AI risks in cybersecurity: Zero Trust strategies for security leaders

The report surveys how AI amplifies external and insider risks—covering model poisoning, prompt injection and jailbreaks, shadow AI adoption, “vibe coding,” and AI-powered phishing/malware—and advocates a Zero Trust approach to constrain blast radius and data exposure. Citing examples like the MGM/Scattered Spider breach, it recommends layered controls and governance, positioning ThreatLocker’s application allowlisting, Ringfencing, web/storage/network controls, and MDR as practical mechanisms to enforce least privilege and block unapproved tools and behaviors.