EP135 AI and Security: The Good, the Bad, and the Magical
Aug 21, 2023
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Phil Venables, Google Cloud's Chief Information Security Officer, discusses the game-changing potential of AI in cybersecurity. Topics include the impact of AI and machine learning on security, the use of generative AI to enhance productivity and secure software development, and the asymmetry between attackers and defenders in AI systems. The concept of shared faith in securing AI and the intersection of AI, security, and board governance are also explored.
AI has already been a game changer for security, with advancements in traditional machine learning and the future potential of generative AI.
Generative AI can enhance security in software development by providing automated frameworks for secure coding and vulnerability detection, improving security posture and reducing the toil of security tasks.
Deep dives
AI as a game changer for security
AI has already been a game changer for security, especially in traditional types of machine learning like malware filtering, spam filtering, and safe browsing. However, with the emergence of large language models and generative AI, the future holds even more significant advancements. Although the progress may seem incremental now, the rapid development and integration of AI into various security areas will eventually be recognized as game changing.
The potential of generative AI
Generative AI has the potential to transform productivity and enhance security in various use cases. For example, in software development, generative AI can provide automated frameworks for secure coding, detecting vulnerabilities, and recommending secure configurations. By leveraging generative AI in these areas, organizations can improve their security posture and reduce the toil of security analysis and coding tasks.
Risks and considerations in AI security
While AI brings numerous benefits, it also presents certain risks and concerns. One key focus is the need to ensure secure AI lifecycle management, similar to software security and data governance. This includes managing provenance, secure build, testing, and protecting training data, model weights, and parameters. Additionally, organizations must implement guards and circuit breakers to control AI behavior and prevent unintended consequences. Collaborative efforts and sharing data among defenders can enhance collective defense. Lastly, organizations need to consider a holistic risk management approach, involving security, trust and safety, compliance, and board-level governance to ensure responsible deployment and address potential risks.
Why is AI a game-changer for security? Can we even have game-changers in cyber security?
Is it more detection or is it more reducing toil and making humans more productuve? What are you favorite AI for security use cases?
What “AI + security” issue makes you - a classic CISO question here - lose sleep at night?
Does AI help defenders or attackers more? Won’t attackers adopt faster because they don’t have as many rules (but yes, they have bosses and budgets too)?
Aren’t there cases where defenders benefit a lot more and gain a superpower with AI while attackers are faced with defeat?
Is securing AI more similar or more different from securing other enterprise systems?