Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI is moving fast. But the transition from GenAI tools that respond to prompts to AI agents that execute workflows represents something qualitatively different for security leaders. The shift goes beyond just scale, and is a fundamental change in how data moves, who touches it, and what decisions get made, often without human review.

Enterprise security teams spent years building data loss prevention (DLP) programs around a predictable set of egress channels: email, USB drives, cloud storage, and sanctioned SaaS apps. Generative AI has rewritten those assumptions almost overnight. Today, the same data those DLP controls were built to protect is flowing into AI interfaces that most organizations have no visibility into and no enforcement capability over.

If your organization is evaluating DSPM solutions, you're likely already aware of the core promise: discover sensitive data, understand its risk, and improve your posture. But DSPM's value extends well beyond a single use case or a single team. Security leaders who get the most from their DSPM tool treat it as a cross-functional intelligence layer, not just a compliance checkbox. Below are eight use cases that illustrate how DSPM delivers value across both security and business outcomes.

Artificial intelligence has moved from pilot project to core enterprise infrastructure faster than most security programs can adapt. AI is automating workflows, surfacing insights from complex datasets, and changing how work gets done across every function. But with that acceleration comes a new and expanding attack surface that most organizations are only beginning to understand.

Insider threats account for 34% of all data breaches, yet most organizations are still building security programs designed to stop attackers from the outside. The harder truth? The risk is already inside your walls, and it doesn't always look like a criminal. Not every insider threat is malicious. Some are distracted. Some are overworked. Some are just trying to get things done faster.

As organizations manage sensitive data across endpoints, cloud platforms, and a growing number of SaaS applications, having clear visibility into where data lives and how it moves has become increasingly important. For companies operating in highly sensitive and IP driven environments, the ability to understand data access and respond quickly to risk is essential.

Enterprise data environments have fundamentally outpaced the security architectures designed to protect them. Sensitive data now exists across endpoints, cloud infrastructure, SaaS platforms, and AI workflows simultaneously, often replicated in fragments that carry no labels and trigger no file-based controls.

Most security teams are being asked to "enable AI" before they have any real sense of which tools are safe to use. That gap is costing them. Cyberhaven's research found that the majority of AI tools in active enterprise use today fall into high or critical risk categories, and more than 80% of enterprise data flowing into AI is going to those risky tools, not to platforms built with serious security in mind. To help security teams cut through the noise, we built the Cyberhaven AI App Risk Checker.

For a long time, data loss prevention (DLP) lived in the margins of security programs. It was something teams deployed to satisfy a requirement or reduce obvious risk. A handful of policies, some visibility into network traffic, maybe a scan of cloud storage. That was usually enough. That model reflected how work used to happen. Data moved more slowly, lived in fewer places, and followed more predictable paths. That is no longer true.

Most insider threats do not start with obvious intent. They start with small changes: A file gets downloaded that does not need to be or a user accesses data outside their usual scope. Information gets shared in ways that feel slightly off. Each action on its own can look harmless, but together, they point to insider risk. That is what makes insider threat indicators hard to catch for security teams. You are not looking for a single violation. You are looking for patterns in how people interact with data.