Ensuring AI Security and Safety: Navigating the Regulatory Landscape

The MITRE research "Assuring AI Security and Safety Through AI Regulation" emphasizes the crucial need to build a comprehensive and effective regulatory framework for AI security and safety. As the next government prepares to address this critical issue, there is an urgent need to strike a balance between technological advances, ethical issues, and public trust. The goal is to strengthen the United States' international leadership in AI while also using its revolutionary potential to address a wide variety of pressing concerns.

Over the last decade, advances in artificial intelligence (AI) have heralded a new era of technological innovation, with the promise to address crucial concerns in a variety of disciplines, including healthcare and national security. Each new presidential term provides an opportunity to evaluate and improve our approach to these quickly evolving technology. The future administration must be knowledgeable about the current status of AI, its potential consequences, and the significance of developing a reasonable legislative framework for AI assurance. While existing policy and legislative initiatives have begun to address the need for AI regulation, more work is needed to guarantee that this technology is applied and used properly, while balancing security, ethical considerations, and public confidence.

Because of AI's rapid growth and numerous applications, regulation creates new issues. Bridging the gap between policymakers at the Executive Office of the President (EOP) and agency execution is a considerable challenge. It is critical to ensure that executive-level policies are properly translated into action at the agency level, while taking into account each agency's specific needs and circumstances. Developing sector-specific AI assurance standards that take into account use cases, as well as implementing the National Institute of Standards and Technology's (NIST) AI Risk Management Framework (RMF) across sectors, pose substantial obstacles. These actions are required to guarantee that AI applications meet safety and performance standards while effectively managing risks.

One of the most difficult difficulties in AI legislation is ensuring system auditability and enhancing openness in AI applications. These measures are critical for identifying AI usage and establishing accountability within businesses. However, they present obstacles owing to the complexity of AI systems and the existing lack of technical skills required to effectively integrate and manage these processes. Despite these limitations, there is enormous potential. Rethinking regulatory and legal frameworks can help guide federal funding decisions, accelerate AI research, and promote responsible AI use while discouraging misuse. Strengthening critical infrastructure plans and encouraging continual regulatory analysis can help to protect our critical infrastructure from exploitation by humans, AI-augmented people, or malevolent AI agents.

Furthermore, the different needs and requirements of agencies depending on size, organization, budget, mission, and internal AI talent provide an opportunity to encourage flexibility and adaptability in AI governance. A successful approach to AI regulation should enable the personalized and effective execution of AI strategies and policies across agencies. This includes enabling each agency to develop an AI strategy that is tailored to its needs and level of AI maturity. The guidelines should give a variety of AI governance structures, processes, and practices, allowing agencies to select the ones that best suit their individual needs while maintaining minimal requirements for consistency and effectiveness. As AI technologies rapidly change, these standards must be adaptable to accommodate ongoing innovation and changing expectations about what is achievable.

The paper makes many data-driven recommendations to overcome these difficulties and capitalize on the opportunities created by AI policy. First and foremost, policymakers and those implementing AI plans must improve communication and collaboration. This can be accomplished by assessing existing EOP-interagency groups, broadening their responsibilities, changing their makeup, or increasing their funding. Developing sector-specific AI assurance requirements and plans in partnership with stakeholders guarantees that the usage of AI in specific contexts satisfies relevant safety and performance standards while also managing associated risks.

Promoting the recently established AI Information Sharing and Analysis Center (AI-ISAC) can speed up the sharing of real-world assurance incidents, resulting in a better knowledge of threats, vulnerabilities, and hazards to AI technology adoption. Supporting an at-scale AI Science and Technology Intelligence (AI S&TI) apparatus to monitor enemy AI tradecraft is critical for understanding how adversaries utilize AI to gain a global advantage and determining the extent of adversary capabilities inside the United States.

An executive order mandating system auditability and increasing openness in AI applications is another critical step. This includes asking AI developers to share the data used to train their systems as well as the models that served as the foundation for their systems. Promoting AI principles aligned practices and developing regulatory and legal frameworks for AI systems with increased agency guarantees that AI is developed and used safely and responsibly. To protect critical infrastructure from exploitation by humans, AI-augmented people, or malevolent AI agents, critical infrastructure planning must be strengthened and continual regulatory examination encouraged.

Finally, establishing a National AI Center of Excellence (NAICE) to promote and coordinate these priorities can benefit from threat and risk assessments from the AI-ISAC and AI S&TI. The NAICE should not only promote AI assurance frameworks and best practices, but also take the lead in performing cutting-edge applied research and development in AI. This includes creating new AI technologies, processes, and tools that can be used in a variety of industries, promoting collaboration across industry, government, and academia.

Implementing these proposals will necessitate a combination of knowledge, collaboration, financing, infrastructure changes, ongoing learning resources, and flexibility in AI governance. The report proposes a timeline and milestones to guide the process, beginning with the evaluation of existing EOP-interagency committees in the first 100 days, followed by monitoring the initial implementation of the AI assurance process in the first six months, increased federal funding for AI alignment research in the first year, and continuous monitoring of AI development and use to propose regulatory updates as needed.

By tackling these obstacles and capitalizing on the potential given by AI legislation, the new administration may assure a balanced approach to technological advancement, ethical considerations, and public trust. This will not only strengthen the United States' international leadership in AI, but will also unleash its revolutionary power to address a wide variety of crucial concerns.