How Do You Secure AI Agents?

Key Takeaways

• AI agents become security liabilities when machine identities accumulate excessive permissions across cloud, SaaS, development, and AI environments.
• Identity governance is becoming an AI governance requirement because every autonomous agent authenticates using non-human identities rather than human accounts.
• Only 21% of organizations report having mature governance processes for agentic AI, according to Deloitte, highlighting a growing operational gap between AI adoption and AI governance.
• Organizations that continuously discover machine identities, understand permissions, and enforce least privilege reduce attack paths before credentials are compromised.

Why Does Securing AI Agents Require an Identity-First Approach?

Most AI security advice focuses on the model: prompt injection, jailbreaks, output filtering. That misses where the real damage happens. Every AI agent authenticates using machine identities, service accounts, OAuth tokens, API keys, cloud IAM roles, and it is those identities, not the language model, that an attacker actually inherits when an agent is compromised. Securing AI agents means governing the identity behind the agent, not just the agent's behavior.

How Do You Secure AI Agents?

AI agents are most secure when organizations govern the non-human identities (NHIs), permissions, credentials, and trust relationships that enable them to operate.

Effective AI agent security should:

• Discover every AI agent and the machine identities, service accounts, and credentials it uses
• Enforce least privilege by limiting permissions and continuously reviewing access
• Monitor OAuth grants, API tokens, service accounts, cloud roles, and identity behavior for signs of misuse
• Secure AI agents throughout their lifecycle by rotating credentials, removing unnecessary access, and retiring unused identities

Common AI Agent Security Risks and How to Address Them

Why AI Agent Security Is Different from Traditional Cybersecurity

Traditional applications execute predictable workflows. AI agents make decisions, invoke tools, access multiple systems, and take actions with limited human involvement. That autonomy changes the security model.

Consider an organization deploying an AI operations agent to support its Site Reliability Engineering (SRE) team. Initially, the agent reviews monitoring alerts, opens Jira tickets, and posts Slack updates using read-only access and a handful of OAuth connections. As the team expands its responsibilities, it gains cloud roles, API tokens, service accounts, and access to Kubernetes, GitHub, and internal documentation. Each permission makes sense on its own. Together, they create a highly privileged non-human identity (NHI) with an increasingly difficult-to-understand blast radius.

As agentic AI adoption accelerates, the problem compounds. As organizations deploy more AI agents, cloud services, and automation, non-human identities continue to proliferate faster than security teams can effectively govern them. According to the Non-Human Identity Management Group, only about 20% of security professionals express strong confidence in their organization's ability to securely manage non-human workload identities, while fewer than 6% report having full visibility into service account usage.

The challenge is no longer deploying AI. It is governing the identities, permissions, and authorization paths that allow AI agents to operate.

Why Do AI Agent Identities Become a Security Risk?

Over time, AI agents accumulate the identities, permissions, and credentials needed to perform increasingly complex tasks. Our cloud optimization agent illustrates this pattern. What began as a simple automation gradually restarts Kubernetes workloads, retrieves secrets, creates GitHub pull requests, and accesses internal documentation. Each new capability requires another OAuth grant, API token, service account, or cloud role.

Individually, these permissions appear reasonable. Together, they create a highly privileged non-human identity with access to production infrastructure, source code, cloud resources, and collaboration platforms. Organizations are already seeing the consequences of this growing complexity: according to Token Security's Autonomous but Not Controlled report, conducted with the Cloud Security Alliance across 418 IT and security professionals, 65% of organizations reported experiencing at least one AI agent-related security incident in the past 12 months.

This is why securing AI agents is critical. They authenticate using machine credentials, and if one of those credentials is compromised, attackers inherit the agent's trusted access without needing to bypass authentication. MITRE ATT&CK T1078 (Valid Accounts) describes exactly this pattern: a legitimate credential, operating inside its authorization boundary, used in ways nobody intended.

The risk is not theoretical. In 2025, the Salesloft Drift incident demonstrated how compromised OAuth applications gave attackers access to connected customer environments through trusted application relationships. According to the FINRA Cybersecurity Alert, attackers stole OAuth tokens and used them to access Salesforce, Google Workspace, Slack, and cloud credentials across more than 700 organizations. Rather than breaking authentication, the attackers exploited identities that customers had already authorized.

The cloud optimization agent presents the same challenge. A single compromised credential could provide access to every connected system the agent already trusts. As AI agents accumulate identities, permissions, and access relationships, identity, not the AI model itself, becomes the attack surface.

What Happens When Organizations Ignore AI Identity Governance?

Months later, the team integrates a third-party troubleshooting plugin so the agent can diagnose production failures automatically. The new capability requires another OAuth grant and additional access to cloud infrastructure, expanding the agent's trusted identity.

When attackers later compromise the plugin vendor, they steal the agent's OAuth token instead of targeting the organization directly. Because the identity already has legitimate access, they do not need stolen employee credentials, phishing emails, or MFA bypasses. They simply inherit the permissions the agent already possesses.

This problem extends beyond a single compromised agent. As organizations deploy more AI tools outside formal governance processes, distinguishing legitimate from unauthorized AI activity becomes increasingly difficult. According to Larridin, 68% of enterprise employees use unauthorized AI tools, and 83% of organizations report that shadow AI is expanding faster than IT can track it.

Within minutes, the attackers can:

• Access production configuration
• Enumerate cloud resources
• Retrieve internal documentation
• Move laterally across connected systems through existing access relationships

The compromise succeeds because the trusted identity, not the AI model, became the attack path.

This illustrates why AI identity governance matters. The OWASP Non-Human Identities Top 10 identifies excessive permissions (NHI4), secret leakage (NHI2), vulnerable third-party identities (NHI9), and lifecycle failures (NHI1) among today's most common machine identity weaknesses.

Traditional Identity and Access Management (IAM) controls are designed to detect compromised human accounts. They are far less effective when attackers inherit the legitimate permissions of a trusted machine identity. According to the Cloud Security Alliance's Securing Autonomous AI Agents survey, only 18% of security leaders are highly confident that their current IAM systems can effectively handle agent identities.

What Are AI Agent Security Best Practices?

Effective AI agent security governs AI agents as non-human identities rather than software. They focus on three priorities:

• Discover every AI agent and machine identity
• Understand permissions, authorization paths, and blast radius
• Enforce least privilege and lifecycle governance as agents evolve

Following these AI agent security best practices changes how organizations discover identities, assign permissions, monitor behavior, and govern access over time.

Inventory Every AI Agent and Machine Identity

You cannot secure identities you do not know exist. AI agents are often deployed through low-code platforms, developer frameworks, or SaaS applications outside centralized IT. Discovery should include the machine identities, credentials, and effective access relationships that power AI agents.

Apply Least Privilege Continuously

AI agents naturally accumulate permissions as new capabilities are added. Regular entitlement reviews, credential rotation, and removal of unused access help prevent unnecessary permissions from lingering. This matters because, according to The Hacker News reporting on Entro Security research, 47% of machine identities are more than one year old, and their credentials have never been rotated.

Monitor Machine Identity Behavior

Compromised machine identities rarely generate the same signals as compromised user accounts. Instead, monitor for unusual API activity, new OAuth scopes, unexpected resource access, or changes in credential usage that differ from an agent's normal behavior.

Govern the Identity Lifecycle

AI governance does not end at deployment. As agents evolve, their identities should evolve with them through continuous ownership reviews, credential rotation, permission cleanup, and retirement of unused identities. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) recommends continuously reducing unnecessary privileges and managing credentials to limit identity-based attacks through its Secure by Design initiative.

How Token Security Approaches AI Agent Security

The Challenge

AI agents create expanding networks of machine identities, permissions, and access relationships. As those connections grow, security teams lose visibility into what agents can access and how far a compromised identity could reach. Intent-based security closes these visibility gaps by continuously discovering machine identities and ensuring their access aligns with their intended purpose.

The Approach

Token Security is built for identity-first security rather than adapting controls designed for human identities. It follows three continuous stages:

• Discover: continuously discover NHIs across cloud providers, SaaS applications, AI platforms, Kubernetes, and development environments
• Understand: map entitlements, authorization paths, and blast radius to identify excessive permissions and hidden access paths
• Enforce: automate remediation, reduce unnecessary access, strengthen credential hygiene, and govern machine identities throughout their lifecycle

Applied consistently, these practices remove unnecessary permissions, replace long-lived credentials, and detect unusual behavior before a compromised identity can expand its blast radius.

The Outcome

Rather than treating AI agents as standalone applications, Token Security governs the network of machine identities that allows them to operate. Organizations gain continuous visibility into identities, permissions, and blast radius, allowing them to reduce identity risk before it becomes an attack path.

AI Agent Security Success Stories

Learn how intent-based security helps organizations govern AI agents through continuous discovery of machine identities, least-privilege access, and the elimination of hidden authorization paths.

Controlling Shadow AI Before It Becomes Shadow Risk

HiBob needed visibility into custom AI agents before expanding AI adoption. This is the exact problem the shadow AI section of this article describes: agents deployed through low-code platforms and custom GPT builders that never pass through a formal governance process, and therefore never appear in a security review until something goes wrong.

"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 at HiBob

HiBob's experience illustrates an important point. AI governance begins with discovery. Organizations cannot secure AI agents they cannot see, nor can they govern permissions they do not understand. This maps directly to OWASP NHI1 (improper offboarding and lifecycle failures), which starts with not knowing an identity exists in the first place.

From Machine Identity Visibility to Actionable Remediation

Machine identity inventories are only useful when they produce actionable security improvements. Discovering that an agent's permissions have expanded is not the same as knowing which expansion actually matters, out of thousands of routine grants across a Kubernetes and cloud environment.

"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 at Lemonade

This reinforces that AI agent security is not simply about discovering identities. Security teams also need enough context to prioritize remediation, reduce excessive permissions, and eliminate unnecessary attack paths, precisely the gap between finding an over-permissioned identity and knowing whether it is the one that matters.

Why Non-Human Identities Require a New Security Model

Our agent never became dangerous because of its language model. It became dangerous because its machine identities accumulated permissions across trusted systems. Identity governance is what keeps AI agents operating within their intended scope.

That distinction is becoming increasingly important as organizations deploy more autonomous agents, often without proper oversight. Every AI agent ultimately depends on machine identities, credentials, permissions, and trust relationships to access enterprise systems. Protecting the model is only one layer of AI security. Organizations must also govern the machine identities that allow agents to act.

That is why identity has become the control plane for agentic AI. Organizations that continuously discover machine identities, understand permissions and blast radius, and enforce lifecycle governance will be better positioned to adopt AI safely while reducing their attack surface.