Securing Generative AI Workloads: A Framework for Safe and Scalable Enterprise Implementation

Abstract

As generative AI accelerates enterprise innovation, the risks surrounding data privacy, model misuse, and regulatory compliance have never been higher. While the potential for AI-driven transformation is immense, organizations must build a security-first foundation to realize that potential safely. This session introduces a comprehensive security framework for integrating generative AI in enterprise environments. It centers on three critical pillars: infrastructure security, data protection, and application security, each reinforced by responsible AI practices and regulatory readiness. Key focus areas include secure cloud configurations, encrypted communication, identity and access management, and privacy-preserving data handling. On the application layer, best practices such as input validation, output moderation, and real-time anomaly detection are essential to reducing exposure to harmful outputs and adversarial misuse. The framework also emphasizes the integration of responsible AI principles, including bias detection, toxicity assessment, and transparency. In parallel, it recommends evolving compliance strategies that proactively address emerging regulations across jurisdictions. Particular attention is given to API security, a growing threat vector in AI systems, and strategies for mitigating it through rate limiting, authentication, and continuous monitoring. This presentation offers actionable guidance for enterprise architects, security leaders, and cloud strategists looking to safely deploy generative AI at scale. Attendees will walk away with a clear roadmap to assess their current posture, implement scalable security controls, and maintain innovation without compromising compliance or trust.