The Five Pillars Of MLSecOps With Ian Swanson

The Secure Developer

At the rate at which AI is infiltrating operations around the globe, AI regulation and security is becoming an increasingly pressing topic. As external regulations are put in place, it’s important to ensure that your internal compliance measures are up to scratch and your systems are safe. Joining us today to discuss the security of ML systems and AI applications is Ian Swanson, the Co-Founder and CEO of Protect AI. In this episode, Ian breaks down the five pillars of ML SecOps: supply chain vulnerabilities, model provenance, GRC (governance, risk, and compliance), trusted AI, and adversarial machine learning. We learn the key differences between software development and machine learning development lifecycles, and thus the difference between DevSecOps and ML SecOps. Ian identifies the risks and threats posed to different AI classifications and explains how to level up your GRC practice and why it’s essential to do so! Given the unnatural rate of adoption of AI and the dynamic nature of machine learning, ML SecOps is essential, particularly with the new regulations and third-party auditing that is predicted to grow as an industry. Tune in as we investigate all things ML SecOps and protecting your AI!

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