Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Investigation Gap in Traditional MDR

Most MDR providers stop at detection and escalation. Two new capabilities in BlueVoyant AI (BlueVoyant's MDR platform), Cloud Forensics and Device Forensics, extend the service into active investigation, giving analysts the tools to determine what happened during an incident rather than simply flagging that one occurred. When an incident requires traditional forensic investigation, these same tools provide a direct transition into that process.

The Definitive SOC 2 Compliance Guide

Every day, service organizations handle sensitive customer information covered by data protection laws and subject to security compliance requirements. In the business-to-business world, customers require that their vendors provide validation and assurance over their privacy and security controls, typically asking for independent third-party attestations and reports.

From Paralysis to Action: Why First-Wave DSPM Left Security Teams Drowning in Data They Could Not Use

Boards are investing more in data security than ever before. Analysts have declared data security posture management (DSPM) one of the fastest-growing categories in cybersecurity. And yet CISOs across industries are standing in front of dashboards filled with findings, flags, and risk scores, completely unable to move to action.

Take Command of Risk: Operationalizing CTEM with SafeBreach Helm

Take Command of Risk: Operationalizing CTEM with SafeBreach Helm AI has fundamentally changed the threat landscape. Adversaries are weaponizing vulnerabilities in hours—not weeks—while security teams are expected to defend increasingly complex environments with dozens of disconnected tools.

Qubits vs Classical Bits - Understanding Quantum Computing For Your Data

Einstein called quantum entanglement "spooky action at a distance," but it’s actually a fundamental lesson in how particles share identical information. While classical bits are limited to 0 or 1, quantum bits (qubits) exist in a combination of both until the moment they are measured. Understanding these complex data states is essential as we move toward a future of quantum-resistant security and advanced AI. At Protegrity, we are dedicated to protecting your most sensitive data across every stage of the evolving digital landscape.

LangGraph Integration for Protegrity AI Developer Edition

See how Protegrity AI Developer Edition helps protect sensitive data in AI agent workflows built with LangGraph. This demo shows how Protegrity can fit into modern AI development pipelines as both a preprocessor and postprocessor guardrail, helping teams discover, protect, tokenize, mask, and redact sensitive data before it reaches an LLM — and before responses leave the application. In this video, you’ll learn how developers can.

Protegrity Browser Protector: Zero-Trust Data Security for Web Apps (MV3)

Are you struggling to extend data protection to modern web applications without the friction of custom SDKs? Zero-Trust architecture and AI governance are no longer optional—they are operational imperatives. The Protegrity Browser Protector is a Manifest V3 Chrome extension designed to solve the "last mile" of data security. It allows authorized users to protect, unprotect, and mask sensitive information (like SSNs, PII, and financial data) directly within any web browser—without modifying the underlying application code.

How we tripled Persona's Marketplace integrations in under a year

When I joined Persona's Marketplace team as the product manager in August 2025, we had around 25 integrations. Our goal was to make Persona a seamless fit in every customer's stack: easy to get started with and even easier to grow with. Less than a year later, we've tripled the size of our marketplace to include more than 75 integrations. Here's how we've approached our Marketplace strategy this year.

Best AI Security Tools for 2026 (Top 10 Compared)

Enterprises today are looking to grow faster by adopting artificial intelligence. Teams are now building AI copilots, automating workflows with AI agents, and using Retrieval- Augmented Generation (RAG) to search internal knowledge bases. However, with every successful AI deployment, there is one very important question. How do you keep sensitive enterprise data from becoming a potential AI security risk?