Governance and Compliance for Agentic AI: Reducing Autonomous AI Risks

Read full article from Okta here: https://www.okta.com/identity-101/agentic-ai-governance-and-compliance/?utm_source=nhimg

Agentic AI, autonomous systems that can plan, decide, and act with minimal human oversight, is moving from concept to enterprise reality. Unlike traditional AI, these agents can pursue long-term goals, interact with external tools, and adapt their behavior in real time. Research shows AI task completion is doubling every seven months, meaning capabilities and risks, are accelerating faster than most governance frameworks can keep up.

Why Legacy Frameworks Fall Short

Existing governance models assume predictable workflows, centralized control, and human-in-the-loop oversight. Agentic AI disrupts these assumptions by operating:

• Dynamically (adapting mid-process instead of following linear steps)
• Autonomously (taking actions without real-time human approval)
• Decentralized (interacting across distributed systems, APIs, and other agents)

Traditional tools like the NIST AI RMF, while valuable, weren’t built for machine-to-machine decision chains, black-box reasoning, or autonomous execution that bypasses centralized checkpoints.

Core Risks Emerging from Agentic AI

1. Expanding Attack Surfaces - Each autonomous decision creates a potential failure mode. Token sprawl, unmanaged APIs, and agent-to-agent interactions can amplify vulnerabilities at machine speed.
2. Accountability Gaps - When an autonomous agent causes harm, it’s often unclear who is responsible, especially when reasoning is opaque.
3. Integration Complexity - Enterprises face fragmented identity and access flows when third-party AI tools connect to core systems.
4. Regulatory Tensions - Laws like the EU AI Act require human oversight, yet fully autonomous operations risk conflicting with these mandates.

Identity-First Governance: The New Control Plane

To govern agents effectively, organizations must move from perimeter-based to identity-centric governance. Every autonomous agent is essentially a Non-Human Identity (NHI), requiring unique credentials, least-privilege access, and continuous verification.

Key requirements include:

• Unique agent identities with verifiable credentials
• Dynamic access control that adjusts in real time based on context and risk
• Cross-system authentication protocols (e.g., OAuth extensions for agents)
• Continuous monitoring of decisions, tool use, and behavioral drift
• Explainability measures to generate human-readable audit trails

This identity-first approach creates accountability, visibility, and guardrails, ensuring autonomous agents remain trusted participants in enterprise ecosystems.

Building Effective Governance Frameworks

Organizations should treat AI agents as digital contractors. entities that act on behalf of the enterprise but require strict oversight. Recommended strategies include:

• Risk-based deployment - Start with low-risk use cases and expand autonomy as governance matures.
• Cross-functional governance teams - Blend expertise from security, engineering, legal, and compliance.
• Sandbox testing & graduated autonomy - Validate agents safely before granting broader access.
• Continuous monitoring and improvement - Adjust governance models as AI capabilities evolve.

Regulatory Alignment and the Path Forward

With the EU AI Act and sector-specific rules setting a baseline, enterprises must anticipate stricter oversight on autonomy. Organizations that proactively adopt identity-first governance, real-time monitoring, and explainability measures will not only stay compliant but also build trust with regulators, customers, and stakeholders.

Bottom Line

Agentic AI governance isn’t just about compliance, it’s about risk containment, accountability, and trust at scale. By treating autonomous agents as NHIs with enforceable identities, enterprises can balance innovation with control. Those that invest in identity-driven governance today will be best positioned to scale agentic AI responsibly tomorrow.