Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In my last post, I explained the math behind cosine similarity. Cosine similarity is a powerful search technique. When you are dealing with thousands or millions of chunks, it provides a fast, scalable way to find content conceptually similar to the user’s question. That is a major breakthrough. Without vector search, modern RAG would be much harder to build. But the mistake is pushing every retrieval problem into vector search. That is where practical retrieval starts breaking down.

On June 1, 2026, researchers identified malicious code embedded in at least 32 package releases published under the @redhat-cloud-services npm namespace, a set of frontend components and API clients that power the Red Hat Hybrid Cloud Console. The compromised releases carry a preinstall script that runs an obfuscated payload the moment a package is installed, harvesting developer and cloud credentials and attempting to spread itself to other packages the victim can publish.

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.

Building a resilient CI/CD pipeline means protecting every piece of data that makes your code run. Your environment variables, secret tokens, and configuration files demand the exact same security as your core repositories. Traditional backup protocols leave these assets completely vulnerable to silent manipulation. If ransomware subtly modifies your archived backup, executing a restore will deploy the corrupted files straight into production.

Agentic AI is rewriting the rules of enterprise risk, operating across distributed systems at a speed and scale that most security architectures weren’t designed to handle.

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.

Microsoft 365 DLP delivers real protection for regulated data in Exchange, SharePoint, Teams, and managed Windows endpoints, but only within that boundary. On-premises file servers, Linux endpoints, unmanaged devices, and non-Microsoft SaaS fall outside enforcement regardless of how policies are configured. Most security teams can't yet clearly distinguish the gaps that configuration fixes can address from those that require supplemental controls.

CVE-2026-0257 is an authentication bypass vulnerability in the GlobalProtect portal and gateway of Palo Alto Networks PAN-OS software that lets a remote attacker forge an authentication override cookie and establish an unauthorized VPN connection. The vulnerability carries a CVSS base score of 7.8 (High). It is tracked under CWE-565, reliance on cookies without validation and integrity checking. Exploitation is unauthenticated and requires no user interaction.

By rewriting our secret detection engine in Rust, we made our engine more than three times as fast. But not without making it four times slower along the way.