Why CISOs need a new framework for evaluating cybersecurity AI

Artificial intelligence is rapidly reshaping both sides of the cyber battlefield. Attackers are increasingly using generative AI to create more convincing phishing campaigns, automate reconnaissance, generate malware, and accelerate attacks at machine speed. At the same time, security vendors are racing to integrate AI into prevention, detection, investigation, and response technologies. The challenge for security leaders is that not all AI functions the same way — and not every approach is equally effective against modern threats.

This guide helps CISOs and security teams understand the differences between supervised machine learning, generative AI, large language models (LLMs), and self-learning AI within cybersecurity environments. It explores where each technology excels, where limitations exist, and why behavioral and adaptive approaches are becoming increasingly important as organizations face more novel and targeted attacks.

Learn how organizations are applying AI across cyber defense

Inside the guide, readers will explore how AI can support proactive security, accurate threat detection, autonomous response, and incident investigation across complex enterprise environments. The resource also examines the operational and security risks introduced by AI adoption itself, including prompt injection, hallucinations, insider threats, and sensitive data exposure.

Rather than presenting AI as a single solution, this paper provides a practical framework for understanding how different AI models should be applied to different cybersecurity challenges. It offers security leaders a clearer perspective on building resilient security operations capable of adapting to increasingly sophisticated AI-driven threats.