Not long ago, AI agents were harmless. They wrote snippets of code. They answered questions. They helped individuals move a little faster.
Then organizations got ambitious.
Instead of personal copilots, companies started deploying shared organizational AI agents – agents embedded into HR, IT, engineering, customer support, and operations. Agents that don’t just suggest, but act. Agents that touch real systems, change real configurations, and move real data:
- An HR agent who provisions and deprovisions access across IAM, SaaS apps, VPNs, and cloud platforms.
- A change management agent that approves requests, updates production configs, logs actions in ServiceNow, and updates Confluence.
- A support agent that pulls customer data from CRM, checks billing status, triggers backend fixes, and updates tickets automatically.
These agents warrant deliberate control and oversight. They’re now part of our operational infrastructure. And to make them useful, we made them powerful by design.
The Access Model Behind Organizational Agents
Organizational agents are typically designed to operate across many resources, serving 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 actions across systems on behalf of many users. This design makes agents easy to deploy and scalable across the 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 involvement. To avoid friction and ensure the agent can handle a wide range of requests, permissions are frequently granted broadly, covering more systems, actions, and data than any single user would typically require.
While this approach maximizes convenience and coverage, these design choices can unintentionally create powerful access intermediaries that bypass traditional permission boundaries.
Breaking the Traditional Access Control Model
Organizational agents often operate with permissions far broader than those granted to individual users, enabling them to span multiple systems and workflows. When users interact with these agents, they no longer access systems 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 traditional access control models, where permissions are enforced at the user level. A user with limited access can indirectly trigger actions or retrieve data they would not be authorized to access directly, simply by going through the agent. Because logs and audit trails attribute activity to the agent, not the requester, this unauthorized activity can occur without clear visibility, accountability, or policy enforcement.
Organizational Agents Can Quietly Bypass Access Controls
When agents unintentionally extend access beyond the individual user authorization, the resulting activities can appear authorized and benign. Since the execution is attributed to the agent identity, the user context is lost, eliminating reliable detection and attribution.
For example, a technology and marketing solutions company with roughly 1,000 employees deploys an organizational AI agent for its marketing team to analyze customer behavior in Databricks, granting it broad access so it can serve multiple roles. When John, a new hire with intentionally limited permissions, asks the agent to analyze churn, it returns detailed sensitive data about specific customers that John could never access directly.
Nothing was misconfigured, and no policy was violated. The agent simply responded using its broader access, exposing data beyond the company’s original intent.
The Limits of Traditional Access Controls in the Age of AI Agents
Traditional security controls are built around human users and direct system access, which makes them poorly suited for agent-mediated workflows. IAM systems enforce permissions based on who the user is, but when actions are executed by an AI agent, authorization is evaluated against the agent’s identity, not the requester’s. As a result, user-level restrictions no longer apply. Logging and audit trails compound the problem by attributing activity to the agent’s identity, masking who initiated the action and why. With agents, security teams have lost the ability to enforce least privilege, detect misuse, or reliably attribute intent, allowing authorization bypasses to occur 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 event.
A New Identity Risk: Agentic Authorization Bypass
As organizational 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 or operational systems. It’s 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 authorization bypass paths. Without this context, excessive access can remain hidden and unchallenged. Security teams must also continuously monitor changes to both user and agent permissions, as access evolves over time. This ongoing visibility is critical to identifying new unauthorized access paths as they are silently introduced, before they can be misused or lead to security incidents.
Securing Agents’ Adoption with Wing Security
AI agents are rapidly becoming some of the most powerful actors in the enterprise. They automate complex workflows, move data across systems, and act on behalf of many users at machine speed. But that power becomes dangerous when agents are over-trusted, unmonitored, and unsupervised. Broad permissions, shared usage, and limited visibility can quietly turn AI agents into authorization bypasses and security blind spots.
Secure agent adoption requires visibility, identity awareness, and continuous monitoring. Wing provides the required visibility by continuously discovering 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 embrace AI agents confidently, unlocking AI automation and efficiency without sacrificing control, accountability, or security.
To learn more, visit https://wing.security/
Source: thehackernews.com…