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VideoHow Agentic AI Is Changing AI Governance Requirements
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Most AI governance programs were built around a familiar model: A person submits a prompt, an AI system generates a response and a human reviews the output before taking action. That model begins to change as organizations adopt agentic AI.
Unlike traditional generative AI applications, agentic AI systems can plan, invoke tools, execute multistep workflows and adapt their behavior with limited human intervention. As organizations begin deploying these systems in production environments, governance questions extend beyond the content an AI system generates to the actions it performs.
The result is a governance challenge that many existing AI programs were not designed to address.
Why Existing AI Governance May Not Be Enough
Many organizations have established AI governance policies, approval processes and oversight committees. Those remain important, but they may not fully address the risks introduced by autonomous AI systems.
Agentic AI can execute dozens of tool calls during a single session, making decisions based on previous actions and changing circumstances. Evaluating each action independently may not provide sufficient visibility into the cumulative risk created over the course of an autonomous workflow.
In this white paper, Weaver and Assury identify several governance gaps organizations should evaluate as they prepare for agentic AI, including:
- Managing cumulative risk across an AI session rather than individual actions
- Authorizing actions based on context instead of credentials alone
- Adjusting an agent’s autonomy as risk changes
- Producing audit evidence that demonstrates governance decisions occurred before actions were taken
A Common Theme Across Leading Frameworks
Although ISO/IEC 42001, the NIST AI Risk Management Framework and the EU AI Act were developed for different purposes, they increasingly point toward similar governance expectations for organizations deploying agentic AI.
The white paper examines how these frameworks converge around several common principles, including:
- Human oversight
- Operational governance
- Accountability
- Evidence that governance controls function as intended
Rather than treating these frameworks as separate compliance exercises, organizations may be able to design governance capabilities that support multiple requirements simultaneously.
Governance Is Increasingly About Runtime Evidence
One of the paper’s central observations is that documenting policies alone is no longer enough. As AI systems become more autonomous, organizations need evidence that governance controls are actively evaluating actions during execution, not simply recording what happened afterward.
That distinction has implications for CISOs, governance teams, auditors and technology leaders evaluating how AI systems operate in production environments.
Agentic AI introduces new governance considerations that extend beyond traditional generative AI controls. As organizations evaluate AI adoption, understanding how leading governance frameworks apply to autonomous systems can help inform technology, risk and compliance decisions.
Download the white paper, Governing the Action Layer: How ISO 42001, NIST AI RMF and the EU AI Act Converge on Agentic AI Runtime Controls, for a detailed examination of the governance gaps, framework comparisons and practical guidance for AI, cybersecurity and governance leaders.
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