Agentic AI systems differentiate themselves from traditional AI by exhibiting autonomous behavior rather than simply responding to inputs with outputs. This autonomy allows them to pursue long-term goals, execute actions, and interact with various cloud services without human oversight. Consequently, security implications arise from their capabilities, such as maintaining persistent states, requiring extensive access to APIs, and utilizing non-human identities with high permissions. Traditional security measures, which assume human-driven, short-lived actions, struggle against the continuous and cross-boundary operations of agentic AI. Risks include excessive permissions leading to over-privileged identities, state integrity failures from unprotected shared contexts, and challenges in monitoring actions that intertwine various systems. The OWASP Agentic AI project categorizes these risks, emphasizing the importance of implementing least privilege practices, explicit trust boundaries, and robust detection mechanisms to mitigate potential threats. By focusing on concrete risks and operationalizing security controls, companies can better manage the unique challenges posed by agentic AI.
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