The blog post reviews the "NIST AI 100-2e2023" report, providing insights into adversarial machine learning (AML) and AI system vulnerabilities. It discusses predictive and generative AI, categorizing attacks based on attacker goals and capabilities. Key threats like evasion, data poisoning, and privacy breaches are highlighted, emphasizing the need for robust defense mechanisms. The report serves as a comprehensive guide for AI professionals, outlining challenges and future directions in AML, and stresses the importance of secure, resilient AI systems for a safer technological future.

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The document, titled "Risks and Mitigation Strategies for Adversarial Artificial Intelligence Threats: A DHS S&T Study," prepared in June 2023, addresses the emerging risks associated with adversarial artificial intelligence (AAI) and suggests mitigation strategies. It explores how the Department of Homeland Security (DHS) can adapt to these challenges, especially in light of AI's transformative impact on various sectors. The report discusses different types of adversarial AI, such as evasion attacks, data poisoning, model extraction, and inference attacks, along with generative deceptive AI like deepfakes. Each of these threats is analyzed with respect to potential risks they pose and mitigation strategies. The document emphasizes the importance of understanding these risks early in the system lifecycle and integrating AI security assessments and standards into systems engineering processes. The study also highlights the need for international collaboration in developing standards and test methods to counter AAI threats effectively.

The document is a comprehensive DoD Instruction (5000.97) focused on implementing digital engineering in defense systems development and sustainment. It outlines policies, responsibilities, and procedures for integrating digital models, data, and methodologies across defense systems' life cycles. The instruction emphasizes the importance of digital engineering for effective system development, particularly in response to global challenges, dynamic threats, and technological advancements. Key aspects include establishing digital engineering capabilities, maintaining digital models and authoritative data sources, and ensuring workforce training. It aims to modernize the DoD's approach to system design, development, testing, and sustainment, using digital engineering as a core practice.