Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every audit season, a governance, risk, and compliance (GRC) team pulls access reports, screenshots configurations, and reconstructs months of data handling from whatever logs survived. The package passes the audit, but the evidence is stale a week later.

Data lineage is fast becoming one of the most important ways to understand data exfiltration, for both human and agentic workflows. More and more vendors now market data lineage, each claiming their approach is superior. That makes us proud parents: what was once an esoteric technology is finally going mainstream. Cyberhaven pioneered data lineage in data security and has spent years perfecting it.

Security teams can spend years tuning DLP rules for the tools they know about. But, then an employee will paste a customer list into an AI assistant that did not exist on any policy six months ago, and the rule never fires. The tool was not on the list, and once the data returns as a summary, it no longer matches the pattern a rule was written for either, creating risk the security team is completely blind to. A bigger rule set does not close this new risk gap.

AI has changed how work gets done. Agents can plan tasks, query enterprise repositories, invoke tools, write code, call application programming interfaces (APIs), and take actions across connected systems. As they work, they create, copy, fragment, transform, and share data across workflows. A perimeter and a one-time authorization decision do not provide enough control for this operating model. An agent can chain permissions, tools, integrations, and data sources while completing a task.

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.