4 verified briefings on Risk Management. Each story includes a plain-English summary, why it matters, and the concrete action engineering teams should take.
Microsoft is highlighting the security challenges organizations face when adopting AI. As companies like St. Luke’s and ManpowerGroup move to AI-powered models, establishing a strong security foundation for cloud, data, and identity becomes essential for enabling responsible innovation and managing new risks.
A recent analysis looks back at the last 20 years of cybersecurity, highlighting significant failures and missteps. The review covers major incidents like the CrowdStrike outage, persistent systemic issues, and major business blunders, reflecting on the lessons learned and the industry's evolution.
A new report highlights that traditional security benchmarks are ineffective for evaluating AI systems. Unlike standard software, AI security is an emergent property that cannot be measured by simple tests, challenging teams to rethink how they approach securing their AI models and applications.
A new report from Orchid Security reveals that 57% of enterprise identities are “identity dark matter”—unseen and unmanaged. This growth in unmanaged access points creates significant security vulnerabilities, especially as companies rapidly adopt Agent AI, which can exploit these gaps.