Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

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.

As Winter Storm Fern made its way across the US this weekend, children across the country were glued to phones, computers, or televisions as they tried to guess how long they would be out of school this week. Little do they know, however, the data, science, and lack thereof, that goes into that decision. School closures are the very public end of a complex and fast-changing dataset that is highly dependent on locality and can be wildly different on either side of a district line.

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).