4iGov is a practitioner-and-research studio for AI governance in regulated industries. We turn doctoral research into tooling that makes the evidentiary record a byproduct of the work teams already do, not a separate documentation exercise written after the fact.
The accountability gap that governance frameworks describe, and the risk-acceptance gap that product-security teams live with, are structurally the same problem.
4iGov works at that boundary, where what engineering teams build meets what regulators expect to find. As AI systems move into consequential decisions, the question stops being whether a decision was right. It becomes whether anyone can account for how it was reached.
Each thing we publish is built to be run, not just read: publicly verifiable, and in active use or development.
A Jira workflow that embeds the governance decisions which must exist before any AI agent build begins. PM, architect and risk manager each answer specific questions at the point of ticket creation; the answers become the evidentiary record.
Maps an AI use case to every applicable US regulation across the knowledge base, and flags the gaps where governance expectations persist but no safe harbour applies, built on 56 regulations across insurance, banking and FinTech.
Scans agent codebases and configuration against regulatory frameworks and documented organisational policy, surfacing where what was built has diverged from what was designed.
The core argument, written for practitioners already living with these problems: why accountability is assumed but rarely architected, and what structural conditions separate defensible risk management from compliance theatre.
Plus standalone analysis on the DPDP Act, the Five Eyes agentic-AI guidance, UAE financial-sector governance, and product-security practice. Read all writing →