Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications

This publication summarizes the key takeaways from workshops overseen by the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics,Technology, and Governance at the Stanford Cyber Policy Center. The goal of this report is to “provide a high-level discussion of AI vulnerabilities” and “articulate broad recommendations as endorsed by the majority of participants at the workshop.” The article addresses the complexities of AI vulnerabilities with one participant stating, “adding a machine learning model [to a product] is two lines of code; adding defenses can take hundreds.” With the understanding of the urgency to address threats to national security, the report is divided into four parts: understanding how current cybersecurity practices can handle AI, recommendations for those currently overseeing transformations in AI business systems, recommendations about “legal issues surrounding AI vulnerabilities” and ways that government and policy can create more safe AI systems.