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About Us
Mission Statement
AI Risk Tracker is an AI Risk Management consultancy focused on designing governance architectures, deployment safeguards, and operational risk frameworks required to build and scale AI systems responsibly.
We operate at the intersection of AI product development, risk engineering, and public policy; helping organizations move from reactive compliance to structured AI risk maturity.
We help organizations:
- Identify and assess AI failure modes prior to deployment
- Architect governance structures aligned to system-level risk exposure
- Design enterprise deployment safeguards and monitoring controls
Our Approach
AI systems do not fail randomly — they fail through identifiable failure modes across the product lifecycle.
Our work draws on structured risk methodologies, including AI Failure Mode & Effects Analysis (FMEA), governance maturity modeling, and enterprise deployment architecture frameworks.
Proactive AI Risk Engineering
AI Risk Tracker advances a proactive model of AI risk engineering — embedding risk modeling directly into product design, process workflows, and organizational governance.
Rather than treating risk as a post-deployment compliance exercise, we treat it as a core design discipline.
Experience & Credibility
AI Risk Tracker was founded by Abhishek Paul, an AI Executive and AI Risk Management author with over a decade of experience leading AI strategy, product development, and enterprise deployment initiatives across aerospace, pharmaceuticals, and advanced manufacturing.
He has partnered with senior executives within Fortune 100 environments to support AI adoption, enterprise AI strategy development, and generative AI awareness and deployment initiatives.
Abhishek is the creator of the MLOps Failure Mode & Effects Analysis (FMEA) framework — awarded Best Paper at IEEE RAMS 2024 — which formalizes structured AI risk modeling across the product lifecycle.
Throughout his career, he has raised and secured funding for AI initiatives, built 0→1 production systems, defined multi-year AI strategies, modernized internal AI infrastructure, and led cross-functional teams to develop enterprise AI standards, best practices, and scalable deployment frameworks.
He has presented at over ten industry conferences and delivered organization-wide keynote presentations on AI strategy and governance.
His academic foundation spans industrial and systems engineering, machine learning, and computer science, with studies conducted at Columbia University, MIT, and Binghamton University.