AI Authentication: How to Securely Verify and Govern AI Agent Identities

Key Takeaways

• AI authentication verifies an AI agent's identity, but effective security also requires continuous governance of permissions, credentials, and trust relationships.
• OAuth 2.0 and OpenID Connect establish trusted access, but they do not identify excessive AI agent permissions or expanding blast radius.
• Identity abuse now drives nearly two-thirds of all breaches, underscoring the importance of securing non-human identities (NHIs).
• Organizations that continuously discover identities, understand permissions, and enforce lifecycle governance are better positioned to scale agentic AI securely.

How to Securely Verify and Govern AI Agent Identities

AI authentication verifies an AI agent's identity before granting access to applications, APIs, and data. Effective AI authentication also includes governing AI agent permissions, access tokens, and non-human identities throughout their lifecycle.

• Assign each AI agent a unique identity to ensure accountability and traceability.
• Use OpenID Connect to authenticate AI identities and OAuth 2.0 to authorize access with scoped access tokens.
• Continuously review AI agent permissions to identify excessive privileges and reduce attack paths.
• Govern AI identities from creation through retirement to maintain visibility, enforce least privilege, and support ongoing compliance.

Quick Facts

That challenge illustrates why AI authentication has become a foundational element of identity security.

Why AI Authentication Is Only the Beginning

Imagine your organization deploys an AI operations assistant to help engineers troubleshoot cloud infrastructure. It begins with read-only access to AWS billing data, then gradually connects to GitHub, Kubernetes, Jira, Slack, Microsoft 365, Terraform, and internal APIs. Every integration adds another permission, access token, or trust relationship.

When the security team asks, "What can this AI agent actually access?", authentication alone cannot answer.

Modern AI applications commonly rely on OpenID Connect (OIDC) to verify identity and OAuth 2.0 to authorize access with scoped access tokens. These technologies establish trusted access, but they do not reveal how permissions have accumulated, whether an AI agent has become overprivileged, or how far an attacker could move if the identity were compromised.

Like service accounts and Kubernetes workload identities, AI agents are non-human identities that continuously accumulate permissions and credentials. According to Gartner, by 2028, 25% of enterprise breaches will involve AI agent abuse by external attackers or malicious insiders.

As organizations adopt agentic AI, governing AI identities becomes just as important as authenticating them.

Following our example, six months later our AI assistant can review GitHub code, create Jira tickets, query production monitoring tools, access Kubernetes clusters, and interact with internal APIs. Every new capability improves productivity, but it also expands the agent's identity footprint.

If that identity is compromised, an attacker inherits the AI agent's accumulated permissions across multiple cloud services. Organizations without continuous visibility into AI agent permissions often struggle to identify overprivileged agents, orphaned credentials, and unnecessary trust relationships.

According to the OWASP Non-Human Identity Top 10, excessive privileges and unmanaged identities are among today's most common identity security risks.

What Are Best Practices for Securing Agentic AI Identities?

Leading security teams treat AI authentication as one part of a broader identity security strategy. Instead of relying on authentication alone, they continuously discover, understand, and enforce identity security throughout the AI identity lifecycle.

Discover AI Identities

Continuously inventory AI agents, service accounts, Kubernetes workload identities, OAuth applications, API keys, and other machine identities across cloud environments. Discovery provides visibility into every identity, credential, and connected service supporting an AI agent.

Understand Permissions and Risk

Use entitlement mapping to identify what each AI agent can access, how permissions have accumulated, and how trust relationships expand blast radius. Identity behavior profiles help identify compromised credentials, excessive permissions, and unusual AI behavior before they become attack paths.

Enforce Lifecycle Governance

AI agents are becoming another class of non-human identity that requires continuous governance. Reflecting that shift, Gartner predicts that by 2028, more than half of enterprises will deploy AI security platforms to secure third-party AI services and custom-built AI applications. Authentication provides the foundation, but organizations also need visibility into AI identities, permissions, and trust relationships.

Leading organizations:

• Assign every AI agent a unique identity.
• Authenticate identities with OpenID Connect and authorize access using scoped OAuth 2.0 access tokens.
• Enforce least privilege and regularly review AI agent permissions.
• Rotate access tokens, remove unused identities, and retire unnecessary permissions.
• Monitor behavior and maintain audit logs to support compliance and investigations.

This intent-based approach helps ensure AI agent permissions continue to align with each agent's intended purpose as applications, cloud services, and business requirements evolve.

How Token Security Approaches AI Authentication

Token Security extends AI authentication with the continuous visibility and identity governance administration (IGA) needed to secure AI agents at scale.

The Challenge

AI authentication verifies an AI agent before it accesses applications, APIs, or data. What it does not reveal is whether that identity has accumulated unnecessary permissions, established risky trust relationships, or expanded its blast radius across cloud environments.

As organizations adopt agentic AI, security teams often struggle to answer fundamental questions, such as: Which AI agents exist? What can they access? Which identities have become overprivileged? Which permissions no longer align with an agent's intended purpose?

Without continuous visibility into AI identities and their relationships, governing AI agent permissions becomes increasingly difficult.

The Approach

Token Security takes an identity-first approach, continuously discovering AI agents and other non-human identities across Kubernetes, cloud environments, SaaS applications, and connected infrastructure.

Token Security helps organizations:

• Discover AI agents, workload identities, service accounts, API keys, and other non-human identities.
• Map entitlements and trust relationships to understand what each identity can access.
• Analyze blast radius to identify excessive permissions and potential attack paths.
• Establish behavioral baselines that help detect unusual identity activity.
• Automate remediation of identity risks before they become security incidents.
• Govern AI identities throughout their lifecycle, from creation through retirement.

The Outcome

Organizations gain continuous visibility into AI identities, reduce unnecessary permissions, strengthen identity governance, and support AI adoption without losing control of machine identity security.

How Have Leading Companies Secured AI Agent Identities?

Leading organizations are applying identity-first security principles to gain visibility into AI agents and govern their access.

Gaining Visibility into AI Agents

As organizations adopt custom GPTs and other AI agents, maintaining visibility becomes increasingly important. HiBob wanted to accelerate AI adoption while ensuring every AI agent remained accountable and operating within approved security policies.

"Token Security gives us visibility we simply didn't have before. We can now automatically identify and control custom GPT agents running in our environment and ensure the required security level. Knowing that no AI agent is operating beyond our oversight means we can confidently accelerate our AI adoption." — Tamir Ronen, Global CISO, HiBob

HiBob's experience illustrates an important point: AI authentication establishes trust, but continuous visibility and governance ensure AI agents remain secure as their identities, permissions, and responsibilities evolve.

Turning Visibility into Action

Discovering machine identities is only the beginning. Security teams also need enough context to prioritize risks and remediate them efficiently.

"Token Security has enhanced our security by providing us accurate and relevant visibility into machine identity risks, something I have yet to see from other vendors. It's the first service I've seen that delivers on the widely-made claim of finding machine identity risk. It provides us with the right amount of detail to mitigate issues without burdening us with tens of rows of useless alerts." — Jonathan Jaffe, CISO, Lemonade

This experience reinforces the need to manage AI agents as non-human identities. As AI adoption grows, security teams need actionable insights into permissions, trust relationships, and blast radius so they can reduce identity risk before it becomes an attack path.

Why Identity Is the Control Plane for Agentic AI

AI authentication is an essential first step because it verifies an AI agent before access is granted. Authentication alone, however, cannot determine whether an AI agent's permissions remain appropriate as responsibilities expand and new integrations are added.

Every AI agent becomes another non-human identity that must be continuously discovered, understood, and governed. As identity abuse now drives nearly two-thirds of all breaches, organizations that map entitlements, analyze blast radius, enforce least privilege, and apply lifecycle governance will be better positioned to adopt agentic AI without increasing identity risk.

Authentication establishes trust. Identity governance maintains it.