Praetorian security researchers have published a comprehensive lifecycle analysis identifying the specific points where AI systems expose sensitive data throughout their operational journey. The research maps data leakage vectors from training through deployment and ongoing operations, revealing that many organizations significantly underestimate their AI data exposure surface.

The training phase presents initial exposure risks through data collection pipelines that may inadvertently capture sensitive information, training datasets stored without adequate access controls, and model weights that can potentially leak training data through extraction attacks. Praetorian documents cases where proprietary business data became recoverable from deployed models through careful prompting techniques.

During inference and deployment, AI systems create multiple exposure pathways. Prompt logs and conversation histories often contain sensitive user inputs stored with insufficient protection. API responses may include more information than intended, and caching mechanisms designed for performance can retain sensitive data longer than security policies permit. Integration points with external services multiply exposure vectors.

The research identifies monitoring and debugging as overlooked leakage sources. Telemetry data, error logs, and performance metrics frequently capture input and output samples containing sensitive information. These operational data stores often receive less security attention than primary databases despite containing equally sensitive content.

Praetorian recommends implementing data classification throughout the AI lifecycle, deploying output filtering to prevent unintended disclosure, establishing retention policies for all AI-related data stores, and conducting regular assessments of data flow paths. Organizations should treat AI systems as data processors subject to the same governance requirements as traditional data handling infrastructure.