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LotR Episode 7: Securing AI Applications

Runtime AI Application Security

Featuring:

In this episode, the hosts discuss the critical aspects of AI security with industry experts. They explore the unique challenges posed by AI technologies, the role of CISOs in navigating these challenges, and the emerging threats that organizations face. The conversation emphasizes the importance of data control, compliance, and the need for robust testing and red teaming strategies. The experts also highlight industry-specific concerns and the future of AI security tools, providing valuable insights for organizations looking to secure their AI applications.

Takeaways

  • AI fundamentally changes how we approach security.

  • Protecting sensitive data and models is crucial.

  • Security must enable innovation, not hinder it.

  • Data is now executable, increasing risks.

  • CISOs need to focus on compliance and data control.

  • Emerging threats require new security strategies.

  • Testing AI systems is complex and requires new methods.

  • Industry-specific regulations impact AI security needs.

  • Collaboration between security and data teams is essential.

  • The future of AI security tools is evolving rapidly.

Chapters

00:00 Introduction to AI Security

02:29 Understanding the Shift in Security Paradigms

05:18 The Rapid Evolution of AI Technologies

07:45 CISO Perspectives on AI Security

10:13 Top Concerns in AI Security

11:59 Emerging Threats and Attack Vectors

14:27 Data Governance and Compliance Challenges

17:21 The Role of Security Teams in AI Programs

22:30 Collaboration Between Security and Data Science

23:39 The Importance of Data Control in AI Security

25:00 Understanding Risks in AI Security

29:02 Identifying Malicious vs. Benign Activities

31:26 The Role of Testing Infrastructure in AI Security

33:45 Industry-Specific Security Concerns

35:52 Red Teaming and AI Security Testing

39:10 The Need for Comprehensive Threat Modeling

41:21 Data Security in the Age of AI

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