Legacy DLP Is Showing Its Age
Mike Vizard speaks with Ido Livneh, Co-Founder and CEO of Jazz, about why legacy data loss prevention tools are struggling in the age of AI. Livneh says many DLP programs were built for a different era. They often rely on rigid rules, pattern matching and manual review. That creates alert fatigue, blind spots and friction for employees who are trying to get work done.
AI Creates New Data Risks
The conversation explores why AI makes the data protection challenge more urgent. Employees now use generative AI tools, agents, personal cloud services and complex workflows that older tools were not designed to understand. AI-native DLP becomes more important as sensitive data moves through prompts, files, screenshots and automated actions. Livneh says security teams need to know not just what happened, but why it happened.
Context Changes the DLP Model
Livneh explains that Jazz uses an AI investigator called Melody to understand data movement in context. Instead of sending every alert to a human analyst, the platform looks at the data, the systems involved, the people taking action and the business process behind the activity. That context helps separate normal work from risky behavior. It also helps reduce false positives and makes DLP less disruptive for employees.
Security Teams Need Answers, Not Noise
The discussion also looks at how AI agents change the threat model. Agents can act quickly and use employee credentials across many systems. That creates new risks when guardrails are weak. AI-native DLP can help security teams place smarter controls around those workflows without blocking useful business activity.

Livneh says the goal is not to police employees. The goal is to help people work safely while protecting sensitive data. As organizations adopt more AI tools, data security needs to become more contextual, more automated and easier to operate. For many teams, that may mean rethinking DLP from the ground up.