AI incident response

Road to audit readiness

Conducting a gap analysis Implementation of AI lifecycle controls Risk analysis and impact assessment AI incident response Audit criteria Getting through an internal audit

AI incident response refers to the structured processes and practices for detecting, managing, and recovering from incidents involving artificial intelligence systems. Unlike traditional cybersecurity incidents (for example, data breaches), AI incidents often present unique challenges, such as algorithmic bias manifesting in production, model hallucinations leading to harmful outputs, adversarial attacks, data poisoning, or unintended societal impacts, including discrimination or misinformation. While the core clauses (for example, Clauses 9–10 on performance evaluation and improvement) require monitoring, nonconformity handling, and continual improvement, specific AI-focused controls are in Annex A:

A.8.3: Requires a documented process for interested parties (for example, users, affected individuals) to report adverse impacts from AI systems, akin to whistleblower mechanisms but tailored to AI harms like bias or privacy violations.
A.8.4: Mandates timely and auditable communication of incidents to relevant stakeholders, ensuring transparency and compliance with legal obligations.
A.8.5: Focuses on documenting and fulfilling reporting obligations to external parties, aligning with regulations like the EU AI Act’s incident reporting requirements.

Some key components of an effective AI incident response plan are:

Preparation: Assemble a cross-functional team (AI experts, legal, ethics, IT security, communications). Define what constitutes an AI incident (for example, bias amplification, safety failures, ethical violations). Conduct tabletop exercises simulating AI-specific scenarios.
Detection and identification: Implement continuous monitoring for anomalies (for example, performance drift, unusual outputs). Use logging for explainability and provenance tracking.
Containment and mitigation: Pause model deployment, rollback to previous versions, or disable features. Contain harm (for example, notify affected users, mitigate ongoing bias).
Eradication and Recovery: Root cause analysis, often involving retraining or patching models. Restore systems while preserving evidence for audits.
Post-Incident Review and Learning: Document lessons learned, update policies, and report as required. Share anonymized insights internally or with industry databases (for example, AI Incident Database).
Communication: Transparent internal and external reporting, balancing confidentiality with accountability.

Audit criteria

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