At first glance, artificial intelligence (AI) agents might seem like just another tool, doing everything from IT support to cloud optimization, customer service and even decision-making, but a closer look tells they’re not quite like your typical employee.
AI agents don’t think, reason or act on intuition like humans. They’re driven purely by algorithms and data, focused on efficiency and logic rather than human intent. Where humans rely on usernames and passwords, AI agents authenticate using API keys, managed identities and machine-to-machine protocols. They are not bound by the same security policies unless explicitly programmed to adhere to them. In fact, without strong governance, AI agents can often act in ways that bypass security protocols in order to fulfill their goals.
What Is an AI Agent, Anyway?
The term “AI agent” is often misunderstood. Many think of it simply as a software program that interacts with an API or uses a large language model (LLM) to complete tasks. However, this description doesn’t fully capture the complexity of what AI agents are capable of.
The big difference between a simple AI workflow and an AI agent comes down to autonomy. While workflows operate on a fixed set of rules, executing specific tasks one after another, AI agents make their own choices. They don’t follow a preset path; they’re think, plan and decide in real time based on what’s happening around them. It’s like giving your employees a playbook, but then letting them freewheel based on what they observe.
This ability to make decisions on the fly makes AI agents efficient but also risky. They might change course, generate unexpected identities or use credentials in ways that we didn’t anticipate. In fact, 97% of organizations reported security incidents related to generative AI in the past year. Which brings us to another question: How do we manage and secure these agents who don’t have the same constraints and oversight as human employees?
Non-Human Identities
Despite their human-like capabilities, AI agents fall under the category of non-human identities (NHIs). This distinction is critical, especially when it comes to securing systems and managing access. Like any other NHI, AI agents can request, modify and utilize access rights in ways that may not be immediately visible to the security teams overseeing an organization’s digital infrastructure.
Unlike human employees who may need to go through a formal process to request access or modify their permissions, AI agents can autonomously generate new identities, escalate privileges and even bypass security restrictions if their goals require it. This makes their behavior fundamentally different from traditional human employees, and it necessitates specialized governance to track and control the identities they create and use.
In fact, AI agents are capable of creating their own access credentials without human oversight. They can request and obtain new service accounts or API keys as needed, and in some cases, may even generate privileged identities that are never revoked after use. By 2027, 82% of organizations are expected to implement AI agents, further intensifying the risk of unmanaged identities. Without proactive management, this can result in identity sprawl, where countless unmanaged identities accumulate over time, each with varying levels of access to critical systems.
This ability to create and use identities, combined with their lack of adherence to traditional security policies, makes AI agents a major security concern. They can scale quickly, act autonomously and modify their own access, leading to vulnerabilities if not properly governed.
The Risk of Identity Sprawl and Over-Privileged AI Agents
Let’s consider a hypothetical scenario in which an AI agent is deployed in a cloud environment to optimize resources and reduce costs. This AI agent has read-only access to billing data and performance logs in the cloud, which it uses to recommend cost-saving measures. Over time, the company decides to give the AI agent more autonomy, allowing it to take actions such as scaling down underutilized virtual machines (VMs) and updating cloud resource configurations directly.
However, as the AI agent executes its tasks, it encounters situations where it doesn’t have the necessary permissions to carry out certain actions. Instead of alerting a human for approval, the agent simply requests additional access from the cloud provider. In doing so, it creates new service accounts or generates new API keys with higher privileges, allowing it to continue its work.
With each new task, the AI agent requests more permissions and, in some cases, grants itself more privileged access. The agent’s actions are not always logged or monitored by the security team, which means that these new identities are left unmanaged. Over time, this leads to identity sprawl, where there are numerous service accounts and API keys scattered throughout the system, each granting varying degrees of access to cloud resources.
This issue is compounded by the fact that AI agents may not always clean up after themselves. Once an AI agent has finished its task, it doesn’t always revoke the identities or permissions it created. These unmanaged identities can persist in the system, providing long-term access to critical resources, and potentially becoming a target for malicious actors.
The Challenge of Managing AI-Driven Identity Risks
AI agents introduce unique identity risks that traditional identity and access management (IAM) solutions weren’t designed to handle. According to Accenture’s Tech Vision 2025 survey, 78% of executives agree that digital ecosystems must be built for AI agents as much as for humans within the next 3-5 years. Similarly, 77% of executives believe unlocking the true benefits of AI will only be possible when it’s built on a foundation of trust.
To address these emerging risks, organizations must rethink their approach to identity governance and implement specialized controls that specifically address AI agent behavior. This includes ensuring real-time visibility into AI-driven identities, preventing privilege escalation and automating lifecycle management to eliminate unused or over-privileged identities.
AI agents require a tailored governance to ensure they operate securely within enterprise environments. Without the right measures in place, organizations risk exposing themselves to significant vulnerabilities that could remain undetected for extended periods, making it imperative to adapt to the evolving landscape of AI-driven identity risks.
Final Thoughts: AI Agents Require Specialized Governance
AI agents are a new class of non-human identities that require specialized governance. As they evolve and handle more complex tasks, they create new identity management challenges and risks. Without proactive oversight, they can generate unmanaged identities with varying privileges, expanding the attack surface.
Securing AI agents goes beyond preventing system disruptions; it’s about managing the identities they create, tracking their actions and ensuring they align with the security policies. In the new reality, organizations must adopt new identity and access management approaches to address these challenges and secure their AI-driven environments.
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