Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most cybersecurity conversations focus on stopping attackers from breaking in. New malware variants, ransomware campaigns, AI-powered attacks, and zero-day vulnerabilities dominate the headlines. Yet many breaches occur for a much simpler reason: organizations unintentionally expose systems, applications, or data to the internet.

For the past two decades, enterprise security has evolved around a relatively stable assumption: software executes instructions, people take actions, and security teams are responsible for understanding and governing the interaction between the two. The technologies have changed. Infrastructure moved to the cloud. Applications became distributed. Identities expanded beyond employees to include partners, contractors, and machines. Yet the underlying model remained remarkably consistent.

Microsoft Build 2026 delivered a clear message: AI is no longer being positioned as a standalone productivity tool. It is becoming a core platform capability embedded across the Microsoft ecosystem. From AI agents to developer tooling and enterprise governance, this year’s event focused on helping organisations move from AI experimentation to operational adoption. For UK businesses, the most important takeaway is not a single announcement.

For years, installing an npm package has meant trusting that every package in the dependency tree will behave as expected. Whether code originated from the npm registry, a Git repository, a remote URL, or an installation script buried deep within a transitive dependency, npm would typically execute or retrieve it automatically during the installation process.

It’s official. Gartner just published the very first Gartner Magic Quadrant for Software Supply Chain Security, and JFrog has been recognized as a Leader, placing highest for Ability to Execute among all the vendors included. For an inaugural report in a category this important, that placement means a great deal to us, and we don’t take it lightly.

At 8:30 a.m., the scan report is already out of date. New cloud instances came online overnight, a container image was rebuilt, developers shipped code, and the security queue is full of findings that still need triage, ownership, and context. The hard part is rarely detection. The hard part is deciding what to fix first and getting that decision to flow into the systems your team already runs every day.

GitGuardian helps security teams detect leaked secrets across dozens of integrations, all converging in one place: our dashboard. As our data volume grew, some pages started taking several seconds to load.

Your ASPM dashboard shows your mobile security posture. The score reflects what your integrated testing tools found. It does not reflect what they could not test. For mobile apps, the gap between those two things includes the compiled binary, the third-party SDKs linked inside it, and what the app does at runtime on a real physical device. None of that data enters an ASPM dashboard built on source code scan results. The posture view looks complete. The coverage is not.

An AI-driven system at a beverage manufacturer recently churned out several hundred thousand excess cans after misreading unfamiliar packaging. The system didn’t recognize the company’s new holiday labels, flagged them as an error, and triggered additional production runs before the company caught the mistake. The system followed its instructions perfectly.

Phishing has always been a game of impersonation. But for decades, the tell was in the details: a misspelled word here, an awkward sentence there, a logo that was just slightly off. Security awareness training built an entire doctrine around those cues. Spot the typo, avoid the trap. That playbook is now obsolete. KnowBe4's latest Phishing Trends Report found that 86% of phishing attacks observed in the last six months involved some level of AI assistance.