MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257

Aug 30, 2024

Sean Morgan, Chief Architect at Protect AI and a pivotal figure in the TensorFlow Addons community, shares insights on the crucial role of MLSecOps in AI Security. He discusses the need for proactive security integration in MLOps compared to traditional DevOps, emphasizing vulnerabilities in AI models. Sean highlights the challenges of managing model artifacts, securing open-source AI frameworks, and adopting a zero-trust strategy. He also calls for collaborative efforts within the MLSecOps community to enhance overall machine learning security.

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Intro

00:00 • 3min

Securing MLOps: Balancing Innovation and Safety

03:13 • 20min

Addressing Challenges in Model Artifacts and Vulnerability Scanning

23:14 • 3min

Building a Security-First Community in MLSecOps

26:28 • 2min

Securing Open Source AI Frameworks

28:44 • 10min

The Importance of Zero Trust and Clear Ownership in Security Protocols

38:36 • 2min

Prioritizing Security in Machine Learning Operations

40:29 • 2min

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. // Abstract MLSecOps, 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. // Bio Sean 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/Merch https://mlops-community.myshopify.com/ // Related Links Sean's GitHub: https://github.com/seanpmorgan MLSecOps Community: https://community.mlsecops.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch 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