Insights
AI Agents: What Changes for Internal Audit
AI agents do more than generate answers: they can choose steps and take actions. Here is what that changes for internal audit assurance.
A Practical AI Risk Taxonomy for Auditors
A reusable taxonomy for identifying and auditing AI risk across governance, data, models, GenAI, agents, third parties, security, and people.
From Regulation to Audit Procedure
A practical method for turning an AI requirement into risk, control, evidence, testing, and a defensible audit conclusion.
ISO 42001 for Internal Auditors: From Management System to Evidence
ISO/IEC 42001 turns AI governance into an auditable management system. This guide explains its logic, evidence, and practical use for Internal Audit.
Agentic AI: New Risks From Autonomous Actions
As AI moves from generating answers to taking action, permissions, execution limits, stopping conditions, and logs become critical controls.
Third-Party AI: Who Owns the Risk?
Outsourcing AI does not outsource risk. What auditors should assess across vendors, contracts, service changes, monitoring, and exit planning.