AI agents have rapidly moved from experimental tools to core components of daily workflows across security, engineering, IT, and operations. What began as personal code assistants, chatbots, and co-pilots to help individuals improve their productivity have evolved into shared agents across the organization embedded in critical processes. These agents can coordinate workflows across multiple systems. For example:
HR agents that provision or deprovision accounts across IAM, SaaS apps, VPNs, and cloud platforms based on HR system updates. A change management agent that validates change requests, updates configurations on production systems, records approvals in ServiceNow, and updates documentation in Confluence. Customer support agents who retrieve customer context from the CRM, check account status in the billing system, trigger remediation in backend services, and update support tickets.
To deliver value at scale, your organization’s AI agents are designed to accommodate many users and roles. They have broader access privileges compared to individual users to access the tools and data they need to operate effectively.
The availability of these agents has resulted in real productivity gains, including faster triage, reduced manual effort, and streamlined operations. But these early wins come with hidden costs. As AI agents become more powerful and more deeply integrated, they will also become intermediaries of access. Permissions are so broad that it can be difficult to understand who actually has access to what with what permissions. Many organizations are so focused on speed and automation that they overlook newly introduced access risks.
Access model behind the organization agent
Organization agents are typically designed to operate across many resources and serve multiple users, roles, and workflows through a single implementation. Rather than being associated with individual users, these agents act as a shared resource that can respond to requests, automate tasks, and coordinate actions across the system on behalf of many users. This design makes agent deployment easy and scalable across your organization.
To function seamlessly, agents rely on shared service accounts, API keys, or OAuth grants to authenticate with the systems they interact with. These credentials are often long-lived and centrally managed, allowing the agent to operate continuously without user intervention. To avoid friction and ensure that agents can handle a wide range of requests, permissions are often granted broadly, covering more systems, actions, and data than a single user typically needs.
Although this approach maximizes convenience and coverage, these design choices can inadvertently create powerful access intermediaries that bypass traditional permission boundaries.
Breaking through traditional access control models
Organizational agents often operate with permissions that are much broader than those granted to individual users and can span multiple systems and workflows. When users interact with these agents, they no longer have direct access to the system. Instead, you issue requests that the agent executes on your behalf. These actions are performed under the agent’s identity, not the user’s identity. This breaks the traditional access control model where privileges are enforced at the user level. Users with restricted access can indirectly trigger actions or retrieve data that they are not allowed to access directly, just by going through an agent. This privilege escalation can occur without clear visibility, accountability, or policy enforcement because logs and audit trails attribute activity to the agent rather than the requester.
Organizational agents can secretly bypass access controls
Agent-driven privilege escalation risks often surface in subtle everyday workflows rather than outright exploitation. For example, users with limited access to financial systems may interact with an organization’s AI agents to “summarize customer performance.” This agent operates with broader permissions, pulling data from billing, CRM, and finance platforms and returning insights that you don’t have permission to directly view.
In another scenario, an engineer without access to the production environment asks an AI agent to help him with a deployment issue. The agent examines logs, makes configuration changes in production, and triggers pipeline restarts using its own elevated credentials. The user has never touched the production system, but the production environment has been modified on their behalf.
In neither case does it violate any explicit policy. The agent is authorized, the request appears to be legitimate, and existing IAM controls are technically enforced. However, because authorization is evaluated at the agent level rather than the user level, access controls are effectively bypassed, resulting in unintended and often invisible privilege escalation.
Limitations of traditional access control in the age of AI agents
Traditional security controls are built around human users and direct system access, making them poorly suited for agent-mediated workflows. IAM systems enforce permissions based on who the user is, but when an action is performed by an AI agent, permissions are evaluated against the agent’s identity, not the requester’s identity. As a result, user-level restrictions no longer apply. Logging and audit trails further complicate matters by attributing activity to the agent’s identity, hiding who initiated the action and why. With agents, security teams lose the ability to enforce least privilege, detect misuse, or reliably identify intent, and privilege escalation can occur without triggering traditional controls. Lack of attribution also complicates investigations, slows incident response, and makes it difficult to determine the intent and scope of security events.
Unraveling privilege escalation in agent-centric access models
As an organization’s AI agents take on operational responsibilities across multiple systems, security teams need clear visibility into how agent identities map to critical assets such as sensitive data and operational systems. It is important to understand who is using each agent and whether there are gaps between the user’s privileges and the agent’s broader access, creating unintended privilege escalation paths. Without this context, excessive access can be hidden and problems can remain unresolved. Security teams must also continually monitor permission changes for both users and agents as access evolves over time. This continuous visibility is critical to identifying new escalation paths that are introduced silently before they can be exploited or lead to security incidents.
Secure agent recruitment with Wing Security
AI agents are rapidly becoming the most powerful actors within enterprises. They automate complex workflows, move between systems, and operate at the speed of machines on behalf of many users. But when agents are overconfident, their power becomes dangerous. Broad privileges, shared usage, and limited visibility can expose AI agents to privilege escalation paths and security blind spots.
Secure agent deployment requires visibility, identity awareness, and continuous monitoring. Wing provides the visibility you need by continuously discovering which AI agents are operating in your environment, what they have access to, and how they are being used. Wing maps agent access to critical assets, correlates agent activity with user context, and detects gaps where agent privileges exceed user authorization.
Wing allows organizations to confidently employ AI agents and achieve AI automation and efficiency without sacrificing control, accountability, or security.
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