Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Proprietary data is the definitive differentiator in the age of AI. Models can be replicated, infrastructure can be rented, and tools can be replaced. What cannot be easily reproduced is institutional knowledge, customer insight, and strategic intent found in enterprise data. This data must be continuously identified, deeply understood, and actively protected as it changes state, location, and context.

The next major security problem enterprises will face won’t originate in the cloud. It will emerge on endpoints, where agentic AI is already operating with autonomy, authority, and access to sensitive data.

Cybersecurity requirements for companies in the defense supply chain have entered a decisive enforcement phase. The Department of Defense has moved beyond self-attestation and toward verifiable, contract-bound cybersecurity standards. The Cybersecurity Maturity Model Certification (CMMC), now plays a central role in determining which organizations are eligible to work with the DoD. CMMC establishes three compliance levels, each tied directly to the sensitivity of the data an organization handles.

AI has no-so-quietly shifted from a single interface used by a small group of specialists into a mainstream capability embedded across enterprise infrastructure. Employees are now operationalizing AI for core business functions across departments. This shift fundamentally changes how organizations must think about data security.

As organizations generate, copy, and share more data than ever before, traditional security models are breaking down. Sensitive data no longer lives neatly inside a handful of databases or file servers. It flows continuously across cloud platforms, SaaS applications, employee endpoints, and now generative AI tools that create entirely new data derivatives at machine speed. This reality is exactly why data security posture management (DSPM) has become a fast-growing and essential data security solution.