Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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?

Modern applications generate a constant stream of logs, some of which carry more information than they should. For too many organizations, logs include personally identifiable information (PII) such as customer names that were never meant to leave production systems. Teams try to limit this data exposure by using regular expressions to detect and obfuscate matches, only to discover that names like John O’Connor, Mary-Jane, Jane van der Meer, and A. García slip through.

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.

For years, data security has been divided into artificial categories. Data Loss Prevention (DLP) focused on enforcement. Data Security Posture Management (DSPM) focused on discovery. Insider risk management lived somewhere adjacent. And now, AI security has arrived as yet another bolt-on.

In almost every major enterprise, Jira and Confluence are the default operating systems for innovation. They hold your organization's most vital intelligence, from product roadmaps to financial planning. Yet, while companies invest billions in fortress-like perimeter security, firewalls and VPNs, to keep external attackers out, they often ignore the fragility of their internal collaboration environments.

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.