Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

‍At its root, cyber risk management is essentially a forward-looking discipline. The goal has never been solely to understand current exposure, but to determine which actions will reduce it most effectively, given the organization's priorities and constraints. Organizations today can assess control maturity and quantify financial exposure with increasing precision, giving security and GRC leaders a more comprehensive picture of their risk landscape than ever before.

Most organizations didn’t design their infrastructure to become hybrid. It happened gradually. A few workloads moved to the cloud first. Development teams adopted new services. Meanwhile, some systems stayed exactly where they were — inside internal data centers — because moving them wasn’t practical. Over time the environment expanded. Now many organizations run applications across cloud platforms, private infrastructure, and on-premise systems at the same time.