Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI adoption does not happen uniformly across an organization. Some employees have integrated generative AI (genAI) tools into core parts of their workflow. Others have barely opened one. Most are somewhere in between, experimenting on an ad hoc basis, without consistent visibility into what data those tools handle or where it goes. That variance is the problem. Security programs built around either universal AI adoption or zero AI adoption will miss most of the actual risk.

Consider this common scenario: The executives of an organization have approved the AI strategy, the vendors have been selected and the tools launched into production. Within days the internal security team finds out that employees have been pasting customer contracts into a generative AI (genAI) summarization tool for six months before anyone noticed. All that work didn’t stop unintentional data leaks.

Security budgets are tightening, and tool consolidation reviews keep landing on the same three categories: data security posture management (DSPM), data loss prevention (DLP), and AI security. At the same time, vendor marketing has done little to clarify the differences among the three and the path for organizations needing to enhance data security efficiently.

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.