Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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?

Most enterprise data security tools are built for a world where IT owns and manages every device. That world no longer exists. Contractors work from personal laptops. Entire teams run ChromeOS. Frontline workers access corporate systems through shared or unmanaged devices. And every one of those browser sessions can involve uploads, downloads, copy-paste, and form inputs touching sensitive data.

Most enterprises are not running on a single cloud. The vast majority of organizations now operate in hybrid or multi-cloud environments and sensitive data follows wherever workloads go. Regulated files end up in S3 buckets. PII lands in BigQuery development tables. Source code copies into Azure Data Lake repositories that no policy anticipated. The problem is not that organizations chose to spread data across clouds. The problem is that most security programs were not built to track it.

Every DLP evaluation starts with the same frustration: The tools that dominated the market a decade ago were built for a threat landscape that no longer exists. Sensitive data now moves across SaaS platforms, AI tools, encrypted messaging apps, and personal cloud accounts, often in ways no file-level policy can follow. If you are evaluating DLP for the first time or replacing a tool that has underdelivered, this guide gives you the framework to ask the right questions and recognize the right answers.

Every security vendor now claims artificial intelligence (AI) capabilities. Foundation models are becoming increasingly interchangeable, and the gap between what vendors promise and what programs actually deliver is widening. The question worth asking is not which vendor has the best model. It is: what is the model running on? The answer to that question determines whether a data security program hardens over time or requires constant manual maintenance.

Regulatory compliance is one of the most operationally expensive obligations security and legal teams carry. GDPR, HIPAA, CCPA, PCI DSS, and CMMC all require organizations to demonstrate, on demand, that they know where regulated data lives, who can access it, and how it is protected. Most enterprises struggle to meet that standard because they are trying to answer a continuous question with a periodic process.