Evidence Libraries: The Foundation of AI Governance

Boards are asking a simple but high-stakes question: “If regulators arrive tomorrow, can we produce evidence that stands up?” For most organizations, the honest answer is no. Policies may exist, and risk discussions may be underway, but the evidence required to satisfy regulatory, or auditor scrutiny is scattered, inconsistent, or missing altogether.

AuditBoard’s 2024 Blueprint to Reality report found that while awareness of AI governance requirements is high, only a minority of organizations have consolidated evidence into defensible programs (AuditBoard, 2024). PwC’s 2024 Responsible AI Survey showed that most companies have developed responsible AI principles, but fewer than half have operationalized evidence collection at the enterprise level (PwC, 2024). NIST’s AI Risk Management Framework reinforces the point: evidence of mapping, measuring, and managing risks is required for credibility, but most organizations remain at the policy stage (NIST, 2023).

The absence of structured evidence libraries creates predictable problems. Regulators increasingly expect system documentation, bias test results, incident logs, and escalation records to be readily available. Without centralized repositories, organizations scramble to gather artifacts during audits, often discovering gaps too late. The Financial Reporting Council’s 2023 review of AI in financial audits noted that firms using AI tools were not systematically capturing evidence of their impact on audit quality (Financial Reporting Council, 2023). This highlights that evidence cannot be improvised at the moment of scrutiny.

Building evidence libraries addresses these challenges. At its core, an evidence library is a structured repository of artifacts that are continuously updated and mapped to regulatory frameworks. Model cards, fairness metrics, system logs, and escalation playbooks become reusable across audits, reducing duplication and strengthening consistency. Deloitte has emphasized that model documentation and structured evidence packs are emerging as best practices for enterprises seeking to scale AI responsibly (Deloitte, 2023).

A layered approach is essential. First, evidence must be comprehensive, capturing not only technical performance but also socio-technical impacts such as bias, fairness, and human oversight. Second, evidence must be portable, available in machine-readable formats for auditors and human-readable reports for executives. Third, evidence must be mapped to overlapping frameworks, ensuring that a single artifact can satisfy requirements under the EU AI Act, ISO/IEC 42001, and the NIST AI RMF simultaneously. Fourth, evidence libraries must be dynamic. Static repositories degrade over time; continuous updates and governance rehearsals keep libraries reliable.

The benefits extend beyond regulatory readiness. Evidence libraries allow organizations to learn from their own history, identifying recurring risks, gaps in oversight, and areas where governance processes require reinforcement. They transform governance from a reactive scramble to a proactive system of continuous assurance.

Boards are right to insist on evidence. Without it, governance remains rhetoric. With it, organizations can withstand scrutiny, protect citizens from harm, and build trust that AI systems are accountable. Evidence libraries are therefore not an optional best practice but a foundational requirement for AI governance.

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