AI agents have rapidly moved from experimental tools to core components of daily workflows in security, engineering, IT, and operations. What started as personal productivity aids like personal code assistants, chatbots, and co-pilots has evolved into shared, organization-wide agents embedded in critical processes. These agents can organize workflows across multiple systems, for example:
- An HR agent that provisions or deprovisions accounts across IAM, SaaS apps, VPNs, and cloud platforms based on HR system updates.
- A change management agent that validates change requests, updates configurations in production systems, logs approvals in ServiceNow, and updates documents in Confluence.
- A customer support agent who receives customer references from the CRM, checks account status in the billing system, initiates improvements to backend services and updates support tickets.
To provide value at scale, organizational AI agents are designed to serve multiple users and roles. To access the tools and data needed to operate efficiently, they are granted broader access permissions than individual users.
The availability of these agents has unlocked real productivity gains: faster triage, less manual effort, and streamlined operations. But these early wins come with a hidden price. As AI agents become more powerful and more deeply integrated, they also become access intermediaries. Their broad permissions can obscure who is actually accessing what, and under what authority. In focusing on speed and automation, many organizations are ignoring the new access risks being introduced.
Access model behind organizational agents
Organizational agents are typically designed to operate on multiple resources to serve multiple users, roles, and workflows through a single implementation. Rather than being tied to an individual user, these agents act as shared resources that can respond to requests, automate tasks, and orchestrate tasks across the system on behalf of multiple users. This design makes agents easy to deploy and scalable across an organization.
To function seamlessly, agents rely on shared service accounts, API keys, or OAuth grants to authenticate to the systems they interact with. These credentials are often long-lived and centrally managed, allowing the agent to work continuously without user involvement. To avoid friction and ensure that the agent can handle a wide range of requests, permissions are often granted broadly, covering more systems, actions, and data than any single user.
While this approach maximizes convenience and coverage, these design choices may inadvertently create powerful access intermediaries that bypass traditional permission limitations.
Breaking the traditional access control model
Organizational agents often operate with far broader permissions than those granted to individual users, enabling them to span multiple systems and workflows. When users interact with these agents, they do not access the system directly; Instead, they issue requests that the agent executes on their behalf. Those actions run under the agent’s identity, not the user’s. This breaks the traditional access control model, where permissions are enforced at the user level. A user with limited access can indirectly initiate actions or retrieve data that they would not be authorized to access directly through the agent. Because logs and audit trails attribute activity to the agent, not the requester, this privilege escalation can occur without clear visibility, accountability, or policy enforcement.
Organizational agents can silently bypass access controls
The risks of agent-driven privilege escalation often emerge in subtle, everyday workflows rather than in direct abuse. For example, a user with limited access to financial systems could interact with an organizational AI agent to “summarize client performance.” The agent, working with broad permissions, pulls data from billing, CRM, and finance platforms, and returns insights that the user would not be authorized to see directly.
In the second scenario, an engineer without production access asks the AI agent to “fix the deployment issue.” The agent examines logs, modifies the configuration in the production environment, and triggers a pipeline restart using its own elevated credentials. The user never touched the production systems, yet production was changed on their behalf.
In both cases, no explicit policy has been violated. The agent is authorized, the request appears legitimate, and existing IAM controls are technically enforced. However, access controls are effectively bypassed because authorization is evaluated at the agent level, not the user level, leading to unintended and often invisible privilege escalation.
Limitations of traditional access control in the era 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 executed by an AI agent, the authorization is evaluated based on the identity of the agent, not the identity of the requestor. As a result, user-level restrictions will no longer apply. Logging and audit trails exacerbate the problem by attributing activity to the identity of the agent, thereby identifying who initiated the action and why. With agents, security teams have lost the ability to enforce least privilege, detect abuse, or reliably detect intent, allowing privilege escalation without triggering traditional controls. The lack of attribution also complicates investigations, slows incident response, and makes it difficult to determine intent or scope during a security incident.
Exposing privilege escalation in an agent-centric access model
As organizational AI agents take on operational responsibilities across multiple systems, security teams need clarity Visibility into how agent identities are linked to critical assets such as sensitive data and operational systems. It is essential to understand who is using each agent and whether gaps exist between a user’s permissions and the agent’s broader access, creating unintended privilege escalation paths. Without this context, overreach can remain hidden and unchallenged. As access evolves over time, security teams must continuously monitor changes to both user and agent permissions. This continuous visibility is critical to identifying new escalation paths as they are quietly introduced, before they can be misused or lead to security incidents.
Security of adoption of agents with Wing Security
AI agents are rapidly becoming some of the most powerful actors in the enterprise. They automate complex workflows, run throughout the system, and act on behalf of multiple users at machine speed. But that power becomes dangerous when agents are trusted too much. Broad permissions, shared access, and limited visibility can quietly turn AI agents into privilege escalation paths and security blind spots.
Secure agent adoption requires visibility, identity awareness, and continuous monitoring. Wing provides essential visibility by continuously tracking which AI agents operate in your environment, what they can access, and how they are being used. Wing maps agent access to critical assets, correlates agent activity with user context, and detects gaps where agent permissions exceed user authorization.
With Wing, organizations can confidently adopt AI agents, unlocking AI automation and efficiency without sacrificing control, accountability, or security.
