Built from doctoral research in AI governance for regulated industries. Each item is publicly verifiable and in active use or development.
A structured Jira workflow that embeds the governance decisions that must exist before any AI agent build starts. PM, architect, and risk manager each answer specific questions at the point of ticket creation. The answers become the evidentiary record. No separate documentation process. No additional meetings. The record is a byproduct of the workflow your team is already running.
A decision-tree tool that takes an AI use case and maps it to every applicable US regulation across the current knowledge base. Surfaces obligations where formal regulatory coverage exists and flags gaps where no safe harbour applies. Built on 56 regulations across Life Insurance, Health Insurance, Banking and Lending, and FinTech spanning federal and state jurisdictions.
Scans AI agent codebases and configuration files against regulatory frameworks and documented organisational policies. Identifies gaps between what was designed and what was built. Generates audit-ready findings mapped to specific regulatory obligations.
This project builds an AI-assisted compliance evaluation system for Indian data protection compliance. The system checks whether a company’s policies and internal documents are correctly aligned with the Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025. The user uploads company policies and fills a company profile form. The system then determines which legal obligations are applicable, maps those obligations to evidence found in the uploaded documents, identifies missing or weak implementation, computes a compliance score, suggests remediations, and generates a structured report. This system is not intended to replace a lawyer or compliance officer. It is intended to act as a structured compliance analyst that performs document review, evidence extraction, obligation matching, and first-level risk scoring.
Repository: PrivateSenior governance role responsible for designing, operationalising, and enhancing enterprise-level governance for AI and Agentic AI systems. Requires deep understanding of responsible AI, AI risk management, agentic AI and LLM architectures, and enterprise governance frameworks. Specific focus on NIST AI RMF, EU AI Act, and ISO/IEC standards. Cross-functional accountability across technology, risk, and business functions. 8-10 years total experience, minimum 5 in AI governance.
The Apple Card failures of 2019 and 2024 occurred under US jurisdiction: CFPB, TILA, and NYDFS regulatory authority. The EU AI Act, DORA, and NIST AI RMF are applied prospectively throughout, as the analytical lens through which comparable deployments should be evaluated today. This bundle does not constitute a legal finding, a regulatory determination, or an audit of Apple or Goldman Sachs.
Originally developed as a practitioner submission for an AI governance consulting role. Published here as a practitioner reference for governance professionals in regulated financial environments.