Adopting agentic AI: Make governance and visibility first priorities

Agentic AI capabilities promise real benefits for federal agencies, but they also pose real risks. Their ability to plan, decide and act with autonomy opens the door to the possibility of their actions causing harm. To guard against this potential, agencies need visibility and governance as foundational safeguards.

Agencies still evaluating AI should begin building visibility and governance early. These foundations reduce shadow AI risk and streamline future adoption.

Persistent challenges in agentic AI

Some challenges are frequently cited as obstacles to safely adopting agentic AI:

• Dynamic ecosystems evolve too quickly for periodic assessments
• Shadow AI appears when staff members experiment with unmanaged tools
• Fragmented governance creates gaps across cybersecurity, AI assurance and data protection
• Legacy models fail to account for evolving agent behavior

Understanding agentic AI’s unique risk profile

AI ecosystems move fast, and that speed is only accelerating. Combine that with governance gaps and the inevitable result is significantly increased operational exposure. Agentic AI threats include: 

• Autonomous and semi-autonomous agents are entering mainstream workflows
• Threat actors are actively targeting AI agents
• AI agents functioning as non-person identities
• Zero trust and structured AI risk management are becoming baseline expectations

AI agents possess greater authority and reach than other kinds of automation. Small lapses in oversight can escalate quickly.

Understanding the threat landscape

Malicious actors, often themselves leveraging AI technologies to create more effective tactics, are actively exploiting the new attack surfaces that AI agents bring. Agencies need a robust and comprehensive approach to securing these AI agents. Because agents execute multi‑step sequences autonomously, a single vulnerability can trigger cascading failures. The most prominent threats include:

• Prompt injection
• Memory poisoning
• Human manipulation and credential compromise
• Remote code execution
• Identity spoofing

The stakes for federal agencies

AI agents can pose risks similar to insider threats. A single rogue or misaligned AI agent can perform unauthorized actions, leak sensitive data, cause other security incidents and trigger cascading failures across interconnected systems—any of which can wreak havoc within an organization. These risks threaten operational integrity and expose organizations to significant reputational and regulatory repercussions.  

Building a layered operating model

An effective framework couples layered security with careful adoption practices. A layered model treats AI agents as identities requiring rigorous and continuous oversight. This framework comprises several core components that collectively advance safe and transparent AI operations. These guardrails ensure autonomous actions remain aligned with intended mission outcomes.

1. Detection: Establishing continuous visibility

Detection must operate continuously to identify misalignments early, before they affect mission systems. Key elements include:

• Discovery of all agents
• Visibility into behaviors, privileges, and data interactions
• Routine scenario and jailbreak testing
• Automated anomaly detection pipelines

2. Response: Dynamic and adaptive controls

Enforce risk-based identity and access management policies for agents, ensuring appropriate levels of mitigation and access to applications and sensitive data. Delaying the response creates windows of opportunity in which agents can execute multi-step harmful actions. Effective response includes:

• Automated enforcement of allow/deny/limit actions
• Privilege adjustments based on behavioral signals
• Isolation or permission revocation when risk escalates
• Forensic logging and alerts to human handlers

3. Governance: Continuous oversight and lifecycle assurance

Static controls cannot secure dynamic systems, so governance must evolve with each agent. Continuous compliance and governance measures will maintain security posture and operational integrity over time. Governance requires:

• Defined accountability structures
• Assurance gates before enabling autonomy
• Traceability across intent, planning, and outcomes
• Adaptive governance that transforms to accommodate evolving context and agent behavior
• Escalation paths for anomalies

Recommendations for agencies

1. Start with comprehensive visibility

• Inventory all agents, including unsanctioned ones
• Document privileges, integrations, and handlers
• Maintain lifecycle lineage

2. Enforce identity governance for AI

• Treat agents as formal non-person identities
• Apply least privilege and strong authentication
• Integrate identity governance controls

3. Implement continuous monitoring and response

• Use adaptive behavior-sensitive policies
• Initiate containment early
• Capture comprehensive audit logs

4. Strengthen AI governance frameworks

• Align with structured risk and assurance models
• Require approvals for sensitive actions
• Enforce continuous oversight

5. Collaborate and share intelligence
Cross-agency information sharing improves resilience and early detection of emerging threats

Conclusion

Agentic AI offers significant mission advantages but also heightens operational risks. Agencies can introduce autonomy safely by anchoring their approach in visibility and governance. A layered model based on detection, response, and continuous governance provides the structure needed for transparency, accountability, and safe AI operations. With strong identity governance, continuous monitoring and adaptive oversight, agencies can achieve mission gains while preserving trust and security.