AI Data Security Framework for Regulated Environments

This technical report presents a framework for AI data security specifically designed for regulated environments. It outlines the critical importance of data labeling and classification as foundational steps before deploying AI systems. The report identifies four key pillars essential for ensuring compliance and security: first, organizations must classify and label their data accurately; second, they need to define a compliance boundary for AI operations; third, security controls must be applied throughout the entire AI data lifecycle; and fourth, continuous governance of the AI system is necessary post-deployment. The framework references several compliance standards, including the CMMC Program and NIST guidelines, emphasizing that regulated organizations must know the location and classification of their sensitive data. The report also discusses the implications of AI on data handling and compliance, noting that failure to manage data appropriately can lead to significant risks in regulated sectors such as defense, healthcare, and finance. It concludes with recommendations for organizations to implement robust data governance practices.