Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI didn't replace the old ways data leaves a company. It added new methods on top and gave insiders a way to chain them together. You only see the chain if one sensor watches humans and AI together. If you spend enough time around CISOs / data security/ insider risk practitioners who have run these program for a decade you won't hear that AI is the only thing that matters.

Ask five security leaders what "data security for generative AI" covers and you'll get five different answers. Some may point to an existing DLP rule for ChatGPT, while others would point to an AI acceptable use policy. Some operate under the assumption that their DSPM vendor already handles genAI security. The confusion isn't about effort, as most teams are actively trying to get ahead of GenAI risk.

There is a difference between watching data go into an AI tool and knowing what is inside it. Most security tools do the first. Few do the second. Employees now keep contracts, source code, and customer records inside their AI workspaces, in chats and projects that build up for months. Cyberhaven's integration with Claude's Compliance API brings that content into view for Claude Enterprise, and ties it back to where it came from.

Teams evaluating whether to replace or extend an existing DLP stack often run into the same question: Is this actually a different category of tool, or just DLP with an AI feature bolted on? The two security categories overlap enough to cause real confusion in a buying cycle, and many may think that once will, by extension cover the other.

New AI tools appear every day. The novelty and utility they bring, along with the constant pressure to be more productive, pull employees toward them to get work done faster. The intent is good but the effect can range from problematic to damaging, because while there are rules in place for sanctioned tools, there are none for the ones that quietly show up in between.

Departing employees still email files to personal accounts and copy them to USB drives. Now they also paste sensitive content into ChatGPT, Claude, or Gemini to summarize a codebase, draft a portfolio piece, or package a project before their last day. Neither channel has replaced the other. The attack surface has simply gotten wider, and most security teams are still staffed and tooled for the channels they already know how to watch.

Most security tools evaluate risk by looking at the file, but risk is no longer confined to files. A clause pasted into an AI prompt carries no filename. A table summarized into Slack carries no label. A screenshot dropped into a deck carries no metadata, yet none of these trip an alert, because legacy data loss prevention (DLP) was built for a world where the sensitive unit is a discrete object with a name, a location, and a policy attached to it. That world is gone.

When an auditor asks who can access protected health information, cardholder data, or EU personal data, and why, most security teams cannot answer with confidence right away. Access sprawls across cloud storage, SaaS applications, shared drives, and generative AI tools faster than manual reviews can track it. Permissions get granted for a single project and never revoked. A spreadsheet gets shared broadly and forgotten.

Most enterprise data loss prevention (DLP) programs get judged on a single metric: how many exfiltration attempts did it block last quarter. That framing undersells what a modern DLP program needs to do. Sensitive data now leaves through AI prompts, personal cloud accounts, and agent-initiated file transfers that a legacy blocking rule alone was never built to catch, while auditors and boards expect evidence that the program is working, not just alerts confirming it.

Most organizations already have an insider risk management (IRM) program in some form. They have a tool, a dashboard, and an analyst reviewing alerts. What they often lack is a program built on the specific capabilities that turn activity logs into stopped incidents and reduced insider risk.