AI Agent Identity and Access Control: A Framework for Agentic B2B Applications

The IBM Cost of a Data Breach Report 2024 highlights a record average cost of $4.88 million per breach, often linked to compromised credentials and overprivileged identities. With the integration of AI agents—autonomous programs acting across multiple systems—traditional Identity and Access Management (IAM) models designed for human users are insufficient. AI agent identity involves assigning verifiable identities to software agents, setting and enforcing access policies, and maintaining auditable action records. This necessitates extending IAM principles like least privilege and role assignment to dynamic, non-human entities. The text explores the challenges of AI agent identity, the inadequacy of Role-Based Access Control (RBAC) at scale, and the benefits of Attribute-Based Access Control (ABAC) and Fine-Grained Authorization (FGA) models, particularly Google's Zanzibar-inspired model for handling complex, multi-tenant data environments. It emphasizes the need for careful management of multi-agent delegation, robust audit trails, and the alignment of agent identity with enterprise Single Sign-On (SSO). As AI agents become integral to sensitive workflows, organizations must transition from basic RBAC to more sophisticated models like ABAC and FGA, ensuring precise, context-aware access control and compliance-ready audit trails.

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