What are the security risks of AI agents

What Are the Security Risks of AI Agents?

Key takeaways

• AI Agent Security Risks expand attack surfaces because every agent operates through non-human identities, permissions, credentials, and connected systems.
• Excessive permissions and unmanaged OAuth grants can allow a compromised AI agent to access multiple business-critical environments without triggering the alerts a stolen human credential would.
• Just 22% of IT leaders are highly confident they could produce evidence of their AI governance decisions to auditors or regulators, according to the American Arbitration Association.
• Effective AI security depends on continuously discovering identities, understanding access relationships, and enforcing controls throughout the identity lifecycle, not on protecting the model alone.

What are the common security risks of AI agents?

The biggest AI agent security risks stem from the non-human identities they use to access enterprise systems, not from the language model itself. As AI agents accumulate permissions, credentials, and trust relationships, controlling identity becomes more important than protecting the model alone.

• Compromised tokens, secrets, or service accounts can provide attackers with legitimate access paths that generate no unusual authentication signal
• Agent permissions frequently grow over time without adequate review, since no offboarding event ever triggers a review
• Traditional Identity and Access Management (IAM) controls were designed for people, not autonomous software entities that act continuously
• Security teams often lack visibility into what AI agents can access and what actions they can perform once deployed

The non-human identity risks behind AI agents

How AI agents increase identity sprawl and security risk

Consider a common scenario. An enterprise deploys an AI operations agent to reduce cloud costs and improve uptime. As new capabilities are added, the agent accumulates OAuth grants, API tokens, service accounts, and other non-human identities across cloud and SaaS environments.

Over time, understanding the agent's true access and blast radius becomes increasingly difficult.

According to Gartner, 40% of enterprise applications will incorporate task-specific AI agents by 2026, up from less than 5% in 2025. As adoption accelerates, organizations must govern not only AI models, but also the identities, permissions, and credentials that enable them to operate.

The impact of unmanaged AI agent security risks

AI agents differ from traditional applications because they autonomously make decisions and take actions on behalf of users. Every action depends on non-human identities such as OAuth grants, API tokens, service accounts, and cloud roles.

As AI agents gain new capabilities, they create expanding machine-to-machine trust relationships. The OWASP Non-Human Identity Top 10 highlights excessive privileges (NHI4), secret leakage (NHI2), lifecycle failures (NHI1), and insufficient monitoring (NHI8). These same challenges increasingly affect agentic AI as identities and permissions accumulate.

Our AI operations agent illustrates the pattern. Each new integration introduced another identity and expanded the agent's effective access.

The challenge is no longer simply identifying AI agents. It is governing the identities, permissions, and authorization paths that enable them to operate.

What are the most important AI agent security risks?

AI agents introduce familiar security challenges, but they amplify them through automation, machine-to-machine trust, and rapidly expanding access. As capabilities grow, so do the identities, permissions, and credentials that support them.

Excessive permissions

AI agents often begin with limited access, but permissions accumulate as new integrations and automation capabilities are added. The Non-Human Identity Management Group reports that 97% of NHIs carry excessive privileges. In our example, the operations agent gradually expanded from read-only billing data to cloud infrastructure, developer tools, and production systems, increasing both its capabilities and the potential impact of a compromise.

Token and credential exposure

Each new integration also introduced another machine credential, whether an API key, OAuth token, or service account. Instead of protecting a single account, security teams now had to govern multiple credentials supporting the same agent. A single exposed token could provide legitimate access to connected systems.

Shadow AI and unmanaged agents

The challenge rarely stops with one agent. As additional teams deploy AI copilots, custom GPTs, and autonomous workflows, organizations inherit an expanding ecosystem of non-human identities that often bypass formal security review and governance.

Third-party trust relationships

AI agents rarely operate in isolation. They depend on OAuth, APIs, and machine-to-machine trust relationships to connect cloud providers, SaaS applications, and external services. In 2025, the Salesloft Drift incident demonstrated how compromised OAuth applications could provide attackers access to connected customer environments. According to the FINRA Cybersecurity Alert, the compromise cascaded to more than 700 organizations, highlighting how identity trust chains can extend well beyond a single application.

Our operations agent demonstrates the same pattern. Each new integration extended its access relationships, increasing its effective access and blast radius over time if the risk is not addressed.

What happens when non-human identities go unmanaged?

The consequences of a compromised AI agent extend far beyond a single credential. A stolen API key, OAuth token, or service account can provide legitimate access to every system the agent is authorized to use.

Depending on its permissions, a compromised AI agent may enable:

• Unauthorized infrastructure changes
• Data exposure
• SaaS application compromise
• Regulatory violations
• Business disruption
• Lateral movement across cloud environments

AI adoption is accelerating faster than governance. In the EY Technology Pulse Poll, 78% of leaders said AI adoption is surpassing their organization's ability to manage the associated risks.

Our AI operations agent demonstrates why. Once an agent's identities and permissions are established, a single compromised credential can provide legitimate access to multiple trusted systems.

Securing AI requires more than protecting the model. It requires governing the identities, permissions, and credentials that enable autonomous agents to operate.

A practical framework for autonomous AI agent security

Effective autonomous AI agent security starts with identity. Organizations must continuously discover AI-related identities, understand their permissions, and govern the access relationships that enable autonomous agents to operate. Yet many still lack complete visibility. Token Security's Autonomous but Not Controlled report, conducted with the Cloud Security Alliance across 418 IT and security professionals, found that 82% of organizations have discovered previously unknown AI agents operating on their networks, highlighting how easily unmanaged AI can undermine traditional security controls.

Our AI operations agent illustrates why this matters. Rather than asking only what the agent can do, security teams need to understand which identities it uses, what systems those identities can access, and how far an attacker could move if they were compromised.

Organizations can reduce that risk by following three continuous practices:

Discover all AI-related identities

Autonomous AI agent security starts by identifying every identity that enables an AI agent to operate. Without complete visibility, organizations cannot understand risk or govern access effectively.

Understand permissions and blast radius

Map how those identities connect to systems, data, and applications to understand effective access and potential blast radius.

Enforce continuous controls

Continuous governance requires more than periodic access reviews. Organizations should enforce least privilege, rotate credentials, monitor identity behavior, automate remediation, and govern identities throughout their lifecycle as AI capabilities evolve. The importance of ongoing monitoring is reflected in SANS Institute's 2026 AI Survey, with nearly half of practitioners identifying behavioral detection as their most effective AI security control, ahead of user awareness training (45%) and human analyst review (39%).

How Token Security approaches AI agent security

The challenge

AI agents create expanding networks of non-human identities, permissions, and credentials. As those connections grow, security teams lose visibility into access and an agent's true blast radius. It is a growing governance challenge: according to the Cloud Security Alliance, 68% of organizations cannot distinguish between actions performed by humans and those performed by AI agents.

The approach

Token Security takes an identity-first approach to AI agent security. It discovers non-human identities across cloud, SaaS, AI, and development environments, maps permissions and access relationships, analyzes blast radius, automates remediation, and governs identities throughout their lifecycle.

The outcome

Organizations gain continuous visibility into the identities behind AI agents, reduce excessive permissions, uncover hidden authorization paths, and maintain governance as AI environments evolve.

AI agent discovery becomes AI governance

Effective governance requires more than visibility. Organizations must continuously discover identities, understand permissions and risk, and enforce controls as AI agents evolve.

Bringing AI agents under governance at HiBob

As organizations expand AI adoption, governance becomes increasingly difficult when security teams cannot see which agents exist or what access they possess. HiBob needed visibility into custom AI agents before it could govern them, the exact problem the shadow AI section of this article describes.

"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 reinforces a central theme: organizations cannot govern AI agents until they can discover the identities, permissions, and credentials that enable them. This maps directly to OWASP NHI1, improper lifecycle management, which starts with not knowing an identity exists in the first place.

Understanding machine identity risk at scale at Lemonade

Visibility alone is not enough. Security teams must identify which machine identities create meaningful exposure so they can prioritize remediation instead of drowning in alert volume across a growing AI agent population.

"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

Lemonade's experience demonstrates the next step after discovery: understanding permissions, trust relationships, and blast radius so security teams can prioritize remediation instead of responding to an overwhelming volume of alerts.

Why AI agent security starts with identity

Our AI operations agent began as a simple reporting assistant. Over time, it evolved into an autonomous system powered by service accounts, OAuth grants, API tokens, cloud roles, and machine-to-machine trust relationships.

That progression reflects a broader shift in enterprise AI. While AI models make decisions, non-human identities determine what systems those decisions can access and what actions agents are authorized to perform. Our reporting agent became risky not because of its language model, but because its machine identities gradually accumulated permissions across trusted systems.

That challenge will only grow as AI adoption accelerates. According to IBM's Institute for Business Value, by 2027, enterprises expect to deploy an average of 1,661 AI agents, a 38% increase from today. As the number of autonomous agents grows, continuous identity governance becomes essential to maintaining visibility, enforcing least privilege, and keeping AI agents operating within their intended scope.

AI agent security is ultimately a non-human identity challenge because identity has become the control plane for modern AI. Organizations that continuously discover identities, understand permissions, and govern access relationships will be far better positioned to scale AI securely than those that rely solely on protecting the model. Protecting the model is important, but governing the identities that enable it is what keeps autonomous AI operating safely.