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Agentic AI Compliance: Achieving Auditability Across the Full AI Flow

Agentic AI Compliance: Achieving Auditability Across the Full AI Flow

As enterprises deploy Agentic AI to automate critical business decisions, a dangerous compliance gap is emerging: a critical visibility gap in their auditability. AI agents perform complex, multi-step actions—querying data, calling tools, and generating responses—making it nearly impossible for traditional audit logs to provide a verifiable record of the data, tools, and services involved in each automated decision. This lack of traceability is a primary barrier to responsible AI adoption and a significant risk for the business.

This session explores why current access controls create these visibility gaps and how shifting to a proactive, policy-based approach closes them. We will demonstrate how a dynamic authorization framework, grounded in Zero-Trust principles, creates an immutable, real-time audit trail for every decision across the full AI workflow.

Join us to learn how to build trustworthy, transparent, and fully auditable AI systems that satisfy regulators and accelerate business innovation.

Key Takeaways:

  • Understand why traditional audit logs fail to provide a complete, context-aware trail for complex AI actions.
  • Learn how to map dynamic, fine-grained authorization policies to each stage of the AI flow—from prompt to data retrieval, tool usage, and response.
  • Discover the critical role of Identity-First security in tying every automated action back to a verifiable human or machine identity.
  • Gain a practical blueprint for creating a real-time, compliant audit trail for regulatory and internal governance.
  • Learn how to answer the toughest questions from auditors regarding your AI systems’ behavior and data access.

Who Should Attend:

  • Chief Information Security Officers (CISOs) and IT/Security Leaders
  • Governance, Risk, and Compliance (GRC) Professionals
  • Security, Enterprise, and IAM Architects
  • Data Governance and Data Security Leaders

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