Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Legacy DLP operates on a fundamental constraint: it identifies sensitive data by matching patterns. Credit card numbers follow the Luhn algorithm. Social Security numbers conform to a nine-digit format. API keys match specific string patterns. This approach works for structured data, but it fails to address a critical reality: Your most sensitive assets aren't numbers. They're documents.

The security landscape is shifting. For the past two years, security teams have focused primarily on what users type into chatbots by monitoring interactions with ChatGPT, Gemini, and Claude. But a new risk vector is emerging, one that operates largely outside traditional security controls: AI agents accessing corporate data autonomously through the Model Context Protocol (MCP).

In May 2025, Coinbase disclosed a data breach that exposed nearly 70,000 customer records—not through a sophisticated external attack, but through bribed customer service agents. The cryptocurrency exchange refused a $20 million ransom demand and instead pledged that amount toward catching those responsible. One arrest has been made in India, but the incident highlights a fundamental problem in modern security: your people can become your greatest vulnerability.

The security landscape has shifted dramatically. Employees now work across dozens of applications, browsers, and devices—often using personal accounts alongside corporate ones. They're adopting generative AI tools at unprecedented rates, and your source code is moving between repositories faster than traditional DLP tools can detect. This creates a fundamental problem: how do you enable productive work while preventing corporate IP from leaving your trusted environment?

Large data breaches rarely begin with dramatic system failures. More often, they start with sustained, unauthorized access to sensitive data that goes undetected for months. The recent breach at Coupang, South Korea’s largest e-commerce platform, illustrates this pattern clearly. Nearly 34 million customer records were likely exposed over an extended period before detection.