Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Everyone's racing to use AI right now. But securing AI adoption while maintaining productivity—getting visibility into shadow AI, educating employees without blocking innovation, and building governance that actually works—is harder than it looks. We recently hosted a discussion between Anant Mahajan, Head of Product at Nightfall, and Yunique Demann, VP of Information Security at TPx, to dig into the practical realities of AI governance.

The Workday breach isn't just another security incident—it's a blueprint for how modern attackers are systematically dismantling traditional Data Loss Prevention (DLP) strategies. When a company renowned for security excellence falls victim to social engineering that bypasses every conventional control, it's time to fundamentally rethink your approach.

This summer has been big for Nightfall. From launching Nyx, our AI copilot for DLP, to expanding our detection capabilities across more platforms, we’re making it easier than ever for security teams to protect sensitive data without slowing down work. In this update, you’ll find new AI-driven features and platform enhancements designed to make your DLP workflows smarter, faster, and more precise.

Everyone’s racing to build copilots right now. But making an agentic AI that feels like a trusted teammate—one that understands context, acts safely, and simplifies complex workflows—is harder than it looks. While building Nyx, our agentic AI copilot for security teams, our team spent a lot of time thinking about how to make her an effective team member - skilled and trustworthy.

Shadow AI refers to the unauthorized or unmonitored use of AI tools (like ChatGPT, Copilot, Claude, and Gemini) by employees in the workplace. It’s now one of the fastest-growing data exfiltration vectors. Employees are pasting source code, customer or patient data, contract terms, and even M&A info into gen AI tools, often without realizing the risk. And many legacy DLP tools are still catching up.

Your organization runs on data. But do you actually know where it goes every day? Between Slack messages, Google Drive shares, AI assistants, and browser uploads, your sensitive data is constantly moving: Every one of these moments is a data exposure risk.

For years, data loss prevention has been synonymous with pain: These legacy approaches treat every potential incident the same, forcing teams to waste time deciphering what really happened and why it matters. Meanwhile, real risks slip through the cracks because no team can manually keep up.

As AI transforms the security landscape, security leaders face a critical decision.

Sensitive data is zooming across dozens of platforms every day, from Slack to email to gen AI platforms and many more sources. We all need this connectivity to stay productive, but the connectivity also creates countless opportunities for data to slip through the cracks. A single misplaced email attachment can end up exposing confidential information in a matter of seconds.