Indicators of Data Loss: The Path to Fully Automated DLP

For the past decade, Data Loss Prevention (DLP) tools and platforms have been built on thousands of human-authored policies intended to cover every possible way sensitive data might leave the organization.

Traditional DLP implementation starts with defining policies. The issue is that it also usually ends there – often without ever leaving “monitor-only” mode. Implementation requires countless efforts from the security team, only to result in a flood of false positives while data still leaks.

Maintenance and false positives are just symptoms of the real problem: Policy reliance.

In this blog, we will explain how ORION goes beyond policies through automated data-loss indicator analysis, helping you achieve true, efficient DLP coverage.

DLP Policies are not Enough

Policies, by nature, focus on what is happening rather than why.

This means they protect against threats you already know to expect – they cannot detect or prevent new or evolving forms of data loss.

The ability of policies to cover broad, deterministic use cases makes them well-suited to meet compliance standards that often require all-encompassing, coarse-grained rules.

But when policies are your entire DLP strategy, they consistently fail to cover every edge case. They’re bound to be too granular or too broad, resulting in a constant flood of false positives and missed exfiltration incidents.

As your organization continues to grow, the destinations for data exfiltration grow exponentially in both number and complexity. This is especially true with the rise of AI-driven workflows, as new, unprecedented ways for sensitive data to leave the organization unnoticed emerge every day.

Modern DLP vendors try to fix this by adding more policies or using AI to generate new ones – but more rules don’t solve the core issue: policies alone will never provide complete DLP coverage.

Data Loss Indicators

To overcome the limitations of policies, ORION takes a different approach to DLP through automated Data Loss Indicator analysis.

A data loss indicator is a contextual signal, such as unusual timing, unexpected destinations, identity mismatches, or abnormal data volume, that suggests a user’s action may be inappropriate or risky, even if it technically matches allowed behavior.

Instead of asking what is happening (“a file was uploaded”), indicators help answer why it is happening and whether the action aligns with normal patterns.

To put it simply, if anti-virus tools versus EDR represent the leap from signatures to behavior, then traditional DLP versus data loss indicator analysis represent the leap from rules to reasoning.

Unlike traditional DLP alerts triggered by predefined policies, data loss indicators are uncovered by ORION’s set of specialized AI agents through continuous observation of how data moves across your environment.

These specialized agents collect and analyze a wide range of contextual information for every data trace in your organization and analyze whether the behavior aligns with expected data movement patterns. From this foundation, ORION can detect subtle signals that static policies could never capture.

The shift from activity-based monitoring to intent-aware detection enables security teams to distinguish between normal business operations and actions that may indicate exfiltration, whether purposeful or accidental.

Security teams don’t need to manually define data loss indicators; the system learns them dynamically. Teams can enrich the database when needed, but the heavy lifting is automated.

To understand how ORION forms these indicators, it’s helpful to look at the agents that collect and interpret the underlying signals.

ORION’s Agents

ORION comprises six specialized agents. Five agents collect core contextual signals, and a sixth agent analyzes them to detect abnormal or risky behavior. Together, they enable a holistic and intent-aware approach to DLP.

A detailed overview of use cases our agents know how to cover can be seen here.

Below is a breakdown of each agent’s role:

Data Classification Agent

The Data Classification Agent analyzes both structured and unstructured data, classifies and tags its content, assesses its sensitivity level, and generates a concise summary.

Tagging often includes labels such as PCI, PII, HIPAA, Secrets, Code, Product, Marketing Materials and more.

ORION also supports custom classifications via simple prompts, allowing teams to tailor classification to their organization’s unique data landscape.

This ensures that every indicator accounts not only for how data moved but also for the type of data at risk.

Data Lineage Agent

The Data Lineage Agent collects and maps all data movement traces within the organization, including:

• The source of data (Storage, codebases, cloud, etc.)
• The type of action performed (Download, copy/paste, zip, encrypt, rename, send, and more)
• The destination (AI tools, personal communications channels, devices, browsers, etc.)

A detailed list of potential sources, destinations, and actions can be found on the Use Cases page.

Combined with the Identity Agent, lineage determines whether an action deviates from typical usage patterns.

This agent provides the behavioral context needed to understand whether movement is routine, anomalous, or suspicious.

Identity Agent

The Identity Agent integrates with IDP and HR systems to extract identity attributes such as title, department, seniority, tenure, and potential departure status. Indicators incorporate not just what happened but who did it and whether that person’s role makes the action reasonable.

Environment Agent

The Environment Agent collects environmental signals such as geography, site location, network zone, and working hours. These signals help determine whether the timing and location of data movement align with legitimate work patterns.

External Relation Agent

The External Relations Agent connects to CRM platforms and extracts customer and vendor information, including BAAs, contracts, and permitted data-sharing levels. This ensures ORION knows whether a destination is legitimate, expected, or unapproved.

Analysis Agent

The Analysis Agent aggregates all collected signals, transforms them into data loss indicators, and detects deviations from expected behavior.

Examples include:

• Scope creep in data access: A developer who normally works in one repository suddenly starts pulling large amounts of data from systems outside their typical scope.
• Unusual destinations for sensitive files. An employee transfers sensitive files to a personal email, an unknown domain, or a storage provider not commonly used by the organization.
• Outlier behavior within a role: A member of the finance team downloads significantly more customer data than peers in the same role, despite no change in responsibilities.
• Suspicious timing and location: A late-night upload from a country where the employee doesn’t typically work, combined with an unusual spike in data access, makes the action high-risk.
• Slow-drip exfiltration: A user moves small volumes of data in ways that seem harmless individually but, when combined, form a clear pattern of data siphoning.

Once an indicator suggests risk, ORION can automatically block, prompt, or notify based on predefined sensitivity settings.

Policy Support – Without Policy Reliance

The shift away from policies doesn’t mean abandoning the concept completely. Policies remain valuable for compliance and deterministic scenarios. ORION’s Policy Engine stores predefined manual policy definitions, reviews them, and suggests enhancements and new policies when needed.

The Benefit of Data Loss Indicators

By analyzing data loss indicators rather than relying solely on static rules, ORION understands the intent behind user behavior in the context of identity, data sensitivity, environment, and organizational norms.

This enables ORION to provide an automated DLP solution at a much larger scale, with far better accuracy, drastically reduced false positives, and, most importantly, the ability to continuously detect and prevent new forms of data exfiltration – not just the ones you anticipate.

ORION’s agents work alongside security analysts, cover every single data movement in the organization, and help them do a better, more effective job on an unprecedented scale, while constantly learning and evolving their reasoning over time.

It’s Time to Move On

The shift from relying on static, human-authored policies to data loss indicator analysis represents the next natural step in DLP – one that brings context, intent, and real-time reasoning into the heart of data protection.

By focusing on why actions occur, ORION delivers the visibility and precision needed to stop both accidental leaks and sophisticated exfiltration attempts at scale, closing the blind spots that policies have never been able to cover.