Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

aikido

Move over, Mythos. Here comes... pretty much any other model with a good harness

Jun 1, 2026 By Dania Durnas In Aikido
Mythos doesn’t need to be treated as the biggest and baddest in the room. Don’t get me wrong. Depending on the benchmark you’re evaluating against, Mythos is among the top models available today, and generally the best at reasoning. But it’s not leaps and bounds ahead of the race. And when it comes to practical use cases, throwing a general model, even a cutting-edge frontier model, at a problem doesn’t get the best results. Nor is it scalable or cost-effective.
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CrowdStrike

CrowdStrike Scales AI-Native Agents Across Falcon Exposure Management with NVIDIA

Jun 1, 2026 By Chase Midler In CrowdStrike
Security teams face a new imperative: act fast, or risk losing the vulnerability battle. The average enterprise faces thousands of vulnerabilities across a sprawling hybrid attack surface. Adversaries are using AI to discover and exploit weaknesses independently, at machine speed, making traditional disclosure timelines increasingly irrelevant. Scan-and-ticket workflows weren't built for this reality, and neither are the teams asked to execute them with finite headcount and growing board-level scrutiny.
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astra

Autonomous AI Agents for Penetration Testing: A Complete Guide

Jun 1, 2026 By Sanskriti Jain In Astra
Your last pentest probably took 2 weeks, cost 5 figures, and tested a fraction of your actual attack surface. Meanwhile, your team shipped 47 deployments in the same window, with each one almost completely untested for security. That gap between how fast you ship and how slowly you test is exactly where autonomous AI agents for penetration testing come in, especially with hackers getting smarter and faster each day (They are not using AI to summarize PDFs!).
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knowbe4

AI Agent Governance Part 3 - Runtime Governance: The Hidden Performance Cost of Agentic AI

Jun 1, 2026 By Anna Collard In KnowBe4
At the World Economic Forum cyber meeting in Geneva recently, I had an interesting conversation with Vinh Nguyen, who is a strategic security advisor and Senior Fellow for AI at CFR. I wanted to know from him how he sees runtime governance in agentic AI working out practically and what approaches actually work. One of the challenges he mentioned was that yes, we need runtime governance to provide continuous and real time assurance that agents are doing what they are supposed to be doing.
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netwrix

Best AI governance tools and platforms in 2026

Jun 1, 2026 By Netwrix Team In Netwrix
Most AI deployments run without formal controls over what data they can reach, what decisions they make, or how they behave in production, yet regulators now require answers to all three. AI governance tools address these risks across three distinct layers: model governance, data access governance, and observability. Most enterprises need coverage across more than one layer. AI governance has shifted from a voluntary best practice into a formal compliance requirement.
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What Makes LCD Displays Reliable and Efficient

What Makes LCD Displays Reliable and Efficient

May 31, 2026 By SecuritySenses In SecuritySenses
Electronic screens are everywhere today. Selecting the right technology makes a major difference in how a device performs. Liquid crystal displays have held a top spot in consumer electronics for decades. They offer a strong mix of performance and value. These screens operate reliably under conditions that cause other displays to fail. Hardware designers look for components that balance clarity with power consumption. Understanding what makes them work helps teams pick the best components. Let us examine the mechanics behind these dependable screens.
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armo

How to Tell If Your AI Agent Has Been Compromised (When Every Symptom Looks Normal)

May 30, 2026 By Ben Hirschberg In ARMO
Your AI agent just did something it has never done. It called a tool that is not in its usual set, or it opened a connection to a destination you do not recognize, or its output came back subtly wrong. So you do what anyone does: you search for what a compromised agent looks like, and you find a checklist. Unusual tool usage. Unexpected data access. Out-of-context responses. Elevated resource consumption.
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