Meta Description: Learn why traditional DLP is failing and how AI-native protection based on indicators of leakage finally fixes data control.
Enterprise DLP (Data Loss Prevention) has been failing for years
Traditional DLP has been relying on a simple premise: If we create enough granular policies, we can achieve full coverage and effectively protect our data from exfiltration.
In reality, data constantly grows in scale and complexity, and trying to manually put it into neat little boxes through static policies creates a system that’s impossible to manage and scale effectively. It requires constant upkeep and generates an unmanageable number of false positives, yet data continues to be exfiltrated.
Without a proper contextual understanding of how and why data flows within and outside your organization, a capability currently reserved for human analysts, DLP is bound to fail.
We’ve seen a similar problem before with antivirus tools that used to scan for bad signatures and stop them. Attackers moved faster than signature updates, and signature-based security systems struggled to keep pace under the weight of scale.
EDR tools stepped in, established behavioral baselines, flagged deviations, and solved the problem. They didn’t need to know what malware looked like – they just needed to know when something wasn’t right. Now, it’s time for DLP to have its EDR moment.
Indicators of Leakage (IOL): A shift in data protection
Indicators of Leakage (IOL) offers a new approach to DLP. If anti-virus tools versus EDR represent the leap from signatures to behavior, then traditional DLP versus IOL represents the leap from rules to reasoning.
IOL uses LLMs to classify data (both structured and unstructured, such as text, images, and code) and assess its level of sensitivity. It then classifies data in motion in a contextual way, providing a full, dynamic graph of all data movement.
Having understood how data moves within your organization, an IOL-based system can define the behavioral patterns of healthy data movement. This is determined based on the industry your company operates in, the standard activities of users within the same department, and those across the company as a whole.
Having analyzed these behavioral patterns based on context, intent, and relationships across documents, users, workflows, and environments, the system can monitor them for anomalous behavior without having explicitly defined a single policy.
IOL has thus allowed us to create the first zero-policy DLP. Instead of wasting time defining and managing manual rules, we now have a contextual understanding of how data moves within an organization. One that can be translated into proactive data protection.
The transition into an IOL-based approach
This new approach to DLP doesn’t necessarily mean replacing traditional solutions and pipelines outright. When positioned as a supplementary solution to legacy DLP, an IOL-based system can analyze its findings and, equipped with contextual understanding, drastically improve its results—reducing false positives and increasing the number of real detections.
Regarding the work of analysts, it’s easy to think of an IOL-based system as an AI agent that joins their team, helping them perform their job more effectively. Instead of having a dedicated team of DLP experts define policies and jump between 10 different tools to understand why an action was flagged, it gives them all the answers they need to make the most accurate final decision.
We believe that as IOL becomes more commonplace, it will enable DLP to become a fully automated process that requires metric-based reviews, rather than a hands-on approach, just like it happened with EDR.
A new, secure way forward
Implementing IOL finally eliminates two of the most pressing issues with data security: the infinite sprawl of manually created policies and the enormous number of false positives they create.
This new approach enables us to completely rethink how we approach data security in a smart, proactive, scalable way that aligns with the scale and complexity of data in today’s organizations.
This article was contributed by Nitay Milner, co-founder and CEO of Orion Security. Orion Security is the first zero-policy DLP solution for automating enterprise data protection. Powered by our proprietary “Indicators of Leakage” AI engine, Orion classifies, traces, and protects data with policy-free, AI-powered contextual reasoning, acting like an EDR for your data. By replacing policies, keywords, and patterns with AI-powered contextual reasoning, Orion prevents more data exfiltration incidents than policies ever could.
Orion is an AI-native DLP that prevents data exfiltration with a “zero-policy” approach. Powered by our proprietary “Indicators of Leakage” AI-engine, Orion automatically detects data incidents with context-aware accuracy, eliminating false positives and manual work. Orion brings a new approach to DLP – like EDR for your data. Already trusted by enterprises in finance, aviation, healthcare and beyond.