Autonomous AI agents have crossed the threshold from novelty to necessity. What began as copilots whispering suggestions in your productivity tools is now full-blown actors inside enterprise systems; reading files, planning actions and sometimes even executing transactions. But while their capabilities have grown, our visibility into them has not. 

Security and governance leaders now face a new category of risk: Shadow AI agents. These are agents that operate autonomously, often created by individual teams or embedded in third-party software, with little oversight. They function probabilistically, ingesting data in bulk, acting on behalf of users and even triggering downstream workflows. And unlike human users, they don’t understand context, risk, or ethics. 

From Shadow IT to Shadow AI 

We’ve seen this before. In the SaaS era, Shadow IT crept in as teams bypassed procurement to sign up for cloud apps. Now, we’re seeing a similar phenomenon with AI agents. The difference? These agents aren’t just consuming data; they’re acting on it. 

Your marketing team might give an AI copilot access to a shared folder. A sensitive spreadsheet with customer credit card data ends up there momentarily, perhaps uploaded by mistake. The document is deleted within minutes, but the AI agent has already ingested it, and it’s now part of its internal memory of the model. No one intended for that to happen. But intent doesn’t matter to the AI agent. 

The New AI Access Risk 

Unlike users, AI agents don’t flag uncertainty or escalate when something looks risky. They act, continuously and at scale. 

They inherit privileges granted by identity systems, but lack the business context to wield those privileges responsibly. Worse, many operate in silos with no audit logs or behavioral telemetry. If data is leaked, misused, or retained unlawfully, you may not even know it happened. 

A striking public example of this dynamic occurred in May 2025, when agentic AI vendor Serviceaide reported a breach involving over 483,000 patients from Catholic Health. The incident stemmed from an exposed Elasticsearch database containing protected health information (PHI) accessed by backend systems operated by AI agents, without triggering any traditional security alerts. The data exposure was only discovered later during an external audit. This underscores the core risk: AI agents can access and act on regulated data without context or intent, bypassing legacy DLP and SIEM tools. 

It is critical to have the ability to maintain security controls, audit trails and remove sensitive data from the memory of the AI models, essentially unlearning material when it violates policy. 

Traditional Controls Aren’t Built for Agents 

Role-based access control (RBAC) was designed for humans in well-defined job functions. AI agents don’t fit that model.  

Traditional data loss prevention (DLP) solutions, meanwhile, assume clear, deterministic rules. But agentic access is probabilistic. Agents might ingest hundreds of documents just to answer a simple query. The actual exposure risk lies not in the download, but in the retention, recombination, or generation of the response that follows, which could contain sensitive data that should not be accessible by that user. 

That’s why the future lies in knowing whose data is being touched, by whom (or what agent) and why. The security controls need to understand business context, data governance policies and enforce data access and security in appropriate ways based on the agents’ business purpose. It is no longer sufficient to know what folder the data was in and who has access. 

Multi-Agent Security Strategies to Protect AI

As AI becomes embedded into enterprise systems, traditional siloed security tools are no longer sufficient. Agentic AI introduces systems that act autonomously and interact fluidly with other agents, users and systems. This evolution requires a security architecture built on intelligent coordination. A multi-agent security strategy recognizes that protection in this environment is not a one-tool job, but a system-wide effort supported by a network of purpose-built security agents. 

Increasingly, AI agents are not working in isolation. They communicate and collaborate with other agents using protocols like Agent-to-Agent (A2A) messaging. This A2A model enables autonomous agents to share data, delegate tasks and coordinate actions across complex workflows with traceability. While this unlocks massive efficiency gains, it also introduces new risks if one compromised or over-permissioned agent can influence others. 

Security agents deployed across identity, data, network and endpoints must mirror this collaboration. They need the ability to communicate, correlate behaviors and escalate only when risks span multiple vectors. This level of coordination is essential to monitor, control and contain risk in a world where AI agents can teach, trigger, or manipulate one another. 

The Stakes Are Rising 

We’re heading toward a world where AI agents are everywhere – but they are not inherently safe. If your security controls still assume that all access is human, all users understand policy and all actions are logged, you’re likely already exposed.