Resources

Insider Threat Indicators: Behavioral and Digital Warning Signs to Watch

Insider threat indicators are the signs a trusted user is putting data at risk. Here are the behavioral, digital, and financial ones, and how to act.

July 20th, 2026

Shadowy figures representing insider threats.

DLP Strategy & Trends

People looking out through binoculars, representing search for data leaks.

AI Tools & Data Risk

How to Prevent AI Data Leaks at Work

Most AI data leaks are accidental, and they slip past traditional DLP. Here’s how to prevent AI data leaks at work without blocking AI outright.

July 17th, 2026

Series of triangles with exclamation points, representing prompt injection.

AI Tools & Data Risk

What Is Prompt Injection? Types, Examples, and How to Stop It

Prompt injection is when hidden instructions hijack an AI’s behavior. Here’s how it works, the main types, real examples, and how enterprises stop it.

July 16th, 2026

News & Announcements

Introducing a Data Loss Threat Model for the Age of AI

ORION Security’s Data Loss Threat Model maps how sensitive data can move and leave an organization, from traditional DLP paths to AI-native ones like MCP and shadow AI.

July 15th, 2026

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DLP Strategy & Trends

DLP Threat Modeling for the Age of AI

A fundamental change in how data moves inside organizations occurred in the last year. The use of generative AI tools is increasing, with AI becoming embedded directly in everyday workflows. Tools like Microsoft Copilot, ChatGPT, Gemini, and countless AI-powered agents are integrated into browsers, SaaS apps, developer environments, and internal systems. In many cases, enabled

April 3rd, 2026

DLP Strategy & Trends

The Great DLP Reset: Why DLP Fails, and How to Fix It

Cybersecurity analyst Lawrence Pingree shares new research on the DLP reset at an ORION Security webinar.

May 20th, 2026

News & Announcements

From Data Visibility to Real-Time Protection: WIZ – ORION Partnership Announcement

Company data is scattered across countless databases, storage buckets, and applications, spanning multiple clouds and on-premises systems. That’s the reality of every modern organization, whether we like it or not. In such a complex system, having visibility into where your data resides is more important than ever – but it’s just one part of the challenge. Once

April 3rd, 2026

Agentic DLP

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

April 3rd, 2026

A chess board move that represents DLP moving ahead of DSPM for cybersecurity deployments.

DLP Strategy & Trends

Why DSPM Doesn’t Need to Come Before DLP

Conventional wisdom says deploy DSPM first, then DLP, but that leaves organizations without enforcement for months. Agentic DLP changes the game.

June 3rd, 2026

DLP Strategy & Trends

Deepfake North Korean Remote Workers: a New Data Loss Threat

Deepfake remote workers from North Korea have been making cybersecurity headlines for a while now. Using fabricated identities, AI-generated résumés, and even deepfaked video calls, these malicious agents secure jobs at Western companies. Once inside, they operate like trusted employees while carrying out their true mission: stealing sensitive intellectual property and exfiltrating customer data. What

April 3rd, 2026

DLP Strategy & Trends

Why ORION Security Is Leading the DLP Reset

Independent research confirms DLP is undergoing a reset. ORION Security CTO and Co-Founder Jonathan Kreiner explains what’s driving it and what autonomous prevention looks like in practice.

June 4th, 2026

DLP Strategy & Trends

Finding Intent: The Three DLP Hazards Every Security Team Must Know

Most DLP tools fail for a simple reason: they’re built to look at a single aspect of data loss. Sensitive data leaks out of organizations for three main reasons: human error, insider risk, and external attackers. Each behaves differently, requires a different approach to recognize and solve, and has a different frequency vs. impact curve. A DLP policy

April 3rd, 2026

AI Tools & Data Risk

DLP for AI Agents: Securing Autonomous Workflows

DLP for AI agents watches the data an agent reads, carries, and sends, and stops sensitive content from leaving before it reaches an external model or service.

June 29th, 2026

Insider Threat Indicators: Behavioral and Digital Warning Signs to Watch

How to Prevent AI Data Leaks at Work

What Is Prompt Injection? Types, Examples, and How to Stop It

Introducing a Data Loss Threat Model for the Age of AI

Get smarter about data loss in the AI era.

DLP Threat Modeling for the Age of AI

The Great DLP Reset: Why DLP Fails, and How to Fix It

From Data Visibility to Real-Time Protection: WIZ – ORION Partnership Announcement

Indicators of Data Loss: The Path to Fully Automated DLP

Why DSPM Doesn’t Need to Come Before DLP

Deepfake North Korean Remote Workers: a New Data Loss Threat

Why ORION Security Is Leading the DLP Reset

Finding Intent: The Three DLP Hazards Every Security Team Must Know

DLP for AI Agents: Securing Autonomous Workflows

Data loss, prevented