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The latest News and Information on Data Security including privacy, protection, and encryption.

How to Build Custom Data Detectors Without Regex: DLP for Context-Aware Detection

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.

Nightfall Forensic Search Demo: Complete Insider Risk Investigation in Minutes

See how security teams reconstruct insider risk investigations with Nightfall's new Forensic Search feature, going beyond policy alerts to uncover the complete story behind every potential threat. In this 15-minute demo, watch three real-world investigation scenarios: Departing engineer exfiltrating code to personal cloud storage Sales associate moving customer data to USB devices CFO accidentally using shadow IT with sensitive financial data.

Effortless Data Security: From Discovery to Enforcement on a Single Platform

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.

Beyond Pattern Matching: How AI-Native File Classification Solves Modern DLP Challenges

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.

Welcome to the Protegrity Developer Edition Set-up Series

Stop struggling with complex security setups and get straight to building with the Protegrity Developer Edition. Our demo series, hosted by Dan Johnson, shows you how to deploy a full, self-contained data protection environment on your local machine in under 15 minutes using GitHub and Docker. You will learn to master everything from PII discovery and automated redaction to advanced encryption and semantic guardrails for AI workflows.

How to Secure Sensitive Data in Jira & Confluence with DLP (Data loss prevention)

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.

Nightfall DLP 2026: Corporate v. Personal Session Differentiation | Live Demo

See the future of data loss prevention in action. This live demo showcases Nightfall's breakthrough session differentiation technology that intelligently blocks sensitive file uploads to personal cloud accounts while seamlessly allowing them in corporate environments.

Semantic Guardrails for AI/ML - Protegrity AI Developer Edition

In this installment of our AI Developer Edition Set-up series, Dan Johnson, a software engineer at Protegrity, introduces semantic guardrails. Learn how to protect your LLM and chatbot workflows from malicious prompts and insecure AI responses. As AI becomes central to enterprise operations, controlling the context of conversations is a major challenge. Semantic guardrails provide a safety layer that ensures your AI stays on topic and never leaks sensitive PII.

AI-Powered Data Detection That Actually Works: 95% Precision, Zero Regex | Nightfall Product Launch

Tired of drowning in false positives? See how Nightfall's AI-powered detection achieves human-level accuracy and makes DLP automation possible. See three breakthrough capabilities from Nightfall: Prompt-based entity detectors - Protect custom IDs with natural language (no regex!) 23+ AI file classifiers - Detect source code, HR files, customer lists automatically Custom classifiers - Build your own in minutes with one sample file.