Classification
AI Risk Management & Compliance
Overview
Record-keeping and audits are foundational practices in AI governance, ensuring transparency, accountability, and traceability of AI system development and deployment. These practices require organizations to systematically document system design decisions, data provenance, model training processes, risk assessments, and operational logs. Audits-internal or external-are then used to verify compliance with legal, ethical, and technical requirements, and to detect gaps or failures in controls. Effective record-keeping enables organizations to respond to regulatory inquiries, facilitate incident investigations, and demonstrate due diligence. However, limitations exist: maintaining comprehensive records can be resource-intensive, and audit processes may struggle to keep pace with rapid AI system updates, potentially leaving compliance gaps. Furthermore, over-reliance on documentation may obscure deeper systemic issues if not paired with substantive oversight.
Governance Context
Record-keeping and audit obligations are explicitly mandated by several regulatory frameworks. For example, the EU AI Act (Article 18) requires providers of high-risk AI systems to keep detailed documentation, including system design, testing, and risk management measures, for at least ten years after the system is placed on the market. Similarly, the NIST AI Risk Management Framework (RMF) emphasizes ongoing documentation and independent audits as part of its 'Govern' and 'Map' functions. Organizations must also establish data retention policies and ensure audit trails are tamper-evident, as seen in ISO/IEC 42001:2023 requirements. Two concrete obligations include: (1) maintaining technical documentation and operational logs for a specified retention period (e.g., ten years under the EU AI Act), and (2) conducting regular, independent audits to verify compliance and assess risk mitigation effectiveness. These controls help regulators and stakeholders verify compliance, investigate incidents, and assess the effectiveness of risk mitigation strategies.
Ethical & Societal Implications
Robust record-keeping and audit mechanisms enhance public trust by enabling transparency and accountability in AI systems. They help ensure that decisions can be explained and challenged, supporting fairness and due process. However, excessive documentation may burden smaller organizations and raise privacy concerns if sensitive data is over-retained. There is also a risk that audits become a box-ticking exercise, failing to address deeper ethical issues if not conducted with rigor and independence. Additionally, poorly managed audit processes may inadvertently expose proprietary or personal information, raising further ethical considerations.
Key Takeaways
Record-keeping and audits are core to AI transparency and accountability.; Regulatory frameworks like the EU AI Act and NIST RMF mandate these practices.; Effective audits depend on comprehensive, accurate, and accessible documentation.; Limitations include resource demands and the risk of superficial compliance.; Failure modes may arise from incomplete records or inadequate audit processes.; Independent audits and tamper-evident logs are essential for trustworthy AI operations.; Record-keeping supports incident response and regulatory investigations.