AI Act, sector-specific regulations, and emerging requirements.

Limited awareness of relevant regulations 0 pts General awareness but no formal assessment 1 pt Documented understanding with gap analysis 2 pts Comprehensive compliance program with legal review 3 pts Proactive compliance with continuous monitoring of regulatory changes 4 pts

14. Have you conducted risk assessments for your AI systems?

Think about systematic evaluation of potential harms, failures, and consequences.

No formal risk assessment conducted 0 pts Informal or ad-hoc risk considerations 1 pt Basic risk assessment for major systems 2 pts Comprehensive risk assessments with mitigation plans 3 pts Continuous risk assessment with automated monitoring and updates 4 pts

15. How do you manage AI-related liability and insurance?

Consider insurance coverage, liability frameworks, and incident response plans.

No specific AI liability considerations 0 pts General liability coverage, not AI-specific 1 pt AI risks identified in insurance review 2 pts AI-specific insurance with incident response procedures 3 pts Comprehensive liability framework with proven incident handling 4 pts

Section 6 of 8

Human Oversight & Escalation

Mechanisms ensuring humans remain meaningfully in control of AI systems.

16. How do humans stay involved in high-stakes AI decisions?

Consider human-in-the-loop mechanisms, review processes, and approval workflows.

Fully autonomous—no human involvement 0 pts Humans review periodically after decisions made 1 pt Manual review for flagged decisions 2 pts Required human approval for high-risk actions 3 pts Risk-proportional human oversight with clear escalation paths 4 pts

17. Are escalation procedures clearly defined and tested?

Think about when decisions escalate, to whom, and how quickly.

No formal escalation procedures 0 pts Informal escalation paths exist 1 pt Documented procedures but not regularly tested 2 pts Well-defined procedures tested quarterly 3 pts Automated escalation with proven response times and clear SLAs 4 pts

18. Can human operators effectively understand and override AI decisions?

Consider whether operators have sufficient context, tools, and authority.

Cannot override or lack necessary context 0 pts Override possible but requires deep technical knowledge 1 pt Basic override capability with limited context 2 pts Clear override mechanisms with decision context provided 3 pts Intuitive interfaces with full reasoning transparency and one-click override 4 pts

Section 7 of 8

Technical Integration & Infrastructure

Your technical capability to implement and maintain governance systems.

19. How easily can governance controls integrate with your current AI stack?

Consider API availability, instrumentation points, and architectural compatibility.

Black box systems—no integration points 0 pts Would require significant architectural changes 1 pt Some integration points exist but limited 2 pts Good API coverage with standard interfaces 3 pts Fully instrumented with plug-and-play governance layer capability 4 pts

20. Do you have infrastructure for handling governance at scale?

Think about performance, latency, throughput, and reliability requirements.

No governance infrastructure considerations 0 pts Manual processes that don't scale 1 pt Basic automation but performance concerns 2 pts Scalable infrastructure with acceptable performance 3 pts Production-grade infrastructure with sub-second latency and high availability 4 pts

21. Is your governance approach model-agnostic?

Consider whether governance works across different AI models, vendors, and frameworks.

Tightly coupled to specific models/vendors 0 pts Works with one model family only 1 pt Supports multiple models with custom integrations 2 pts Standardized approach across most models 3 pts Fully model-agnostic governance layer (works with any LLM/agent framework) 4 pts

Section 8 of 8

Documentation & Organizational Readiness

Documentation quality and team preparedness for AI governance.

22. How well-documented are your AI systems and governance processes?

Consider technical documentation, process guides, and training materials.

Minimal or no documentation 0 pts Scattered documentation, often outdated 1 pt Basic documentation exists but incomplete 2 pts Comprehensive documentation, regularly updated 3 pts Living documentation auto-generated from system state and continuously maintained 4 pts

23. Are teams trained on AI governance responsibilities and procedures?

Think about training programs, role clarity, and ongoing education.

No formal training on AI governance 0 pts Ad-hoc knowledge sharing only 1 pt One-time training for key personnel 2 pts Regular training programs for all relevant teams 3 pts Comprehensive, role-based training with certifications and continuous education 4 pts

24. Does your organization have dedicated governance resources and budget?

Consider staffing, tools, and financial commitment to AI governance.

No dedicated resources or budget 0 pts Part-time attention from existing staff 1 pt Allocated budget but limited dedicated staff 2 pts Dedicated team with adequate resources 3 pts Well-funded program with executive sponsorship and cross-functional team 4 pts

AI Governance Readiness Assessment

Let's begin with the Assessment

Governance Framework & Policy

Runtime Monitoring & Observability

Intervention Capabilities

Audit Trail & Evidence

Compliance & Risk Management

Human Oversight & Escalation

Technical Integration & Infrastructure

Documentation & Organizational Readiness

Your AI Governance Readiness Score

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