Securing AI Agents Governance and Risk Control Strategies

In a recent interview with Help Net Security, Rohan Sen, Principal at PwC US, emphasized the critical need for robust governance frameworks when designing autonomous AI agents. As AI becomes increasingly integrated into business ecosystems, lax security measures can lead to significant reputational, operational, and compliance risks.

Key Governance Mechanisms for AI Agents

Sen highlights that autonomous agents should be treated as digital identities with real-world impact, requiring governance akin to human users. Key measures include:

• Least-privilege access and unique credentials
• Immutable logging for full auditability
• Sandboxed environments and real-time monitoring

Weak implementations, he warns, grant broad access without oversight, leaving agents vulnerable to prompt injection and adversarial manipulation.

Emerging Risks (12–24 Months)

Sen identifies four major risks from poorly governed agents:

1. Impersonation and brand damage: Malicious actors exploiting unsecured agents for phishing or fraud.
2. Unintended business actions: Over-permissioned agents triggering irreversible financial or operational consequences.
3. Regulatory exposure: Agents violating privacy rules due to lack of explainability.
4. Incident response gaps: Slow detection and containment of misbehaving agents.

Building Resilience in AI Ecosystems

Sen recommends concrete steps for leaders:

• Treat agents as actors, not tools, with high-privilege oversight.
• Implement foundational controls (e.g., authentication, logging) before deployment.
• Conduct red teaming to simulate adversarial scenarios.
• Classify agents by risk, applying stronger safeguards for high-risk functions.
• Foster cross-team awareness to ensure preparedness.

Incident Response Preparedness

A well-prepared plan should include:

• Agent registry detailing systems, permissions, and ownership.
• Behavioral baselines to detect deviations.
• Predefined kill switches for rapid containment.
• Cross-functional coordination with legal, compliance, and leadership teams.

Vendor Evaluation Questions

Buyers should ask AI vendors:

• How are agents authenticated and authorized?
• What safeguards prevent unsafe decisions?
• How is adversarial testing conducted?
• Is there tamper-proof logging?
• Which governance frameworks are followed?

For more details, read the full interview here.

Image credit: Help Net Security