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Navigating the AI Security Landscape with Somesh Jha
In this Cyber Sentries episode, host John Richards interviews AI expert Somesh Jha on using AI for security. They discuss the promise and perils of AI in cybersecurity, best practices for implementation, challenges with fine-tuning models, and adopting a multi-agent approach.
Jha provides insights on the potential of AI to transform cloud security through automating tasks like intrusion detection. However, attackers could also weaponize AI for large-scale spear phishing. As the technology matures, it remains unclear exactly what will be possible. The episode covers common mistakes like applying AI too broadly, the need for careful benchmarking to avoid hallucinations, the large data requirements for fine-tuning models, and the benefits of a multi-agent framework.
Questions You May Have
Key Takeaways
Jha advises starting with a focused security use case and doing careful benchmarking to demonstrate value before expanding AI more broadly. He notes the challenges of fine-tuning models with limited data. Jha explains how Langroid is designed around a multi-agent approach for maintainable and extensible AI code.
This episode provides insights for security teams on leveraging AI responsibly, with practical advice on implementation pitfalls. Jha offers perspectives on realizing the future potential of AI in cybersecurity. His expertise provides a useful guide for applying AI to security effectively.
Links & Notes
18 قسمت
Navigating the AI Security Landscape with Somesh Jha
In this Cyber Sentries episode, host John Richards interviews AI expert Somesh Jha on using AI for security. They discuss the promise and perils of AI in cybersecurity, best practices for implementation, challenges with fine-tuning models, and adopting a multi-agent approach.
Jha provides insights on the potential of AI to transform cloud security through automating tasks like intrusion detection. However, attackers could also weaponize AI for large-scale spear phishing. As the technology matures, it remains unclear exactly what will be possible. The episode covers common mistakes like applying AI too broadly, the need for careful benchmarking to avoid hallucinations, the large data requirements for fine-tuning models, and the benefits of a multi-agent framework.
Questions You May Have
Key Takeaways
Jha advises starting with a focused security use case and doing careful benchmarking to demonstrate value before expanding AI more broadly. He notes the challenges of fine-tuning models with limited data. Jha explains how Langroid is designed around a multi-agent approach for maintainable and extensible AI code.
This episode provides insights for security teams on leveraging AI responsibly, with practical advice on implementation pitfalls. Jha offers perspectives on realizing the future potential of AI in cybersecurity. His expertise provides a useful guide for applying AI to security effectively.
Links & Notes
18 قسمت
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