Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

With Mimecast's acquisition of Code42, enterprise security teams are discovering what many already suspected: their insider risk platform has fundamental limitations that no amount of tuning can fix. Real customers are reporting critical gaps that leave organizations vulnerable, while security teams drown in noise and manual processes. If you're experiencing these frustrations, you're not alone.

Nightfall leads the 2025 Mac DLP market with its API-first, cloud-native architecture that aligns perfectly with Apple's modern security model, offering real-time protection across SaaS, AI tools, and endpoints without the kernel-level complications of legacy solutions. This comprehensive guide evaluates all top 10 solutions and answers the critical questions security teams need to make informed decisions.

Nightfall leads the 2025 Windows DLP market with its unified cloud-native architecture that delivers enterprise-grade endpoint protection without the bloat of legacy solutions, offering real-time AI-powered detection across all exfiltration vectors with >95% precision. This comprehensive guide evaluates the top 10 solutions and answers critical questions security teams need for Windows data protection.

Key insights from a fireside chat between Nightfall CEO Rohan Sathe and cybersecurity veteran Enrique Salem, Partner at BCV and Nightfall investor Twenty years ago, enterprise security teams scrambled to address shadow IT as employees brought consumer applications into the workplace. Today, we're witnessing the same phenomenon with AI tools—what we now call shadow AI. The fundamental question remains unchanged: What happens to our data?

The traditional network perimeter is dead. Your sensitive data now travels paths that legacy DLP solutions can't see—from Salesforce to Google Drive, across laptops, into personal Dropbox accounts, and through AI chatbots. No single traditional DLP catches all of this. We're at a turning point where shadow AI and rapid data movements expose blind spots that legacy solutions simply can't address. The reality?

AI has become essential infrastructure for modern business. What started as pilot programs has evolved into production deployments across business functions, fundamentally changing how work gets done. While this transformation drives significant productivity gains, it creates a fundamental security challenge that traditional data loss prevention (DLP) approaches can't address.

It's 8:47 AM. Your phone buzzes with another "urgent" DLP alert. You've already ignored three this morning. This one screams "SENSITIVE DATA DETECTED" in all caps. But it’s just a lunch menu with a credit card number for catering. You silence the notification and grab your coffee. What you don't know? While you're dismissing false alarms, your VP of Finance just dropped next quarter's earnings in a public Teams channel. Your DLP system? Completely silent.

The cybersecurity industry woke up to yet another supply chain nightmare this week. Cloudflare, one of the world's largest web infrastructure companies, confirmed that attackers accessed 104 of their API tokens through the cascading Salesloft Drift breach. This incident perfectly illustrates why modern organizations need to rethink their approach to third-party vendor security.

The recent lawsuit filed by xAI against former engineer Xuechen Li should serve as a critical wake-up call for every CISO. When a trusted engineer can allegedly download proprietary Grok IP, and jump to a competitor, it exposes fundamental gaps in how we protect our most valuable digital assets. This isn't just about one rogue employee. It's about the reality that your company's most sensitive data is at the risk of exfiltration every day—in laptops, SaaS and AI apps, endpoints and browsers.