Get Ahead of the Game: LLM Compliance and Mocking

As enterprises adopt large language models, they must address compliance risks involving sensitive-data exposure, incomplete audit trails, non-deterministic outputs, testing costs, and regulations such as GDPR, HIPAA, SOX, and emerging AI governance standards. The material presents advanced LLM mocking, particularly through Proxymock’s capture-and-replay approach, as a way to record realistic interactions while sanitizing sensitive information, preserving conversational context, validating responses for privacy, accuracy, tone, and bias, and maintaining auditable logs. It recommends a phased implementation covering regulatory assessment, data-flow mapping, governance policies, compliant infrastructure, CI/CD integration, automated monitoring, and metrics for audit readiness, test coverage, exposure risk, and cost efficiency. Reported examples from financial services and healthcare claim substantial reductions in API costs and compliance-review time while maintaining audit and privacy requirements, and the proposed best practices emphasize layered protections, continuous compliance enforcement, thorough documentation, and preparation for future regulations and AI capabilities.

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