Bridging the AI Security Gap: Risk, Compliance, and Innovation

Recently, attacks on machine learning (ML) systems have become a paramount concern for cybersecurity practitioners. Artificial Intelligence (AI) systems, including classical ML and generative AI platforms, are being exploited to produce harmful content, generate biased results, and facilitate data leakage. This increased use has led to a variety of challenges centering on trust, privacy, risk management, innovation, and resilience. While the technical considerations of these issues are well-studied, the organizational, consumer, and societal impacts of these threats within the context of rapidly increasing AI adoption are not fully understood. Key questions focus on the balance of traditional cybersecurity concerns with AI/ML-specific risks, the need for new skillsets for cybersecurity practitioners, and methodologies for balancing novel risks with rapid innovation. This panel brings together industry and academic experts to discuss the cybersecurity risks and challenges surrounding rising AI adoption and debate the recommended focus areas for future research and methodology development.