Company's Solutions

For years, the story around Data Loss Prevention (DLP) has been one of failure.
This wasn't an accident.
DLP was built on a fundamentally flawed premise—a dumb framework designed for a different era.
It was never equipped to understand the context of modern work, leading to the noise, friction, and failure we see today.
Security teams today are stuck in one of two states - trapped by a noisy, failing legacy tool, or paralyzed by the fear of adopting one.

Our focus is on “The Paralyzed” (AKA Greenfield):
Modern, agile companies have wisely refused to adopt DLP, knowing it promises a guaranteed operational nightmare.
They consciously accept the immense risk of data loss because the pain of the "solution" is worse than the pain of the problem.
They are left dangerously exposed because the available tools are so fundamentally broken that choosing to use nothing is the smarter business decision.
But with the explosion of SaaS apps, the rise of remote work, and the uncontrolled adoption of Generative AI, the 'do nothing' strategy has gone from a calculated risk to a ticking time bomb.
The attack surface is no longer the network; it's every employee, everywhere.

Here’s the realization that changes the game:
The entire DLP model is broken because it creates a division between the machine and the human.
We tasked machines with using rigid rules to find potential problems, and then expected our human analysts to do the impossible: manually investigate and understand the context.
This model will always fail.
The only way forward is to make the understanding itself autonomous and ubiquitous, as a replacement to the rule-based system.
The first wave of so-called "modern" DLP didn't solve this.
They simply built a faster horse—applying AI to do better classification or better lineage.
They still operate within the flawed rule-based framework, inevitably creating noise and leaving you to do the hard work of finding the context of what actually happened.