Sean Morgan is an active open-source contributor and maintainer and is the special interest group lead for TensorFlow Addons. Learn more about the platform for end-to-end AI Security at https://protectai.com/.MLSecOps is Fundamental to Robust AI Security Posture Management (AISPM) // MLOps Podcast #257 with Sean Morgan, Chief Architect at Protect AI.// AbstractMLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly how you can improve that security posture through the use of MLSecOps.// BioSean Morgan is the Chief Architect at Protect AI. In prior roles he's led production AIML deployments in the semiconductor industry, evaluated adversarial machine learning defenses for DARPA research programs, and most recently scaled customers on interactive machine learning solutions at AWS. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Addons.// MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksSean's GitHub: https://github.com/seanpmorganMLSecOps Community: https://community.mlsecops.com/ --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Sean on LinkedIn: https://www.linkedin.com/in/seanmorgan/Timestamps:[00:00] Sean's preferred coffee[00:10] Takeaways[01:39] Register for the Data Engineering for AI/ML Conference now![02:21] KubeCon Paris: Emphasis on security and AI[05:00] Concern about malicious data during training process[09:29] Model builders, security, pulling foundational models, nuances[12:13] Hugging Face research on security issues[15:00] Inference servers exposed; potential for attack[19:45] Balancing ML and security processes for ease[23:23] Model artifact security in enterprise machine learning[25:04] Scanning models and datasets for vulnerabilities[29:23] Ray's user interface vulnerabilities lead to attacks[32:07] ML Flow vulnerabilities present significant server risks[36:04] Data ops essential for machine learning security[37:32] Prioritized security in model and data deployment[40:46] Automated scanning tool for improved antivirus protection[42:00] Wrap up

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