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ML Security: AI Incident Response Plans and Enterprise Risk Culture; With Guest: Patrick Hall
Manage episode 362900667 series 3461851
In this episode of The MLSecOps Podcast, Patrick Hall, co-founder of BNH.AI and author of "Machine Learning for High-Risk Applications," discusses the importance of “responsible AI” implementation and risk management. He also shares real-world examples of incidents resulting from the lack of proper AI and machine learning risk management; supporting the need for governance, security, and auditability from an MLSecOps perspective.
This episode also touches on the culture items and capabilities organizations need to build to have a more responsible AI implementation, the key technical components of AI risk management, and the challenges enterprises face when trying to implement responsible AI practices - including improvements to data science culture that some might suggest lacks authentic “science” and scientific practices.
Also discussed are the unique challenges posed by large language models in terms of data privacy, bias management, and other incidents. Finally, Hall offers practical advice on using the NIST AI Risk Management Framework to improve an organization's AI security posture, and how BNH.AI can help those in risk management, compliance, general counsel and various other positions.
Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models
Recon: Automated Red Teaming for GenAI
Protect AI’s ML Security-Focused Open Source Tools
LLM Guard Open Source Security Toolkit for LLM Interactions
Huntr - The World's First AI/Machine Learning Bug Bounty Platform
41 episodes
Manage episode 362900667 series 3461851
In this episode of The MLSecOps Podcast, Patrick Hall, co-founder of BNH.AI and author of "Machine Learning for High-Risk Applications," discusses the importance of “responsible AI” implementation and risk management. He also shares real-world examples of incidents resulting from the lack of proper AI and machine learning risk management; supporting the need for governance, security, and auditability from an MLSecOps perspective.
This episode also touches on the culture items and capabilities organizations need to build to have a more responsible AI implementation, the key technical components of AI risk management, and the challenges enterprises face when trying to implement responsible AI practices - including improvements to data science culture that some might suggest lacks authentic “science” and scientific practices.
Also discussed are the unique challenges posed by large language models in terms of data privacy, bias management, and other incidents. Finally, Hall offers practical advice on using the NIST AI Risk Management Framework to improve an organization's AI security posture, and how BNH.AI can help those in risk management, compliance, general counsel and various other positions.
Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models
Recon: Automated Red Teaming for GenAI
Protect AI’s ML Security-Focused Open Source Tools
LLM Guard Open Source Security Toolkit for LLM Interactions
Huntr - The World's First AI/Machine Learning Bug Bounty Platform
41 episodes
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