AI agents were considered harmless sometime ago. They did what they were supposed to do: write snippets of code, answer questions, and help users in doing things faster.
Then business started expecting more.
Slowly, companies started using organizational agents over personal copilots- agents integrated into customer support, HR, IT, engineering, and operations. These agents didn't just suggest, but started acting- touching real systems, changing configurations, and moving real data:
- A support agent that gets customer data from CRM, triggers backend fixes, updates tickets, and checks bills.
- An HR agent who overlooks access throughout VPNs, IAM, SaaS apps.
- A change management agent that processes requests, logs actions in ServiceNow, updates production configurations and Confluence.
- These AI agents automate oversight and control, and have become core of companies’ operational infrastructure
Work of AI agents
Organizational agents are made to work across many resources, supporting various roles, multiple users, and workflows via a single implement. Instead of getting linked with an individual user, these business agents work as shared resources that cater to requests, and automate work of across systems for many users.
To work effectively, the AI agents depend on shared accounts, OAuth grants, and API keys to verify with the systems for interaction. The credentials are long-term and managed centrally, enabling the agent to work continuously.
Threat of AI agents in workplace
While this approach maximizes convenience and coverage, these design choices can unintentionally create powerful access intermediaries that bypass traditional permission boundaries.
Although this strategy optimizes coverage and convenience, these design decisions may inadvertently provide strong access intermediaries that go beyond conventional permission constraints. The next actions may seem legitimate and harmless when agents inadvertently grant access outside the specific user's authority.
Reliable detection and attribution are eliminated when the execution is attributed to the agent identity, losing the user context. Conventional security controls are not well suited for agent-mediated workflows because they are based on direct system access and human users. Permissions are enforced by IAM systems according to the user's identity, but when an AI agent performs an activity, authorization is assessed based on the agent's identity rather than the requester's.
The impact
Therefore, user-level limitations are no longer in effect. By assigning behavior to the agent's identity and concealing who started the action and why, logging and audit trails exacerbate the issue. Security teams are unable to enforce least privilege, identify misuse, or accurately assign intent when using agents, which makes it possible for permission bypasses to happen without setting off conventional safeguards. Additionally, the absence of attribution slows incident response, complicates investigations, and makes it challenging to ascertain the scope or aim of a security occurrence.
