DLP should know better. Now it can. (Literally.)
For the last 20 years, “DLP” has been the most triggering three consecutive letters in enterprise security. For good reason. DLP’s rule-based framework was built to fail. We asked machines to mindlessly match patterns and fire alerts, then expected humans to investigate the noise. No organization can scale that.
So we rebuilt it from the ground up, and tackled a problem everyone else seemed to give up on.
Jazz is the AI-native, context-aware DLP that actually gets your business. With Jazz, you know whether an incident is a real threat to your data security posture… or just a scone recipe someone sent their mom from a company laptop.
It’s the world-class DLP program you always dreamed of, built to be operated by one person.
Built by good listeners.
Backed by people who believe in
fixing the impossible
"In the intelligence world, context is everything. Traditional DLP fails because it's completely blind to the context of the business it is meant to protect. Jazz's ability to understand the full story behind data movement is a true game-changer for managing insider risk."
Former-CTO, Central Intelligence Agency
"In large financial institutions, the sheer volume of data and the complexity of regulations make traditional DLP difficult to manage. Jazz’s AI-native, context-driven platform is the only scalable way to manage data risk in the modern enterprise."
ex-Global CISO, Barclays
"I've seen multiple generations of DLP, and they've all been variations on a broken theme. Jazz is the first solution built from the ground up for the modern, cloud-first, AI-powered world. It's not just a better mousetrap; it's a completely new approach."
Multi-time CISO (Fossil Group, Rent-A-Center)
"The DLP market has been stagnant for a decade, selling 'checkbox' solutions that nobody loves and everyone bypasses. Jazz isn't just iterating on that; they’re rewriting the rules with an AI-native approach that solves the noise problem once and for all."
Strategic Advisor, ex-CRO at Guardsquare
