Session Risk

Session risk refers to the potential for unauthorized access, data exposure, or security breaches that occur during active user sessions with AI systems or applications. In enterprise AI governance, managing session risk is critical because it encompasses authentication vulnerabilities, token hijacking, unauthorized privilege escalation, and data leakage during live interactions with AI models. Organizations must implement robust session management controls, enforce appropriate access timeouts, monitor session activity for anomalous behavior, and ensure compliance with security frameworks that govern how AI systems handle sensitive information during active use periods.

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ResearchGlobal2026-09-09

Weaver and Assury Map Four Agentic AI Governance Gaps Compliance Programs Are Missing

Consulting firms Weaver and Assury have published a practitioner framework identifying four governance gaps specific to agentic AI deployments: cumulative session risk, context-based authorization failures, dynamic autonomy changes, and the absence of pre-action audit evidence. The analysis maps these gaps directly to enterprise controls including approval workflows, least-privilege enforcement, and independent assurance over agent actions. Compliance teams using conventional AI governance programs will find those programs largely silent on all four issues.

agentic AI Weaver Assury auditability least privilege session risk human oversight authorization