Advancing AI Security: Taxonomy and Mitigation Strategies
Artificial intelligence (AI) systems are rapidly evolving and making great strides in various fields. To ensure their safe and reliable operation, AI systems need to possess certain characteristics. The NIST AI Risk Management Framework and AI Trustworthiness taxonomy have identified the importance of these operational characteristics in building trustworthy AI.
In a recent study conducted by the NIST Trustworthy and Responsible AI team, their goal was to advance the field of Adversarial Machine Learning (AML) by creating a comprehensive taxonomy of terms and providing definitions for relevant terms. This taxonomy is structured into a conceptual hierarchy and has been developed by analyzing the existing AML literature.
The taxonomy includes categories such as Machine Learning (ML) techniques, different phases of the attack lifecycle, the aims and objectives of the attacker, and the skills and information attackers have about the learning process. In addition to outlining the taxonomy, the study also offers strategies for controlling and reducing the effects of AML attacks.
The researchers highlight that AML problems are dynamic and need to be addressed at every stage of AI system development. The research aims to provide a valuable resource that can shape future practice guides and standards for evaluating and controlling the security of AI systems.
The Importance of Trustworthy AI in Operational Characteristics
Trustworthy AI systems are vital in today’s technology-driven world, as they are extensively utilized in various industries. The NIST AI Risk Management Framework and AI Trustworthiness taxonomy emphasize the importance of safe, reliable, and resilient operations for AI systems.
Trustworthy AI systems ensure that the generated content is original and accurate, while also making predictions based on the available data. Predictive AI focuses on making predictions using data, while Generative AI creates original content. Both of these categories play a significant role in the advancement of AI systems.
By incorporating operational characteristics such as safety, reliability, and resilience, AI systems can be trusted to deliver accurate and reliable results. These characteristics are essential in ensuring the overall trustworthiness of AI systems.
NIST Research on Adversarial Machine Learning
The NIST Trustworthy and Responsible AI research team has made significant strides in advancing the field of Adversarial Machine Learning (AML). AML refers to the study of techniques and strategies to protect AI systems from malicious attacks and manipulations.
The team’s research objective was to create a thorough taxonomy of terms related to AML and provide definitions for key concepts. They carefully analyzed the existing AML literature to develop a conceptual hierarchy of terms. This taxonomy covers various aspects such as ML techniques, attack lifecycle phases, attacker objectives, and attacker skills.
The research is aimed at addressing the dynamic nature of AML problems and providing valuable insights into controlling and mitigating AML attacks. By establishing a common language and understanding within the AML domain, the research contributes to the development of future norms and standards for evaluating and controlling the security of AI systems.
Creating a Comprehensive Taxonomy for AML Attacks
The researchers have successfully created a comprehensive taxonomy for AML attacks. The taxonomy covers attacks on systems that use both Generative AI and Predictive AI. Generative AI attacks are categorized into evasion, poisoning, abuse, and privacy, while predictive AI attacks are categorized into evasion, poisoning, and confidentiality.
The taxonomy also addresses attacks on various data modalities and learning approaches, including supervised, unsupervised, semi-supervised, federated learning, and reinforcement learning. This comprehensive taxonomy provides a valuable framework for understanding and classifying different AML attacks.
Additionally, the research discusses possible mitigations and strategies for handling specific attack classes. The research critically analyzes current mitigation strategies and highlights their shortcomings. This analysis provides insights into the efficiency of existing mitigation strategies and encourages further research and development in this field.
Future Norms and Standards for AI Security: A Call to Action
The research conducted by the NIST Trustworthy and Responsible AI team serves as a call to action for the development of future norms and standards in AI security. By establishing a common language and understanding within the AML domain, the research promotes a coordinated and knowledgeable approach to addressing the security challenges posed by the rapidly changing AML landscape.
The taxonomy and nomenclature provided in the research paper lay the foundation for future practice guides and standards for evaluating and controlling the security of AI systems. It is crucial for AI systems to have safe, reliable, and resilient operations, and the research conducted by the NIST team contributes to the ongoing efforts in ensuring the trustworthiness of AI systems.
In conclusion, the NIST Trustworthy and Responsible AI team’s research on Adversarial Machine Learning has made significant contributions in the field of AI security. The comprehensive taxonomy provided in the research paper, along with the analysis of mitigation strategies, serves as a valuable resource for understanding and addressing AML attacks. The research also calls for the development of future norms and standards to ensure the trustworthiness of AI systems.
Analyst comment
As an analyst, I would evaluate this news as positive. The research conducted by the NIST Trustworthy and Responsible AI team on Adversarial Machine Learning (AML) provides valuable insights into controlling and mitigating AML attacks. The comprehensive taxonomy and analysis of mitigation strategies contribute to future norms and standards for evaluating and controlling the security of AI systems. This research enhances the trustworthiness of AI systems and promotes a coordinated approach to addressing security challenges. The market for AI security is expected to grow as organizations prioritize the safe and reliable operation of AI systems.
