“I don’t have visibility into what data is being shared with AI.”
That’s the line echoing through CISO conference rooms right now. But let’s be honest, did we ever really have full control over what data gets dropped into SaaS apps? Or pasted into personal emails? Or quietly exported from spreadsheets?
The truth is: AI didn’t introduce a new threat. It just forced us to confront how little control we’ve had all along.
What if AI isn’t the problem, but rather a mirror showing the problem to us? And more importantly: what could we achieve if we actually did know where our data lives, flows, and leaks in real time?
Data Protection Has Been Quietly Failing for Years
Enterprise DLP (Data Loss Prevention) has always run on the same fragile premise: that sensitive data can be perfectly defined, cataloged, and guarded through static policies, usually some cocktail of regex, keyword matching, or user tagging.
Sadly, real enterprise data isn’t clean. It’s unstructured, duplicated, reworded, visual, and contextual. It flows across unmanaged devices, unsanctioned apps, and human error at scale. Regex rules simply don’t survive contact with reality.
As one Fortune 500 CISO said:
“We started building our data classification model four years ago. We’re still not done. I’m done waiting, and I need something that just works.”
That sentiment isn’t rare. It’s systemic.
The Good News? We’ve Solved This Before: Data Security Needs Its EDR Moment
Antivirus (AV) tools used to be the gold standard. They scanned for known bad signatures and stopped them (and stopped your CPU every time you tried to open a spreadsheet). Meanwhile, in reality, attackers moved faster than signature updates, and signature-based security crumbled under the weight of scale.
Security needed a new model – one that didn’t rely on predicting every threat. That’s when EDR (Endpoint Detection and Response) stepped in and rewrote the rules.
Stop predicting what bad looks like, and start understanding what’s normal.
By establishing behavioral baselines and flagging deviations, EDR didn’t need to know what malware looked like. It just needed to know when something wasn’t right.
From IOA to IOL: The Paradigm Shift in Data Protection
EDR pioneered the concept of Indicators of Attack (IOA), signals that something malicious might be happening, even if no known signature matched.
Today’s modern data protection systems are doing the same with Indicators of Leakage (IOL).
Instead of trying to define every possible form of sensitive data, IOL-based systems look at:
- Who is sharing the data
- What data is being accessed or transmitted
- Where it’s going
- When and how it’s being shared
- And whether that behavior aligns with established norms
If AV vs. EDR was the leap from signatures to behavior, then DLP vs. AI-native protection is the leap from regex to reasoning.
LLMs Are the Solution, Not the Problem
Ironically, the very LLMs that sparked fears of data leakage are now the most promising tools to solve the broader, more systemic problem we’ve ignored for years: we never really had control over our data.
The panic over prompts and AI leaks simply forced us to confront a deeper reality: we didn’t know where sensitive data lived, how it moved, or who touched it. That’s not an AI problem. That’s a visibility problem, a decades-old one. LLMs are finally giving us a way out.
AI-native systems can now:
- Classify unstructured data (text, images, code) without brittle, hand-crafted rules
- Understand context, intent, and relationships across documents, users, and workflows
- Establish behavioral baselines across endpoints, SaaS, cloud, and email
- Detect anomalies, even when no policy explicitly defines what to look for
- Recommend precise protections based on how data actually flows, not how we assume it does
LLMs solve the entire legacy failure of enterprise data protection by exposing the blind spots we’ve normalized and offering a path to eliminate them.
Security Teams Should Be Leaning In, Not Pushing Back
Every hour spent writing regex or tuning DLP rules is time wasted on treating symptoms, not causes. The goal isn’t more policy. It’s a better signal. More context. Less guesswork. AI-based DLP allows security teams to focus on outcomes instead of syntax, where policies emerge from real-world behavior, not theoretical models. That means alert fatigue drops while detection fidelity skyrockets
AI Didn’t Break Data Security, It Just Exposed It
Trace how data flows, understand what’s sensitive, flag what’s abnormal, and respond before it becomes a breach.
Now, for the first time, we have the tooling to do that, at scale, with context, and without tedious manual work. The same force that triggered the panic, LLMs, may finally be the only way to fix what was broken all along.
