Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Delivering Context and Speed for Security Operations with Aurora Security Assistant

Security operations teams are facing a familiar, but growing, challenge. As threat actors leverage AI and automation to move faster, alerts continue to expand in volume and complexity. Even mature security teams struggle to keep up with investigation timelines, maintain institutional knowledge, and ensure consistent response quality. At the same time, buyers are demanding more from their security platforms. They want solutions that go beyond detection.

How to Appear in AI Search Results

A few years ago, the goal was simple: rank on page one of Google. If your website appeared among the first 10 blue links, people would find you. That equation is changing. Search behavior is shifting from keyword lookups to answer-led queries. Instead of scanning a list of results, more people are turning to AI-powered search tools that read across the web, consolidate information, and deliver a direct answer. ChatGPT, Google AI Overviews, Perplexity, and Claude all work this way.

Microsoft Defender for Endpoint: Protection You're Paying For But Not Using

Microsoft Defender for Endpoint ships with serious firepower. But most of it is sitting idle. ASR rules get stuck in audit mode. Devices never get fully onboarded. Exploit protection is switched off. Security baselines drifting across device groups. You're paying for protection that isn't turned on. Reach analyzes your Defender deployment, surfaces every gap, prioritizes the fixes by real risk reduced, and keeps your controls aligned as you scale.

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?

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.

What Is Agentic Threat Intelligence?

Agentic threat intelligence is an emerging CTI model where bounded agents support repetitive investigation work, such as collection, enrichment, prioritization, and evidence packaging, while analysts retain control over takedown and escalation decisions. Vendor briefings are full of “agentic AI” right now. Most of them describe the same thing: faster dashboards and smarter alerts. That is not agentic threat intelligence.

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.