The Market Spoke Before We Did

While we were building, security leaders across industries — from financial intelligence platforms to universities to a publicly traded insurance company — started adopting Jazz. Not because of a launch event or a press cycle, but because they had a problem no one else was solving, and Jazz actually solved it.

These are their words.

The Problem Everyone Shares

Every security leader we spoke with described the same experience: DLP has failed them. The tools built to do it were fundamentally broken.

Rule-based systems that generated endless false positives. Noisy alert machines that buried real risk under mountains of irrelevant results. Tools so heavy and invasive they disrupted the very people they were supposed to protect.

The result? Many modern companies made a calculated decision: use nothing. The pain of legacy DLP was worse than the risk of going without it.

That’s the market Jazz walked into. And that’s the market that’s now responding.

AlphaSense: “DLP Was a Noise Machine. Jazz Is Actionable Intelligence.”

Pieter VanIperen, CISO at AlphaSense

AlphaSense is a leading enterprise, market, and financial intelligence platform. Their entire product is data-driven — and protecting that data is, as Pieter puts it, “quintessential to what we do.”

Pieter had tried every DLP solution available. The verdict was always the same:

“DLP was a noise machine. That’s really what it was. Every solution we tried, we turned it on. You had tons and tons and tons of results, 99% of which were false positives.”

The core issue, he found, was architectural. Legacy DLP relies on rigid rules, and business doesn’t work that way.

“A lot of it was really just a dumb rule system. And business is complicated. Data is complicated. Rules don’t fit that very well.”

What caught Pieter’s attention about Jazz was honesty. Jazz didn’t claim to be a better version of the same broken model — it acknowledged the model itself was the problem.

“Jazz very openly said, DLP doesn’t work — which was something interesting to hear from a DLP company. But I thought it was a very realistic point of view.”

The difference in practice: instead of demanding rules and regex patterns, Jazz asks for your policies in plain language — then learns your business from the data itself.

“Jazz is the first solution that’s really saying, don’t give me rules, don’t give me policies. Give me data, and I will figure out what should be going on with the data. It’s the first solution that’s saying, I want to understand your business.”

The outcome for AlphaSense was immediate and tangible. Jazz didn’t just reduce noise — it delivered intelligence they could act on.

“It’s not yesterday’s DLP. It’s actionable intelligence in our hands. And it allows us to know when to respond and when not to respond and how to respond in a way that I haven’t seen in my career.”

“Jazz is the closest thing I’ve seen to a successful synthetic employee since AI has been AI.”

Pieter’s closing line speaks for itself:

“If someone out there thinks that DLP doesn’t work, I would say they haven’t tried Jazz.”

Lemonade: “Anyone Who Thinks DLP Doesn’t Work Is Right — Until You Look at Jazz.”

Jonathan Jaffe, CISO at Lemonade Insurance

Lemonade is a publicly traded, full-stack insurance company serving property and casualty customers. For Jonathan, data protection is non-negotiable — it means protecting not just intellectual property, but first and foremost, customers’ personal data.

Jonathan had looked at every DLP product on the market. Every time, the same thing stopped him:

“Every time I looked at a DLP product, I was overwhelmed with the number of false positives — and also the reaction that our employees would have, which is pop-up notices and being blocked from doing things. In the end, none of those products ever suited our needs.”

His assessment of the category was blunt:

“DLP solutions, before Jazz, are just a waste of security team time.”

Jonathan came across Jazz through a founder presentation. What stood out was the use of generative AI — an approach he hadn’t seen in any DLP product. He was skeptical. Then he tried it.

“I was very skeptical, but after giving it a try and tuning it using natural language, pretty quickly it showed how good of a product it is.”

What convinced him was how Jazz understands context — not just data patterns, but the full picture of who, what, where, and why:

“It understands context, and it actually understands who the user is, the information that might be moving around between applications, the destination of where that information may go, the person’s role in the company and what he or she has to do — and with all of that context, it makes great decisions.”

Jonathan’s favorite feature is Melody, Jazz’s Agentic Investigator and Co-Pilot that turns alert triage into a conversation:

“Melody allows me to work on an alert using natural language. I can tell Melody, this alert is okay because of this person’s role. Melody will then turn that into a policy and ask me if I want to apply that policy to all people of similar roles. And I just say yes, and like magic, it’s done.”

Beyond detection, Jazz quietly changed how Lemonade’s employees handle data day to day:

“Some of the value that we get out of Jazz is in the way it gently coaches people on how to treat data correctly. It’s been fantastic for that.”

Jonathan now recommends Jazz to his peers — with a line that captures the shift:

“You’ve been burned by DLP before, of course. Try this, it will change your mind.”

UCLA: “Almost Right Away, We Were Starting to See the Value.”

Howard Miller, CIO & Mark Corlew, Director of IT Security — UCLA Anderson School of Management

Higher education presents a unique data protection challenge. Faculty members’ research is their life’s work. Losing it would be, as Howard describes, “detrimental to them in so many different ways.” It’s a problem he always knew he needed to solve — but the available tools weren’t up to it.

The UCLA Anderson team had tried other DLP solutions. The experience was telling:

“They felt a little too big brotherish.”

In an academic environment, that matters. Faculty notice when software hits their machine — “especially if they deem that their system performance is, in their mind, even a nanosecond slower than what it was the previous day.”

This is where Jazz stood apart on a practical level:

“Probably what was most salient about Jazz and differentiating is the fact that it runs with a really, really small footprint.”

But lightweight deployment was just the entry point. The real shift was in what Jazz could actually detect. Mark describes a scenario no legacy tool could handle:

“Hey, did you know that user X is taking screenshots of sensitive data? Well, that’s something that we never would have been able to detect with any other sort of DLP tool.”

Jazz also eliminated the noise that had made previous tools unusable for their small security team:

“They generated a lot of false positives, which for us as a very small security team is maddening.”

With Jazz, the signal-to-noise ratio flipped. When Jazz raised an alert, the team knew it warranted investigation. When it didn’t, they could trust the silence.

“When Jazz does let us know that there is something happening, we know that that’s something we need to investigate.”

The value showed up fast — and the team sees it as a long-term investment:

“We have a tool that will not only do the things that we needed to do today, but forecasting and foreshadowing, we see that the tool will just grow with us and will continue to be a linchpin within our security portfolio.”

UHSP: “The System Almost Sort of Knew What We Needed.”

Zach Lewis, CIO & CISO — University of Health Sciences and Pharmacy

UHSP is a 162-year-old private institution in the Midwest. Zach wears both the CIO and CISO hats, overseeing all of IT, security, and information protection. His team is small. Their data protection needs are not.

Higher education data is uniquely difficult to classify with traditional tools. Zach illustrates this with a deceptively simple example:

“If you’ve ever tried to use a tool to identify a letter grade like an A or B — which we have plenty of — it’s almost impossible to do.”

Legacy DLP tools couldn’t adapt to this context. The result:

“We found it to kind of be useless in most aspects.”

What Zach needed was a tool that could understand context without requiring his team to spend their days writing and tuning rules.

“I don’t have a big team, so I didn’t want to have them sit around writing rules. They don’t want to sit around writing rules. We have other projects to work on.”

Jazz delivered exactly that. The AI-powered approach meant context came built in:

“The system almost sort of knew what we needed and gave us that information in a very easy, digestible manner.”

Deployment was seamless, and value was immediate:

“Immediately once those agents deployed, they were able to start reporting back in — what they were seeing, what was the data doing, where was it going, who was accessing it — and started cutting out all the noise of false positives.”

Zach describes a real finding that illustrates what contextual understanding actually looks like in practice: Jazz detected a user entering student grade data into an unsanctioned Google Doc — something that would look like nothing to a rule-based system (“That’s just a user entering A’s, B’s, C’s into a document. That’s nothing to them.”) but was actually a FERPA compliance risk.

“With Jazz, knowing that this person has access to academic information, hey, they’re entering what looks like grades into an unsanctioned application — well, that’ll trigger an alert for us.”

The Agentic Investigator was a differentiator Zach hadn’t seen anywhere else:

“The AI reads what data is being used, who’s using it, where it’s going. It can actually give you investigative insights on what’s happening from start to finish and why it triggered that alert. That’s taking off the investigative load of the team.”

“It’s like a whole other member of the team doing all this back-end work that no one really wants to do, but giving us real insights that we can act on.”

When Zach presented Jazz to his leadership team, the reaction was unambiguous:

“After the first presentation to a couple members of our leadership team, they were sold. ‘This is absolutely something we have to have. We can see use cases here.’”

What This Tells Us

Four organizations. Different industries, different sizes, different data challenges. The same conclusion.

Legacy DLP is broken — not because of poor execution, but because of a flawed model. Rules can’t capture the complexity of how businesses actually work with data. And security teams can’t be expected to manually investigate the mountain of noise those rules produce.

Jazz works because it starts from a different premise: understand the business first, then protect it. No rules to write. No months of tuning. No walls of false positives. Just clear, investigated answers about what’s actually happening with your data.

These customers found Jazz while we were still in stealth. They stayed because it worked.

Now it’s your turn to see what they saw.