AI agents are no longer experimental. They are writing code, querying data, triggering workflows, accessing SaaS applications, and operating across cloud and production environments, often through API keys, OAuth tokens, cloud roles, and service accounts.

But most organizations still lack a clear way to discover, govern, and secure them.

The Token Security AI Agent Identity Security Buyer’s Guide gives security, identity, and risk leaders a practical framework for evaluating AI Agent Identity Security platforms. Learn why AI agent risk starts with identity, how to assess vendors, and what capabilities are required to secure agentic AI at enterprise scale.

Use this guide to evaluate the capabilities your organization needs to discover every AI agent, understand what it can access and why, and enforce identity-first governance across the full AI agent lifecycle.

What You Will Learn

AI agents introduce a new class of identity risk: autonomous software acting through credentials, permissions, service accounts, tokens, and cloud roles. Traditional IAM and prompt-level guardrails weren’t built for this problem.

Inside the guide, you’ll learn:

• Why AI agents should be treated as first-class identities, not just applications or workloads
• How to evaluate AI agent risk using the Access × Autonomy model
• Why guardrails and legacy IAM fall short once agents already have credentials and permissions
• How to discover shadow AI agents, MCP servers, local agents, production agents, and AI platform credentials
• What “intent-based access governance” means and why it matters for least privilege
• How to evaluate vendors across Discover, Understand, and Enforce capabilities
• What lifecycle controls are required for agent onboarding, ownership, access review, remediation, and decommissioning
• How AI agent risk is amplified by the underlying non-human identities they rely on
• Which integrations, coverage areas, and enterprise readiness requirements matter most when selecting a platform

Why This Guide Matters

AI agents operate differently from human users and traditional service accounts. They can be created quickly, modified constantly, connected to sensitive systems, and abandoned silently. As they become more autonomous, the gap between what they can do and what they should do grows wider.

Securing them requires a new model built around:

Discover every AI agent and the identities it uses
Understand its access, ownership, intent, and blast radius
Enforce policies, least privilege, lifecycle controls, and remediation continuously

Because if you don’t control identity, you don’t control AI.