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The MLSecOps Podcast

Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations

Nov 28, 2023
Speakers discuss concerns of customers and clients regarding security of AI applications and machine learning systems. They explore the distinction between robustness and security in adversarial attacks on ML models. The concept of mitigations in robust ML, including data encryption and secure backups, is discussed. The use of cryptographic signature for data and supply chain validation for data poisoning protection are examined. Techniques of model inversion and differential privacy in adversarial ML are explained. Building effective machine learning models with clear goals is emphasized.
41:19

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Quick takeaways

  • Robust security measures are essential to protect AI applications and machine learning systems, particularly in critical infrastructure and government applications.
  • Understanding distinct attack vectors and their feasibility helps in assessing risks and developing appropriate mitigation strategies for specific use cases.

Deep dives

The Importance of AI Security and ML Security

The podcast episode delves into the significance of AI security and ML security, particularly in the context of the government's concerns. Adversarial machine learning poses real threats, especially with the involvement of nation states with substantial budgets and motivations to carry out attacks. While not all aspects of adversarial machine learning are practical, certain areas, such as attacks on critical infrastructure and data privacy in government applications, require serious consideration. The discussion emphasizes the need for robust security measures to protect AI applications and machine learning systems, as well as the necessity of distinguishing between robustness and security in developing effective defense strategies.

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