Classic DLP can’t read prompts. Anzenna reasons about them. Source code, customer records, and strategy docs are being pasted into AI tools every day. Anzenna sees sensitive data leaving for generative tools and acts on intent, not just patterns.
When an employee pastes a customer list or a code repo into a personal AI account, the data moves as ordinary encrypted browser traffic. Classic DLP rules were never built to inspect it, so the most common modern leak is also the most invisible one.
Pattern matching is not enough. A regex for a credit card number cannot tell that a prompt is quietly exfiltrating strategy. The risk is in the behavior and the data context, not the string.
AI DLP has to understand who is sending what, to which tool, and whether that is normal for them, the way an analyst would.
Source code into ChatGPT. Financials into Claude. Customer records into an AI extension. Anzenna captures these flows and ties each one back to employee identity, behavioral history, and surrounding context, so you see exactly what is entering AI, through which path, and when it crosses the line.
A prompt with sensitive data means nothing until you know who, and why.
A support agent summarizing a non-sensitive ticket in a sanctioned assistant.
A rushed employee pasting a customer list into a personal account to move faster.
A departing engineer feeding the core repo into an AI tool before the last day.
The flow surfaces as a reasoned case: the data, the path, and the person.
Anzenna brings behavioral context to the moment sensitive data meets a generative tool, agentlessly.
Correlate identity, SaaS, and data signals to see where sensitive content is heading toward AI tools, sanctioned or shadow.
Score each flow against a behavioral baseline, the person’s role, and the sensitivity of the data, not a static rule.
Turn the noise into a prioritized, fully-reasoned case file: who, what data, which tool, and why it matters.
Block, quarantine, revoke access, or notify, with a transparent audit trail and one-click action.
Traditional DLP inspects content against patterns on channels it can decrypt. Anzenna reasons over behavior, identity, and data context, so leaks into AI tools surface even when they ride ordinary encrypted traffic.
Data protection is one layer. Pair it with discovery, access control, and misuse prevention for full AI usage security.
