Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

AI Risk Management as a Function of AI Governance: A Holistic Approach

Artificial intelligence (AI) is transforming industries, but it also introduces new risks that organizations must manage. Effective AI risk management is a critical function within AI governance. This article explains how AI risk management fits into the broader governance framework, why it matters, and how organizations can adopt a connected, data-driven approach to reduce AI-related risks continuously.

NVD in the AI Era: The Case for Multi-Source Vulnerability Intelligence

For over twenty years, the global security community has operated under a single, comfortable assumption: that a centralized public source could help track, analyze, and enrich the world’s software vulnerabilities at the pace the industry needed. When the National Vulnerability Database (NVD) was established, the open source vulnerability lifecycle moved at a radically different pace.

ITIL v5: Exploring New Opportunities for IT Professionals

ITIL v5 connects IT service management to real digital product needs and faster delivery. If your team wants clearer direction, improved customer experience, and measurable results, this framework is a practical choice. ITIL v5 unifies strategy, operations, and improvement, offering new opportunities for professionals seeking modern skills and roles in service management.

Why Data Governance Matters When Adopting AI-Driven Student Enrollment Solutions

Schools, colleges, and universities are under constant pressure to make enrollment faster, simpler, and more accurate. This is why so many institutions are now turning to student enrollment solutions powered by artificial intelligence. These tools can predict applicant behavior, automate paperwork, flag incomplete forms, and even help admissions teams identify which students are likely to enroll. The appeal is obvious. But there is a part of this shift that often gets overlooked in the excitement around automation, and that is data governance.

AI Analysts for Autonomous Vulnerability Response

Security teams are drowning in findings, not because scanners miss things, but because nothing confirms which ones an attacker could actually reach. Seemplicity AI Analysts run the investigation themselves, checking runtime configuration, network reachability, and exploit conditions for each finding, and re-rank your backlog by confirmed exploitability. What rises to the top is backed by evidence. What drops down has been checked and reasoned out.

Why AI Is Becoming an Operational Requirement for Security Teams

In our previous article, From Vulnerability Management to Continuous Security Operations, we explored how organizations are moving beyond traditional vulnerability management toward a model built on continuous visibility, continuous prioritization, and continuous action. But that evolution raises an important question: how do security teams sustain this model at scale? For years, the cybersecurity industry focused on visibility.