Will outbound DLP drown my team in false positives?

Outbound DLP has a reputation problem. Pattern-matching rules fire on every nine-digit number, analysts tune them down, and real leaks slip through. These are the questions security teams ask when they evaluate AegisAI's DLP.

Questions in this article

How accurate is detection of sensitive data?

In a benchmark on a public synthetic dataset, AegisAI identified Social Security numbers and credit card numbers with about 99% precision and 99% recall. The balance between precision and recall can be tuned to each customer's risk tolerance.

Finding the data is only half the job. AegisAI's context-aware layer then uses language models to judge the risk of each event: a card number sent to the customer's own payments vendor is not the same as one sent to a personal Gmail account. That judgment is what keeps alert volume down.

We already label mail in Microsoft Purview. Can you use those labels?

Yes. AegisAI can read Purview sensitivity labels on outbound mail and use them to decide what the agents analyze: for example, skipping mail labeled Public and sending Restricted mail through full analysis.

One caveat: when a label also encrypts the message, analyzing its content requires granting the app Rights Management super-user access. Many organizations treat that as a significant permission, so we scope it with you.

Can we add DLP if we're locked into another inbound email security contract?

Yes. AegisAI's DLP can be deployed on its own, alongside any inbound vendor. You don't need to wait for a renewal to close the outbound gap.

Can it block, or just alert?

On Microsoft 365, AegisAI supports outbound DLP enforcement today, in addition to alerting.