AI-Assisted Governance Agent for a Data Clean Room Platform

Overview

The project involved designing and productionizing an AI-assisted governance and code review system for a privacy-preserving Data Clean Room platform. The platform was built to support secure data collaboration across isolated client environments, where SQL, PySpark, Scala, and pipeline definitions had to comply with strict privacy, PII, data-contract, and governance rules. The AI assistant was designed to analyze engineering changes before execution, detect potential policy violations, explain risks, and provide actionable recommendations to engineers. Instead of using an unconstrained chatbot approach, the solution combined LLM reasoning with structured platform context, explicit governance rules, deterministic validation checks, and human-in-the-loop approval flows.

Achievements

The solution was moved from PoC to production-ready implementation in approximately 1.5 months. It helped reduce manual first-line review effort, improved consistency of governance checks, and created a scalable foundation for AI-assisted engineering workflows within a data-sensitive platform. The implementation demonstrated that LLM-based assistants can be safely applied to enterprise data engineering when constrained by bounded context, platform rules, and deterministic validation layers.

Responsibilities

• Designed the AI assistant architecture for governance-aware review of SQL, PySpark, Scala, and data pipeline changes.
• Integrated LLM capabilities through AWS Bedrock while keeping platform-specific context, policies, and execution boundaries under control.
• Defined validation flows for PII exposure, data contract violations, unsafe transformations, and non-compliant data access patterns.
• Created structured context packages containing platform rules, architecture constraints, repository conventions, and review criteria.
• Implemented a human-in-the-loop review model to ensure that AI recommendations remained explainable, auditable, and safe for production use.
• Designed the productionization path, including backend deployment, IAM boundaries, async validation flows, and operational monitoring considerations.
• Evaluated MCP/MaaS-style integration patterns to expose platform capabilities to AI agents without coupling the solution to a single assistant UI.

Technologies Used