Viswanath S Chirravuri - Safeguarding Machine Learning Systems: A Comprehensive Security Plan

In this presentation, we unveil a holistic security approach tailored for machine learning (ML) systems in artificial intelligence (AI). We kick start by dissecting prevalent security risks in ML, such as adversarial attacks and data poisoning, setting the stage for a proactive defense strategy. Our comprehensive approach encompasses Data Security, Model Security, Platform Security, Security Compliance, and Human Security. Data Security emphasizes encryption, access control, and anonymization techniques to safeguard sensitive data. Model Security advocates for model watermarking and adversarial robustness training to fortify models against manipulations. Platform Security ensures secure configurations and continuous monitoring to mitigate vulnerabilities. Adhering to Security Compliance principles aligns with ethical AI deployment, guided by transparency and accountability. Human Security emphasizes comprehensive training. Attendees will gain practical insights into integrating security measures throughout the ML lifecycle, bolstering the resilience and trustworthiness of their ML systems while ensuring responsible AI deployment.