Ten things to understand when using agentic AI applications

Agentic AI applications, which include autonomous software agents like copilots and infrastructure automation bots, are evolving beyond passive roles to execute real-world actions such as managing cloud resources and modifying secrets. These applications introduce complexities in security, identity, and governance, as they often operate as Non-Human Identities (NHIs) that are unmanaged and overprivileged. Unlike traditional automation tools, agentic AI applications possess dynamic behavior and autonomy, leading to increased unpredictability and potential security risks. Key challenges include managing non-human identity sprawl, ensuring least privilege doesn't equate to least risk, and governing API and CLI access, which are increasingly used by these agents. Emergent behavior, where agentic workflows chain multiple actions, complicates access control and necessitates runtime observability. Governance must extend beyond reviews to include actionable remediation and continuous context-aware access decisions. As agentic AI systems become integral to infrastructure, they require the same governance as privileged human actors, with a focus on identity assignment, policy-based access, and credential lifecycle controls, ensuring organizations can leverage these technologies without compromising security.

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