DLP is Broken. It’s Time to Remaster It.

I’ve spent my career building products and talking to security leaders. And if there’s one thing I’ve learned, it’s this: absolutely no one loves their DLP.

For years, Data Loss Prevention has been a story of failure. Not by accident, but by design. It was built on a flawed premise - a rigid framework for a world that no longer exists. It was a tool blind to the nuances of modern work, leading to the noise, friction, and frustration that every CISO knows too well.

I see security teams stuck in one of three states. They are Paralyzed, wisely refusing to adopt a tool they know will become an operational nightmare. They are Trapped, held hostage by a legacy solution they keep running merely as a compliance checkbox, knowing it drowns their best people in false positives. And they are Failed - the ones with the battle scars from implementations that went nowhere, with expensive shelfware serving as a constant reminder of a broken promise.

But the ground has shifted beneath our feet. With the explosion of SaaS, the rise of Generative AI, and a workforce that is more distributed than ever, the old playbooks of ‘do nothing’ or ‘accept the compliance checkbox’ have gone from calculated risks to ticking time bombs.

The Realization That Changes the Game

The fundamental bet of legacy DLP — machines match patterns, humans provide context — was always a compromise. Human-level reasoning couldn't be deployed at scale, so we split the work and accepted the gap. That compromise never actually worked. It just felt like the only option.

It's not anymore. We now have AI that can reason — not just classify files, but understand what a document contains, who a person is, whether an action makes sense in the context of how a business actually operates. Human-level intelligence, deployable at scale.

The moment that became real, DLP needed to be rethought from first principles. Not iterated on. Rebuilt from scratch.

That's what Jazz is.

A Fundamental Framework Shift

When we started building, we asked: "What would a world-class data security program actually look like if it could understand every situation the way a senior analyst does — autonomously, at scale?"

The answer is Melody — our agentic investigator. Melody is an autonomous intelligence, that instead of just flagging potential problems, performs deep, iterative investigations on its own. It analyzes events across the four dimensions that truly matter: the data itself (is it M&A plans or could this perhaps constitute MNPI?), the systems involved (is it corporate SharePoint or a personal Google Drive?), the people (what is their role and typical behavior?), and the business process (why is this happening? is this a sanctioned workflow?). Melody gets to the bottom of exactly what happened, why it happened, and what was the actor’s intent.

Melody’s ability to investigate like this is made possible by two supporting pillars that represent a complete departure from legacy architecture.

First, it’s fueled by Deep Context. Our forensic endpoint agent contains a Context Vault that captures a text-rich signal of every user action. This isn't just a log of metadata; it’s the full story - the why behind what’s happening. This high-fidelity data, previously impossible to process at scale, is a match made in heaven for modern LLMs, allowing us to truly glean an actor's intent. This is what enables infinite coverage, seeing into not only all browsers but all desktop applications.

Second, Melody leans on a Natural Language Policy Engine. We replaced the old, rigid rule-sets that could never capture the true intent of a policy. Instead, you state your goals in plain English. This allows Melody to perform a human-level assessment, comparing its deep understanding of a situation with your actual business intent. This is what enables unprecedented precision.

From Noise to Narrative

This combination of autonomous investigation, deep context, and nuanced judgment was what was needed to finally change the game. It moves you from a stream of noisy alerts to a handful of clear, pre-investigated answers.

For a 5,000-employee organization, this means seeing just ~10 high-risk incidents a day - each one a pre-investigated narrative, complete with the full story and inferred intent that would have taken a human analyst hours to uncover and piece together - just for a single incident.

That’s not just efficiency. That’s the end of alert fatigue. That is a DLP that finally works.

And the market is responding. We’ve been selling in stealth, gaining extreme traction at a velocity that has surprised even us. Because when CISOs see this, they don't just buy it. They share it with their peers.

We are Jazz. We're a team of Unit 81 alumni and builders from Axonius and Laminar. We didn't come here to iterate on DLP; we came to remaster it. And today, we’re officially coming out of stealth with a $61 million capital raise to accelerate this mission.

An Invitation

Today Jazz comes out of stealth. Over a year of building, deploying with design partners, maturing to over a dozen fully licensed customers, running real investigations that found real data theft, learning from the most demanding security programs in the world.

DLP has been broken for twenty years. Every security leader knows it. Many have been burned by it. Some have been paralyzed by it. And today, for the first time, there's a path forward that works.

If you tried DLP and got trapped — we built this for you.

If you tried and failed — we built this for you.

If you never tried because you knew it wouldn't work — we built this for you.

Join us on our journey to establish what should always have been a core tenet of every security program, but only now is finally in reach.

DLP is broken. It's time to remaster it.