AI agents need new access controls beyond RBAC, says expert

Traditional role-based access control (RBAC) is insufficient for AI agents accessing public sector data, according to Gopichand Mannava. Mannava proposes an alternative architecture using attribute-based access control (ABAC) with dynamic policy evaluation, purpose-bound data flows, and decision provenance. This approach aims to provide more granular, auditable, and secure access for AI agents by evaluating requests based on attributes like agent identity, task, and data classification, ensuring that access is proportionate to the task and that audit trails capture the 'why' behind each decision. AI

IMPACT New access control frameworks are needed to securely integrate AI agents with sensitive public sector data.

RANK_REASON Expert opinion piece detailing a technical approach to a problem.

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• attribute-based access control
• Gopichand Mannava
• Rego
• role-based access control
• State of Connecticut

• policy
• product
• infra

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