Pomerium’s Secure AI Agent Access brings Zero Trust authorization to AI agents, scripts, and LLM-backed tools by evaluating each request—not just sessions—against identity, task, and context. Instead of over-permissioned agents and static credentials, Pomerium issues short-lived, scoped identities to agents, enforces granular policies at the application/ingress layer, and logs every action with intent and matched policy for full auditability. Teams can run Pomerium entirely in their own environment, apply centralized policies to human, service, and agent identities, and integrate seamlessly with existing IdPs and agent runtimes, including MCP-based workflows. The result is least-privilege, per-action control that limits data exposure, prevents lateral movement, and provides provable compliance across autonomous workflows, RAG systems, pipelines, and bots. Key Features & Benefits:

• Identity- and context-aware authorization: Enforces per-action access based on agent identity, task scope, source IP, headers, and time to achieve least privilege.
• Short-lived, scoped credentials: Eliminates shared keys and long-lived tokens by issuing policy-verified sessions tied to real context.
• Full audit trail: Logs every request with intent and policy decision for compliance, forensics, and real-time monitoring.
• Central policy engine: Manage human, service, and agent access from one place; apply consistent Zero Trust controls across environments and MCP stacks.
• Self-hosted or hybrid deployment: Keep enforcement and logs in the organization’s environment; no third-party routing or SaaS dependency.
• MCP-ready guardrails: Experimental MCP support to secure agent tool calls, prevent token leakage, and authorize per tool/route with granular logging.
• Data exposure controls: Enforce data-level policies via headers/metadata, limit read/write by purpose, and contain prompt-chaining drift.
• Model endpoint protection: Secure LLM/API access with policy-driven controls; avoid public exposure and static API keys