Agentic AI Governance: A Policy Framework for Autonomous AI Agents

Agentic AI governance is a framework that organizations implement to manage autonomous AI agents, which differ from static LLM chatbots in their ability to perform multi-step actions, call external tools, and make decisions without human intervention at each step. This governance requires a distinct structure focusing on identity management, least-privilege access, behavioral monitoring, and human override mechanisms, as these agents can execute actions like querying databases or triggering financial transactions. The OWASP Top 10 for Agentic Applications 2026 outlines key governance risks such as agent goal hijacking and identity abuse, emphasizing the need for six control layers: identity and authentication, least-privilege access, behavioral monitoring, human oversight checkpoints, tamper-evident audit logging, and supply chain security. Organizations face challenges as Gartner predicts over 40% of agentic AI projects could be canceled by 2027 due to governance failures, with inadequate risk controls being a primary cause. NeuralTrust TrustGuard and TrustLens address several control layers, enhancing the governance of agentic AI systems to prevent unauthorized actions and ensure accountability.

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