Cloud Security Podcast
Securing AI Applications in the Cloud
Mar 6, 2025
Bar-el Tayouri, Head of Mend AI at Mend.io, is a leading expert in AI security and application security. In this conversation, he tackles the hidden dangers of shadow AI and the layers of an AI Bill of Materials (AIBOM). Bar-el emphasizes the necessity of red teaming and shares practical strategies for pre- and post-deployment security. He explores the journey of AI adoption and highlights the complexities in balancing innovation with security. Plus, he shares his love for Ethiopian cuisine, connecting personal passions with professional insights.
45:27
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Understanding the layers of AI applications, including models and infrastructures, is crucial for ensuring comprehensive security against risks.
- Embracing structured experimentation in AI development helps identify vulnerabilities early, significantly reducing potential security issues before deployment.
Deep dives
Understanding AI Security Requirements
Securing AI applications involves understanding the multiple layers and components inherent in AI systems. Organizations should recognize these components, which include the models, datasets used for training, and the individual elements of the application like APIs and infrastructure. For instance, utilizing an open-source AI model or a remote inference provider introduces unique security and compliance challenges. Leaders must consider these layers to understand the risk factors of their AI applications, ensuring a robust security posture that doesn't neglect any part of the system.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
