Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every new AI feature that ships into a platform your employees already use is a security question your stack probably can't answer yet. It sounds like hyperbole, but it's the structural reality of how AI adoption works in 2026. A recent update to WhatsApp is a useful illustration of why.

A sales rep opened Glean—an AI-powered enterprise search platform that connects to your company's SaaS apps and lets anyone query across all of them in natural language—typed "Who are my top 10 customers?" and got a clean, formatted list pulled from Salesforce, cross-referenced with HubSpot, and confirmed against data sitting in Google Drive. They copy-pasted that list into a personal Gmail draft. No alerts fired. No policies triggered. No one noticed. This isn't a hypothetical.

When Anthropic announced Claude Code Security on February 20th—a tool that scans codebases for vulnerabilities and suggests patches for human review—the reaction from markets was swift and brutal. Major cybersecurity names watched their stock prices fall by double digits within days. The implied thesis behind the selling: AI can now do what these companies do, so why pay for them? It's a compelling fear and an inaccurate conclusion at the same time. The DLP space is a clear example of why.

For 83 days, attackers moved freely through Conduent's systems and exfiltrated 8 terabytes of healthcare records, Social Security numbers, and personal data belonging to tens of millions of Americans. No alarm sounded. No transfer was blocked. The breach was discovered when systems stopped working. Not because anyone detected the data leaving.

Traditional data loss prevention stops at detection. You get an alert. You know something happened. But you don't see the full picture. When a departing engineer downloads your entire codebase over the holiday break, you need more than a policy violation. You need to see what they were doing before that moment, where the data came from, and what happened after. You need context, timeline, and the ability to trace every action.

The exfiltration problem has evolved beyond what traditional DLP was designed to solve. Your employees work across personal AI assistants, multiple browsers, dozens of SaaS applications, and offline environments. They collaborate through Git, communicate via email clients, and store data on external drives. Each interaction represents a potential data loss vector—and legacy solutions can't see most of them.

When WorldLeaks claimed to have exfiltrated 1.4TB of Nike's corporate data—188,347 files containing everything from product designs to manufacturing workflows—the incident revealed something more significant than another headline-grabbing breach. It exposed a fundamental gap in how organizations approach data loss prevention. The breach reportedly included technical packs, bills of materials, factory audits, strategic presentations, and six years of R&D archives.

The acting director of America's Cybersecurity and Infrastructure Security Agency—the person tasked with defending federal networks against nation-state adversaries—triggered multiple automated security warnings by uploading sensitive government documents to ChatGPT. If this happened at CISA, it can happen at your organization too.

For years, data loss prevention has meant one thing: finding sensitive entities. Social Security numbers, credit card numbers, API keys—if you could pattern-match it, you could protect it. But this approach has always had fundamental limits. What happens when you need to protect customer IDs unique to your business? What about proprietary source code that doesn't contain any traditional PII?

DLP systems have traditionally relied on regex pattern matching to identify sensitive information. While regex excels at finding patterns, it fundamentally can’t understand context. It’s a massive limitation that forces security teams into endless cycles of tuning expressions and triaging false positives. Nightfall AI built prompt-based entity detection to solve this problem.