PolicyLens AI

PolicyLens AI – Autonomous Data Policy Agent

What is this build about?
PolicyLens AI is an autonomous, agentic AI system designed to automate data policy enforcement and compliance monitoring. It ingests free-text policy documents (PDF/Text), extracts enforceable rules using AI models, and applies those rules to enterprise transaction data to detect policy violations with clear, explainable justifications.

Why did we build it?
Organizations typically store compliance and business policies as unstructured documents, while their operational data changes continuously. Manual compliance checks are slow, error-prone, and do not scale. We built PolicyLens AI to bridge this gap by transforming policy language into executable compliance logic and enabling continuous, automated monitoring.

How can this be useful for others?
PolicyLens AI can be used by financial institutions, enterprises, and compliance teams to:

• Automate AML, business, and risk policy enforcement

• Continuously monitor transactional data for violations

• Generate audit-ready compliance reports

• Provide transparent, explainable compliance decisions

• Reduce manual compliance effort and operational risk

The system is designed with a modular, multi-agent architecture and can be adapted to different policy domains and datasets.

Relevant Links:

• Dataset (IBM AML – Kaggle):
  https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml

• Tech Stack: React, Node.js, SQLite, Gemini (Google Generative AI)