What is Zero Trust for AI agents?

Zero Trust for AI agents means every human, service account, model workflow, and agent must prove identity, authorization, context, and purpose before taking action across tools, APIs, repositories, cloud, or SaaS.

What controls matter most for AI agent security?

The most important controls are unique agent identity, least privilege, short-lived credentials, model gateway policy, MCP and tool governance, sandboxing, egress control, approval gates, revocation, and provenance logs.

Why is provenance important for AI agents?

Provenance lets responders reconstruct what happened by tying together prompt, model, tool call, command, artifact, reviewer, token, and deployment evidence.

Zero Trust AI Agent Security Hub

Control stack

The six layers every AI agent program needs.

Zero Trust for AI agents is not just network segmentation. It is a chain of identity, authorization, runtime policy, tool governance, containment, and provenance that keeps working after agents connect to real systems.

Unique identity for every human, service account, model workflow, and AI agentJust-in-time access and short-lived credentials for tools, APIs, repos, and cloudModel gateway policy for provider routing, logs, token budgets, and data handlingMCP and tool registry controls for what agents can call, read, write, and changeRuntime containment with sandboxing, egress limits, approvals, and revocationProvenance logs that tie prompt, model, tool call, command, artifact, and reviewer

Reading path

Start here for Zero Trust AI agent security.

AI Agent Security2026-09-05

Always-On AI Agents in the Astra Era: Who Controls the Loop?

Learn how always-on AI agents work, what GPT-6 Astra changes, and how to secure agent loops with scoped permissions, durable memory, and clear stop conditions.

AI Agent Security2026-08-23

Zero Trust in the Age of AI Agents: Identity, Provenance, and Runtime Controls

Zero Trust in the age of AI agents means every human, model, agent, device, API, repository, and workflow must prove identity, need, context, and authority before action. Network location is no longer the trust boundary.

AI in Security2026-04-01

NIST's AI Agent Identity Push Gives Security Teams a Deadline and a Design Signal

NIST's February 2026 work on AI agent identity and authorization is a timely signal that the real enterprise risk is no longer model output alone, but what agents are allowed to do, prove, and audit once they start acting.

AI in Security2026-05-15

AWS's New AI Security Framework Argues Day-One Controls Matter More Than Post-Launch Cleanup

AWS used mid-May 2026 guidance to make a useful point for defenders: secure AI programs start with identity, access, and guardrails in the prototype phase rather than after agents reach production.

AI in Security2026-07-11

LiteLLM as the Central Gateway for GenAI and Agentic Code Models: Control, Vetting, and Token Discipline

Security teams do not need every product team wiring its own OpenAI, Anthropic, Bedrock, Vertex AI, and coding-agent credentials. A centralized LiteLLM gateway can make GenAI and agentic-code usage more controlled, vetted, auditable, and cost-aware.

AI in Security2026-04-24

MCP Security Best Practices: How to Secure Model Context Protocol Servers, Clients, and Tokens

Model Context Protocol can make AI tools dramatically more useful, but it also expands trust boundaries. Security teams should treat MCP like a privileged integration layer: sandbox servers, minimize scopes, block token passthrough, defend against SSRF, and review every tool as a potential remote-action surface.

AI in Security2026-08-08

New AI Models Can Silently Hack Companies: What Defenders Should Change Now

Frontier AI models are moving from code suggestions to sustained cyber operations. Security teams should assume quiet, multi-step AI-driven intrusion attempts are becoming realistic and update controls before attackers operationalize them.

Implementation guides

Translate the architecture into daily workflows.

CISOs, platform security teams, AI platform owners, and security architects2026-08-19

Zero Trust for AI agents starts with identity and ends with evidence.