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.

Every security leader has a version of the network in their head. They know which systems should be segmented, which applications should be reachable, which ports should never be open, and which access paths should not exist. They know how the architecture is supposed to work. The harder question is whether the live environment is actually enforcing that design right now. That question is getting more difficult to answer.

If you’re running applications behind an Application Load Balancer, Amazon CloudFront, API Gateway, or AWS AppSync, AWS WAF is your first line of defense against malicious web traffic. Graylog provides a purpose-built way to make those enforcement decisions immediately actionable.

Microsoft 365 includes a wide range of security capabilities designed to help protect identities, devices, email, and data. Most MSPs already have access to these tools. The challenge is ensuring everything is configured correctly and reviewed consistently; not just having the “correct tools”. As MSPs grow, manual security reviews become increasingly difficult to maintain. Every tenant needs regular attention as configurations change over time and security recommendations continue to evolve.

TL;DR: AI governance solutions help organizations inventory, secure, and monitor AI systems. Best for AI security and shadow AI: Mend AI; enterprise risk and compliance: Credo AI and IBM watsonx.governance; model monitoring: Fiddler AI. Effective AI governance implementation involves establishing a cross-functional committee, compiling an AI bill of materials (AI-BOM) to identify risks, and implementing policies based on frameworks like NIST AI RMF.

Arctic Wolf Internal Security Operations (SecOps) recently identified a GitHub page impersonating Arctic Wolf to target our customers and prospects. The SecOps team immediately escalated these findings to our Threat Research team, who uncovered a complex attack chain subsequently deploying information-stealing malware. Arctic Wolf has since removed this fake GitHub page.

Phishing used to be the easy alert. Bad grammar. A link to “Amazon.com.” A sender address off by one character. Analysts joked about it. You almost felt bad for the attacker. That era is over. AI killed it.