Agentic AI in enterprise risk management: a practical framework

Autonomous AI agents change enterprise risk management by acting directly on systems, data, and workflows without the human review traditionally used to catch errors, creating operational consequences from misclassifications, security failures, or flawed objectives. The material identifies five principal risks—privileged access inheritance, multi-agent drift, data poisoning, compliance misreporting, and goal misalignment—and cites survey findings showing broad concerns about agent trust, permission overreach, and security incidents. It proposes a three-pillar governance model centered on discovering and inventorying agents, enforcing runtime defenses such as least-privilege access and prompt-injection filtering, and embedding accountability through ownership, audit logs, monitoring, and cross-functional oversight. Implementation is presented as a phased process, beginning with a 30-day pilot for a high-risk internal agent, extending to third-party agents within 90 days, and reaching enterprise-wide governance, automated enforcement, and board reporting over 12 months. The framework recommends aligning controls with NIST AI RMF, the EU AI Act, and sector-specific regulations, while calibrating human oversight according to each agent’s risk tier.

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