Insights
How to Audit AI Agents
A practical audit guide to AI agents covering tools, permissions, approvals, memory, logs, exceptions, human takeover, and stopping conditions.
AI Agents in Internal Audit: Automating Workflows Without Surrendering Judgment
AI agents can coordinate audit work. The real challenge is controlling permissions, evidence, approvals, traceability, and quality.
Auditing the AI Lifecycle: From Idea to Retirement
AI lifecycle auditing connects approval, design, deployment, monitoring, change, and retirement to evidence, controls, and accountable decisions.
What Evidence Should AI Auditors Request?
A practical guide to AI audit evidence requests: policies, inventories, assessments, test results, logs, and monitoring records that support conclusions.
How to Build an AI Audit Work Program
Build an AI audit work program that links objectives and risks to controls, evidence requests, procedures, and defensible conclusions.
When AI Audits Need Technical Specialists
A practical guide to deciding when an AI audit can stay with generalists and when data science, cybersecurity, or model-risk support is needed.