Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A procurement lead asks an AI agent to summarize renewal terms for a software vendor. The agent pulls from a contract repository, a legal team’s chat thread, and a spreadsheet of negotiated discounts. A second agent turns the summary into a briefing and posts it to a shared workspace. No person reviews either handoff. Three weeks later, the vendor quotes an internal walk-away price that was never meant to leave the company.

Most organizations now have an AI policy on paper. Far fewer can tell whether employees follow it. According to ISACA, 25% of organizations have no AI policy at all, and only 38% of those with one describe it as formal and thorough. Meanwhile, employees keep pasting, uploading, and delegating work to AI tools, both generative and agentic. Cyberhaven Labs found that 39.7% of employee interactions with AI tools involve sensitive data.

Most AI security evaluations start with the tools employees already know about: the sanctioned chatbot, the browser plugin, the approved copilot. The harder inventory of AI tooling sits on endpoints, where coding assistants, desktop agents, and custom Model Context Protocol (MCP) servers read files, query databases, and chain actions with no human approving each step.

A data loss prevention (DLP) team writes a rule to keep customer records out of ChatGPT. The rule catches an uploaded spreadsheet but misses the same records pasted into a prompt, because no file transfer occurred. The team adds text pattern matching to close the gap, and the rule starts flagging harmless prompts that happen to contain a name and a number. Both failures share one cause. The rule judges what the text looks like and cannot see where it came from.

“Threat Actors Combine Automated and Hands-on Hacking Tools to Steal Sensitive Data.” Security teams read headlines like this every week. When they learn about new threats from sources like CISA Cybersecurity Alerts & Advisories, each one prompts a check on whether current tools cover it. And as these new threat vectors get published, some expose gaps that existing tools can’t cover. Teams respond by searching for products to address these risks.

A well-written AI policy is where governance starts. It gets legal, security, and the business aligned on how much risk the organization is willing to accept, and nothing else in the program works without it. Every AI policy ultimately exists to protect the data held by the organization. In today’s world, data held in information systems is the crown jewel of most organizations but the same data can put the organization at risk if it leaks, gets stolen, or is mishandled.

At Cyberhaven, we replaced our previous LLM-based approach to document understanding with a small model that makes 83% fewer decision errors at less than 1% of the inference cost. We achieved this by breaking complex tasks into simpler decisions, using uncertainty as a training signal, and heavily optimizing our inference stack. The model produces calibrated decision probabilities while preserving multimodal understanding and generative capabilities.

An AI support agent picks up a customer case, signs in with valid credentials, works on it in only approved systems, and closes it. I would reasonably predict that if someone were to review this workflow, it would likely come back clean, with no policy rule violation. But if we were to take a closer look, we might find that the agent did more than just close the case.

Security teams built their controls around people, applications, devices, and networks. AI agents operate across all four at once. An agent can pull records from Salesforce, combine them with financial data, summarize the result with a language model, and post it to Slack, with no person touching any step. Each action may look permitted on its own, which means the risk sits in the sequence, and most tools can’t see the sequence.

Data loss prevention (DLP) has been around for decades. But the way data moves today looks very different from the environment traditional DLP was built to protect. That change comes through clearly in the new Market Overview for Data Loss Prevention by Gartner. We see that Gartner describes a mature market undergoing significant evolution, with cybersecurity leaders increasingly looking for DLP that is adaptive, policy-driven, risk-based and AI-enhanced.