Boards are asking a pressing question: “We have policies for AI, but will they stand up as evidence when regulators or auditors demand proof?” The gap between stated policies and verifiable evidence is one of the most consistent weaknesses in organizational AI governance today.

AuditBoard’s 2024 Blueprint to Reality survey showed that while 75 percent of organizations have drafted AI-related policies, only 25 percent have implemented programs that generate consistent, regulator-ready evidence (AuditBoard, 2024). PwC’s 2024 Responsible AI Survey similarly found that while most companies report having ethical guidelines, fewer than half have operationalized controls such as bias testing, incident logging, or monitoring mechanisms (PwC, 2024). The Financial Reporting Council’s 2023 review of technology in audits adds another perspective, observing that firms increasingly deploy AI but rarely document its impact on audit quality in a structured way (Financial Reporting Council, 2023).

The evidence gap matters because regulators have shifted from evaluating intentions to examining practices. The EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework all emphasize demonstrable proof of governance. The OECD has likewise noted that principles without evidence erode trust and create accountability failures (OECD, 2023). In this environment, policies alone provide little defense.

The root causes of the gap are structural. Policies often reside in compliance or legal departments, while evidence generation depends on engineering, operations, and audit teams. Without integration, documentation remains fragmented. Furthermore, organizations tend to focus on drafting policies in anticipation of regulatory deadlines but fail to embed the workflows that would generate evidence continuously.

Closing the evidence gap requires deliberate design. The first step is to establish live risk registers that link identified risks with corresponding artifacts such as test results or mitigation actions. Second, incident escalation drills should be conducted regularly, ensuring that incidents are documented and logged into governance systems rather than handled informally. Third, monitoring processes should be automated where possible to generate time-stamped logs that can be integrated into evidence libraries. Fourth, organizations should conduct internal reviews simulating regulator queries, asking not “what policies exist?” but “what proof can we provide today?”

Evidence is not merely a compliance requirement. It is a governance practice that provides confidence to boards, reassurance to regulators, and trust to the public. The organizations that will succeed are those that shift from policies on paper to evidence in action.

Recommended Reading

• Link “risk registers” → https://mercurysecurity.io/ai-audit-ready/
• Link “incident escalation drills” → https://mercurysecurity.io/incident-escalation-playbook/
• EU AI Act portal (https://artificialintelligenceact.eu/)
• OECD AI system framework (https://oecd.ai)

References

AuditBoard. (2024). From blueprint to reality: The state of AI governance programs. AuditBoard. https://auditboard.com/blog/new-research-finds-only-25-percent-of-organizations-report-a-fully-implemented-ai-governance-program

Financial Reporting Council. (2023). The use of technology in the audit of financial statements. FRC. https://www.frc.org.uk

Organisation for Economic Co-operation and Development. (2023). OECD framework for the classification of AI systems. OECD Publishing. https://oecd.ai

PwC. (2024). Responsible AI survey: How US companies are managing risk in an era of generative AI. PwC. https://www.pwc.com/us/en/tech-effect/ai-analytics/responsible-ai-survey.html