Fireside Chat: Breaking Free from Legacy DLP

There’s a silent frustration building inside security teams today. It’s the fatigue of defending critical data with tools that can’t keep up. The friction of investigating endless false positives. The anxiety of not knowing what sensitive data is actually doing across your environment. And the sinking realization that despite massive investments, DLP tools are failing at the one thing they were designed to do–prevent data loss.

If you’ve spent any time managing or deploying traditional DLP, you know exactly what we’re talking about. Legacy DLP was built in an era when data sat still and was locked away in static files, behind on-prem firewalls, governed by rigid, policy-heavy frameworks. Back then, blocking a USB port or flagging attachments based on keyword regex might have sufficed.

But the world has changed. Data no longer sits still. It flows dynamically across SaaS apps, cloud storage, unmanaged devices, AI tools, contractors, and global teams working asynchronously. Employees want to work without friction, and the business demands speed. Meanwhile, threat actors exploit gaps in security controls not designed for this reality.

And so, traditional DLP is cracking under the pressure.

The Problem with Legacy DLP

Legacy DLP has essentially positioned security teams as blockers. Overbearing enforcers of rigid rules that broke legitimate workflows. A common fear in deploying any data protection solution is system stability. Legacy DLP agents are notorious for consuming memory, degrading app performance, and even crashing operating systems during deep scans or policy enforcements.

Ask any security leader about their experience with legacy DLP, and their feedback is consistent:

False positives everywhere: Relying on static regex, keyword dictionaries, or simple file tagging creates floods of alerts. Analysts waste hours triaging non-incidents, while real risks hide in the noise.

Brittle policies that break business processes: Tight policies block legitimate workflows, frustrating users and leading to constant policy exceptions or outright disablement.

Heavy agents that slow or crash systems: Legacy DLP agents consume resources, interfere with apps, and sometimes even crash endpoints. Security teams end up tuning them down to avoid user complaints, effectively nullifying protection.

Limited visibility into modern data movement: With work occurring in cloud SaaS apps, generative AI tools, and unmanaged devices, traditional endpoint-based detection can’t see data as it flows across modern environments.

Reactive instead of proactive: Legacy tools react to rule triggers rather than understanding the context, intent, and lineage of data movement to prevent breaches proactively.

As a result, many organizations have deployed DLP for compliance optics, but in practice, they are under-protected, over-burdened, and stuck with a tool that creates more problems than it solves.

Why Context Is the Missing Ingredient

Data protection today demands far more than static rules and pattern matching. It requires a deep, real-time understanding of context—what the data actually is, where it originated, how it has been transformed, who is accessing it, and why. Without this context, security decisions are little more than educated guesses. 

Traditional DLP tools see data as isolated artifacts. Things like a file on a desktop, a string of keywords in an email, or an attachment leaving the network. They have no insight into the true business value, risk, or purpose of that data. 

Context-aware platforms change this by treating data as living assets with rich histories. They connect dots across user behavior, data origin, and business processes to identify real risk versus normal workflow. This depth of understanding transforms security from reactive blocking to proactive, intelligent protection. Security teams are then empowered to focus on what truly matters and operate the business without unnecessary friction.

That’s the power of context-aware data protection. It shifts security from static policies to dynamic, informed decisions grounded in the true nature of the data and user behavior. By understanding intent, lineage, and behavior, policies become adaptive. Users work with fewer interruptions. Security teams focus on real risks. And business leaders see security as a force multiplier, not an impediment.

Introducing Our Fireside Chat

To explore this critical transformation, we sat down with Matt Webb of Zebra Technologies to talk about their journey away from outdated tooling. Our fireside chat, “Breaking Free from Legacy DLP,” dives deep into the why, the how, and the outcomes of moving away from legacy platforms to embrace modern, context-driven data security.

In our fireside chat, Matt and Cole Pedula of Cyberhaven discuss how modern platforms:

• Leverage efficient architectural designs to minimize CPU and memory footprint.
  
  
• Operate at the endpoint without intrusive scanning that destabilizes apps.
  
  
• Maintain business continuity with real-time, risk-based decisions instead of blunt force blocking.
  
  

Watch the Fireside Chat

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