Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most data security posture management (DSPM) evaluations start with a deceptively simple question: where does our sensitive data live? There are many tools that answer that question. However, the number of tools that go further by tracking how data moves, enforcing controls when data leaves controlled environments, and closing the gap between visibility and action are far more limited.

Sensitive data has become the target, the signal, and the source of risk in nearly every modern security program. Source code, customer records, intellectual property, credentials, and regulated data now move continuously across endpoints, cloud apps, SaaS platforms, browsers, collaboration tools, and GenAI applications. That movement is not inherently bad. It is how modern work gets done.

Data exfiltration is the unauthorized transfer of sensitive data out of an organization's control. It happens across endpoints, cloud applications, removable storage, email, and, increasingly, AI tools. Understanding the most common forms of data exfiltration is the first step toward stopping it.

Most security programs have more visibility than ever. Dashboards are full. Alerts are firing. And incidents are still happening. That contradiction is not a coincidence. It reflects something most security vendors have quietly avoided saying out loud: Visibility and control are not the same thing, and for a long time, the industry has been selling one while calling it the other.

Security teams that have invested in AI governance programs over the past two years face a problem that those programs were not designed to solve. The controls built to manage generative AI, network proxies, browser monitoring, and SSO enforcement work when data moves through defined channels. Endpoint AI agents do not move through those channels. They run locally, operate at the OS level, and access data through pathways that exist entirely outside your current visibility.

AI agents are already running inside your organization. They are accessing files, calling APIs, and executing multi-step workflows with no human reviewing each action. Most governance programs were not designed for this. They were built around policies for human users, controls for known data channels, and audits that happen after the fact. None of those structures were designed to govern systems that act at machine speed across every environment where data lives.

Security teams have a data problem. Not a shortage of data, but instead there is a growing data surfacing problem. The signals are there, the incidents are logged, and the classifications exist. But, getting from raw data to a prioritized action plan still requires close to an hour of manual querying, tab-switching, and context reconstruction, every single time. The Cyberhaven Analyst Plugin changes that.

Security teams have spent years tracking what employees do with data. The harder problem now is tracking what agents do on their behalf. AI agents, whether running in an IDE, installed locally on a laptop, or connected to internal data through a model context protocol (MCP) server, operate with the permissions of the user who deployed them. They read files, query databases, call external APIs, and generate outputs. And in most enterprise environments, security teams have no reliable way to see any of it.

Security teams often focus governance efforts on the most popular AI tools. But the real risk question isn't which tools employees use most. It's which tools are growing fastest and what data those tools can reach. New data from Cyberhaven Labs shows that the AI categories posting the largest year-over-year growth numbers are the same categories with privileged access to source code, credentials, customer contracts, and internal architecture.

Data discovery has fundamentally changed over the last two years. The question is no longer just "Where is our sensitive data?" Organizations that stop there have a map but no enforcement. The tools that actually reduce risk answer a harder set of questions: Where did the data come from? Where is it going? Who touched it? And can we stop it before it causes damage?