Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For the first time anywhere, the MITRE ATLAS framework and the OWASP Top 10 for LLM applications are written into binding law. Article 15 names data poisoning, model poisoning, adversarial examples, model evasion and confidentiality attacks as threat classes you must have technical measures against—and must be able to evidence to a regulator. The question is no longer whether you have thought about AI security. It is whether you can prove your AI system holds.

The EU AI Act's August 2nd, 2026 deadline just changed shape. Host Tova Dvorin and offensive security engineer Adrian Cully separate what actually lands—Article 50 transparency duties and GPAI enforcement powers—from the high-risk obligations that slipped to December 2027. Inside: Article 15 writes MITRE ATLAS and the OWASP LLM Top 10 into binding law, the DORA / NIS2 / AI Act overlap that makes one incident reportable three times, penalties up to 7% of global turnover, and the five things a CISO should do this week. Part 1 of 2.

Advanced AI models are changing the cyber threat landscape by accelerating vulnerability discovery, exploit development, and attacker decision-making. Regulators like the ECB have made clear: action plans are necessary. Financial institutions need to understand whether their existing cyber risk programs can keep pace, not only across their own attack surface, but across the critical third parties and software dependencies they rely on.

Most healthcare AI systems rely on Retrieval-Augmented Generation (RAG) and vector databases to search millions of insurance claims, physician notes, lab reports, billing documents, and customer records. But what happens before those documents become searchable? In this Short, we explain how embedding models convert raw healthcare documents into vectors—and why patient names, medical record numbers (MRNs), diagnoses, and Social Security numbers can end up stored inside your vector database if data isn't protected before ingestion.

One platform to discover, monitor, and remediate data and identity risks across your environment. More visibility. More control. Fast setup.

AI Led Pentesting is redefining how organizations approach application security. As software development accelerates with AI-assisted coding, cloud-native applications, and rapidly evolving attack surfaces, traditional penetration testing is struggling to keep pace. In this detailed video, we explore why the future of security testing needs to be continuous, intelligent, and autonomous led by AI.

CVE of the day - CVE-2026-69198.

A criminal talked commercial AI models past their guardrails. Then stood up a "company" of AI agents that engineered, tested and refined malware. It began when Sophos analysts found a folder named "test" on an endpoint nobody recognised. Inside was the front end of a machine: Cobalt Strike profiles, a Telegram command channel, a hidden Sliver server behind Cloudflare and scripts written with the help of AI.

AI has moved beyond the browser. It now runs in the terminal, the IDE, and desktop apps with your users' full privileges, where most security tools can't see it. In the AI era, the endpoint is where AI executes. This video shows how CrowdStrike Falcon AI Detection and Response (AIDR) extends the Falcon sensor you already run to the AI interaction layer. One sensor and one browser extension deliver visibility and control over every AI interaction on the endpoint and in the browser, from the Falcon console you already use.

In this Demo Drill Down, learn how Manual Sensor Scan enables teams to create targeted Python-based checks for pre-CVE vulnerabilities, supply chain risks, proprietary software, and other environment-specific conditions. See how built-in helper functions simplify custom detection logic and how identified findings are brought directly into Falcon Exposure Management for investigation, prioritization, and remediation.