Governance Guide
AI Audit Framework (2026) - Enterprise Compliance Checklist
Enterprise AI systems require regular audits to maintain compliance, security, and operational integrity. This framework structures audit checkpoints, evidence requirements, and remediation workflows.
Direct answer
Enterprise AI systems require regular audits to maintain compliance, security, and operational integrity. This framework structures audit checkpoints, evidence requirements, and remediation workflows.
Fast path
- Define audit scope: model inventory, data flows, access controls, and policy compliance.
- Establish evidence collection requirements for each control checkpoint.
- Set audit frequency by risk tier: quarterly for high-risk, annually for standard systems.
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Implementation Steps
- Define audit scope: model inventory, data flows, access controls, and policy compliance.
- Establish evidence collection requirements for each control checkpoint.
- Set audit frequency by risk tier: quarterly for high-risk, annually for standard systems.
- Create remediation tracking with owner assignment and SLA deadlines.
Frequently Asked Questions
What should an AI audit framework include?
An AI audit framework should include model inventory verification, data flow mapping, access control testing, policy compliance checks, evidence collection protocols, and remediation tracking workflows.
How often should enterprise AI systems be audited?
High-risk AI systems (handling sensitive data or critical decisions) should be audited quarterly. Standard systems can be audited annually. New deployments require initial audit within 30 days.
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