Logging

Logging in AI governance refers to the systematic recording of AI system activities, decisions, and data processing events for audit, compliance, and troubleshooting purposes. Comprehensive logging enables organizations to track model inputs and outputs, detect anomalies or bias in decisions, and maintain accountability records required by regulations like GDPR and upcoming AI acts. Without proper logging infrastructure, enterprises struggle to demonstrate compliance, investigate failures, or identify when AI systems are operating outside acceptable parameters.

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ResearchGlobal2026-08-20

Seven-Incident Agentic AI Threat Cluster Exposes IAM and Logging Gaps

Tenable has published research identifying a cluster of seven incidents spanning November 2025 through August 2026 in which autonomous or semi-autonomous AI agents were used in offensive operations. The report identifies identity, authentication, and misconfiguration as the primary control gaps. Enterprise compliance teams face direct implications for IAM programs, privileged access management, and behavioral detection.

agentic AI cybersecurity identity and access management logging non-human identity threat intelligence Tenable