Learn the differences between supervised machine learning, generative AI, LLMs, and self-learning AI, including where each approach succeeds and where it falls short.
The CISO's Guide to Cyber AI: Categorizing the Use of AI in Cyber Security
Understand how AI is reshaping modern cyber defense
Download this guide to understand how AI is reshaping both cyber attacks and defence, and what that means for your security strategy. It breaks down risks, opportunities, and practical considerations for CISOs adopting cyber AI effectively today.
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What's inside this resource
Why not all cybersecurity AI works the same
How attackers are using AI to evolve cyber-attacks
Explore how offensive AI is enabling more targeted phishing, automated reconnaissance, novel malware creation, and “one-of-one” attacks tailored to individual organizations.
How security teams can apply AI responsibly and effectively
Understand how modern organizations can combine AI techniques to improve threat detection, automate investigations, reduce response times, and strengthen cyber resilience against unknown threats.
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Download this guide to understand how AI is reshaping both cyber attacks and defence, and what that means for your security strategy. It breaks down risks, opportunities, and practical considerations for CISOs adopting cyber AI effectively today.
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.
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Gartner, Magic Quadrant for Email Security Platforms, Max Taggett, Nikul Patel, Franz Hinner, Deepak Mishra, 16 December 2024Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant and Peer Insights are a registered trademark, of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.
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