AI is no longer an IT experiment—it’s an enterprise reality. Your employees are using AI tools (sanctioned or not), your vendors are embedding AI into their products, and your board is asking about AI strategy.
For CISOs, this creates a challenge with no easy answers: How do you govern AI without blocking innovation? How do you protect data without slowing business? How do you maintain compliance when the technology moves faster than regulations?
The stakes are high. According to the 2025 CSA AI Security Report, only about a quarter of organizations have comprehensive AI security governance in place—the remainder rely on partial guidelines or policies still under development. Meanwhile, 100% of organizations plan to incorporate generative AI, and Gartner predicts over 100 million employees will interact with AI by 2026. The gap between AI adoption and AI governance represents real risk.
This checklist provides a structured framework for evaluating and improving your organization’s AI governance maturity.
How to Use This Checklist
For each question, score your organization from 0 (not in place—no capability or process exists), to 1 (partial—some capability exists but gaps remain), to 2 (mature—fully implemented and operational). Add scores within each category to identify strengths and weaknesses.
Category 1: Visibility
Can you see what AI is doing in your organization?
Audit and Logging: Can we audit every agent decision? Do we have centralized logging for all AI interactions, including inputs, outputs, and decisions made? The ability to answer “what did this system do and why” is foundational to everything else in governance.
Complete inventory: Do we have a complete inventory of all AI agents and tools in use—including shadow AI that employees may be using without approval? According to research, 78% of CISOs believe AI is affecting cybersecurity, but 45% admit they’re still not ready to address the problem. You can’t govern what you can’t see.
Data lineage: Can we trace data lineage for any agent interaction? Do we know what data sources each agent accessed and what data it produced? This becomes critical during incidents and audits.
Sensitive data access: Do we know which agents access sensitive data sources? Is there a registry mapping agents to the data they can access? Sensitive data exposure ranks as the leading AI security concern among survey respondents.
Shadow AI detection: Can we detect shadow AI usage—unapproved tools that employees are using? Do we monitor for this actively? Given that most organizations lack formal AI risk management programs, shadow AI often operates completely below radar.
Category 1 Score: ___ / 10
Category 2: Control
Can you control what AI does and who can change it?
Deployment authority: Who can deploy agents? Who can change their prompts? Is there clear ownership and authorization for AI deployments? Without clear authority, agents proliferate without oversight.
Role-based access: Do we have role-based access control (RBAC) for agent capabilities? Can we limit what different agents can do based on sensitivity? Not every agent needs access to every system.
Approval workflows: Is there an approval process for new agents entering production? Do security, legal, and compliance review before deployment? The SANS report highlights a concerning lack of security team involvement in governing GenAI—many believe they should play a role but few organizations have formal processes.
Policy enforcement: Can we enforce policies programmatically—not just through guidelines? Are guardrails built into the infrastructure? Policies that rely solely on human compliance will fail.
Security testing: Do we test agents for security vulnerabilities before deployment? Do we check for prompt injection, jailbreaking, and data leakage risks? According to research, 62% of AI-generated code is either incorrect or contains a security vulnerability.
Category 2 Score: ___ / 10
Category 3: Data
Is sensitive data protected when AI accesses it?
Data source mapping: Which data sources can each agent access? Is there a clear registry of permissions and restrictions? Data access should be explicit, not assumed.
PII protection: Do we have PII detection and masking in place? Can we prevent agents from exposing personally identifiable information? This is table stakes for any customer-facing AI.
Regulatory compliance: Are we compliant with GDPR, CCPA, and other data regulations for AI-processed data? Have we verified this with legal? As of mid-2025, state legislatures had introduced some 260 AI-related bills during the 2025 legislative sessions—the regulatory landscape is rapidly evolving.
Data retention: Do we have data retention policies for agent interactions? Do we know how long logs are kept and when they’re deleted? Compliance requirements vary by jurisdiction and data type.
Right to deletion: Can we fully delete user data on request (right to be forgotten)? Does this include data in AI training sets and logs? This is a legal requirement in many jurisdictions and technically complex to implement.
Category 3 Score: ___ / 10
Category 4: Incident Response
Can you respond when something goes wrong?
Rollback capability: How do we roll back a rogue or compromised agent? Can we quickly revert to a previous version or disable an agent entirely? The faster you can respond, the smaller the impact.
Incident runbooks: Do we have runbooks for common AI incidents—data leaks, hallucinations, prompt injection attacks, model compromise? AI introduces failure modes that traditional security runbooks don’t cover.
Kill switch: Can we disable an agent in less than 5 minutes? Is this tested regularly? When an agent is causing harm, every minute matters.
On-call ownership: Who is on-call for AI security incidents? Is there a clear escalation path and 24/7 coverage? AI systems don’t fail during business hours only.
Post-mortems: Do we conduct post-mortems and share learnings after AI incidents? Is there a continuous improvement process? Learning from incidents prevents repetition.
Category 4 Score: ___ / 10
Category 5: Compliance and Audit
Can you prove compliance to auditors and regulators?
Audit readiness: Can we pass an AI audit today? If regulators asked to see our AI governance, could we demonstrate compliance? The CSA AI Controls Matrix provides 243 control objectives across 18 security domains—a useful benchmark.
Immutable logs: Do we have immutable logs for sensitive operations? Can we prove logs haven’t been tampered with? Immutability is critical for legal and regulatory purposes.
Policy documentation: Are AI governance policies documented and communicated? Do employees know what’s expected? Documentation is the foundation of demonstrable compliance.
Compliance metrics: Do we measure and report Governance Compliance Rate? Can we show the percentage of AI interactions that comply with policies? Metrics make governance tangible.
Board visibility: Is AI governance represented at the board level? Do executives understand AI risk exposure? AI risk is business risk and belongs in board conversations.
Category 5 Score: ___ / 10
Scoring Interpretation
| Total Score | Maturity Level | Recommended Action |
|---|---|---|
| 0-10 | Foundational | Start with visibility: establish inventory and basic logging before adding controls |
| 11-25 | Developing | Fill critical gaps: prioritize based on risk—data protection and incident response are typically highest priority |
| 26-40 | Established | Optimize and scale: strengthen existing capabilities and prepare for audit |
| 41-50 | Advanced | Lead: share practices, influence industry standards, and continue innovation |
Priority Actions by Risk Level
If you’re processing customer PII: Prioritize PII detection and masking, comprehensive logging, RBAC, right to deletion capability, and regulatory compliance verification. Data protection failures have immediate regulatory and reputational consequences.
If you’re in a regulated industry: Prioritize immutable audit logs, policy documentation, compliance metrics, approval workflows, and audit readiness. Key compliance pathways include mappings to the EU AI Act, NIST AI 600-1, ISO 42001, and BSI AIC4 Catalogue.
If you’re scaling AI rapidly: Prioritize complete inventory, shadow AI detection, programmatic policy enforcement, kill switch capability, and incident runbooks. Speed without governance creates technical and compliance debt.
If you’re just starting: Prioritize basic logging, agent inventory, clear ownership, simple approval process, and documentation. Foundation comes before sophistication.
The AI Risk Heatmap
Not all AI use cases carry equal risk. Prioritize governance based on both business value and risk sensitivity—a concept we explore in depth in our AI risk heatmap framework.
High Value, High Risk (Govern Tightly): Customer support agents with PII access, financial data analysis agents, contract review and drafting, and HR policy chatbots need RBAC, PII protection, comprehensive logging, human-in-the-loop review, and regular audits.
High Value, Medium Risk (Govern Moderately): Code assistants and copilots, sales research assistants, and AI meeting note takers need zero data retention agreements, code review requirements, consent mechanisms, and approved vendor lists.
Medium Value, Low Risk (Govern Lightly): Internal knowledge assistants and content drafting tools need basic logging, user feedback loops, and source citation requirements.
Getting Started
If you scored below 25, focus on these immediate actions.
First, conduct an AI inventory. Survey departments, review expense reports, analyze network traffic. You can’t govern what you can’t see, and the gap between what security teams believe is deployed and what’s actually in use is often substantial.
Second, establish basic logging. Ensure all production AI agents have logging enabled. Centralize logs where possible. This creates the audit trail everything else depends on.
Third, define ownership. Assign clear owners for AI governance. Create an AI governance committee if needed. Without ownership, governance becomes everyone’s problem and no one’s priority.
Fourth, document policies. Write down acceptable use guidelines. Communicate them to all employees. Documentation transforms implicit expectations into enforceable standards.
Fifth, plan for incidents. Create basic runbooks for data leaks, hallucinations, and unauthorized access. Incident response planned in advance is dramatically more effective than improvisation under pressure.
For measuring the business impact of your governance investments, see our AI ROI measurement framework.
The Bottom Line
AI governance isn’t about blocking innovation—it’s about enabling it responsibly. The organizations that build strong governance foundations now will scale AI with confidence, while others will hit walls of compliance violations, security incidents, and audit failures.
This checklist is a starting point. The goal isn’t perfection; it’s continuous improvement toward a governance posture that matches your AI ambitions. The Future of Agentic guide to agent characteristics provides additional context on what makes AI systems increasingly autonomous—and why governance becomes more critical as autonomy increases.
Ready to improve your AI governance maturity? Schedule a demo to see how Olakai provides the visibility, controls, and compliance tools CISOs need.