Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In March 2026, Stryker Corporation experienced a global cyber incident that disrupted operations across its environment. Manufacturing slowed, internal systems went offline, and employees were instructed to disconnect devices. At first glance, it looked like another large-scale cyberattack. It wasn’t. This incident exposed a much more important reality about modern cybersecurity risk: organizations are no longer being breached in traditional ways.

RSA Conference 2026 made one thing clear very quickly. Security leaders are done with generic AI pitches. After two years of relentless “AI everything,” the market is now pushing back. There is a growing fatigue with vague promises, surface-level features, and what many are calling outright AI washing. The result is a trust gap. What cut through this year was not another AI-powered detection claim. It was a much more grounded question.

Each year, the threat environment changes, and the way we measure cyber risk has to keep up. Attackers adjust quickly. At the same time, organizations add cloud services, SaaS applications, and third parties to their environments. That makes it harder to maintain a stable, external measure of security performance. At Bitsight, the Ratings Algorithm Update (RAU) is one of the major initiatives that helps keep the Bitsight Security Rating a reliable indicator of security performance.

AI isn’t just another technology wave—it’s a force multiplier for both innovation and risk. In a recent webinar featuring insights from Bryan Palma and guest speaker Jinan Budge, Vice President and Research Director at Forrester, one message came through clearly: the rise of AI and AI agents is fundamentally reshaping the human risk landscape—and security leaders need to move fast to keep up.

Enterprise security teams spent years building data loss prevention (DLP) programs around a predictable set of egress channels: email, USB drives, cloud storage, and sanctioned SaaS apps. Generative AI has rewritten those assumptions almost overnight. Today, the same data those DLP controls were built to protect is flowing into AI interfaces that most organizations have no visibility into and no enforcement capability over.

A supply chain compromise that impacted the Python package LiteLLM, with malicious versions 1.82.7 and 1.82.8 was published to PyPI on March 24, 2026. Bitsight Threat Intelligence, public reporting and vendor disclosures indicate the malicious releases included credential harvesting, Kubernetes-focused lateral movement, and persistence mechanisms, creating serious risk for cloud-native and AI-related environments that installed or ran the affected versions.

The fastest way to reduce risk at enterprise scale is to standardize on a vulnerability and exposure management platform that unifies asset visibility, prioritizes what matters, and automates workflow to remediate. In this article, we’ll break down the nine essential requirements security leaders should insist on when evaluating an enterprise vulnerability management system, whether it’s an existing tool in their tech stack or a potential new capability.

For channel partners, AI has quickly moved from a future conversation to a current customer problem. Clients are already using AI across their organisations, often faster than governance can keep up. What’s emerging is not just another technology trend, but a new class of risk that customers cannot fully see or control. Our latest research, based on insights from senior security leaders in highly regulated industries, highlights the scale of the issue.

Interest in cloud-native application protection platforms (CNAPPs) has exploded over the recent years, partly due to their ability to reduce alert noise by translating siloed misconfigurations into correlated, theoretical attack paths and exposures. While many organizations have adopted these solutions in pursuit of outcomes like zero critical issues, cloud breaches continue to rise.