Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Artificial intelligence (AI) is transforming industries, but it also introduces new risks that organizations must manage carefully. This article explains how to develop and apply AI risk categories aligned with recognized frameworks, focusing on operational, technical, and ethical risks. Readers will learn how to prioritize these risks based on their potential impact on the organization.

Counter Threat Unit (CTU) researchers investigated two interconnected threat groups known as Vect and TeamPCP. The two groups announced a formal operational partnership in late March 2026 to combine TeamPCP’s credential harvesting and data theft capabilities with Vect’s ransomware deployment infrastructure in a widespread campaign involving supply chain attacks and the extortion of multiple organizations.

Infrastructure as a service (IaaS) is becoming a bigger opportunity for managed service providers (MSPs), cloud service providers (CSPs), hosters and telecommunications providers. Clients still need compute, storage and networking. But many are rethinking where those workloads should run.

OWASP, the Open Worldwide Application Security Project, has published Top 10 lists for over two decades to help security teams prioritize the risks that matter most. The original OWASP Top 10 for web applications became the industry’s default checklist for application security. When large language models moved into production, OWASP followed with the Top 10 for LLM Applications, addressing risks like prompt injection and sensitive information disclosure in single-turn model responses.