Agentic AI governance is crucial for managing the authority and actions of autonomous AI systems within organizations, establishing clear boundaries on their access during execution. Unlike traditional AI governance, which focuses on model outputs and human decision-making, agentic governance must address action risks as these agents can directly execute tasks within business processes. The rise of agentic AI can unlock significant financial value, yet many organizations struggle with governance maturity. Risks include loss of execution control, unauthorized tool use, privilege escalation, and data misuse, necessitating robust oversight.
Effective governance requires a structured approach, beginning with defining an agent’s scope, mapping identity boundaries, conducting impact assessments, and establishing runtime controls. Ongoing evaluation and incident response plans are also critical to adapt governance as systems evolve. Ultimately, successful agentic AI governance integrates continuous oversight, ensuring accountability and operational integrity throughout the AI lifecycle.
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