AI Governance, Risk & Compliance Practitioner | NIST AI RMF, ISO/IEC 42001 & EU AI Act | AI Risk, Controls & Compliance
Rumteen Tebyanan is an AI Governance, Risk, and Compliance Practitioner focused on helping organizations responsibly evaluate, document, and govern AI systems.
My work focuses on practical AI governance: identifying AI risks, documenting AI use cases, assessing controls, reviewing vendor AI systems, supporting human oversight, and preparing governance evidence for leadership review.
I am building hands-on experience applying major AI governance frameworks, including:
- NIST AI Risk Management Framework
- ISO/IEC 42001
- EU AI Act
- Responsible AI and AI assurance concepts
- AI risk, control, monitoring, and evidence practices
I am especially interested in how organizations can safely deploy AI systems that affect people, business decisions, financial outcomes, compliance obligations, and trust.
My goal is to support responsible AI adoption by helping organizations make AI systems more governable, explainable, fair, accountable, monitored, and compliant.
| Area | Focus |
|---|---|
| AI Governance | AI policies, governance workflows, accountability, approvals, and oversight |
| AI Risk Management | Risk identification, risk registers, mitigation planning, residual risk, and monitoring |
| NIST AI RMF | Applying Govern, Map, Measure, and Manage to real AI use cases |
| ISO/IEC 42001 | AI management system concepts, roles, responsibilities, risk treatment, and evidence |
| EU AI Act | High-risk AI classification, transparency, human oversight, logging, and FRIA concepts |
| Vendor AI Risk | Third-party model review, vendor evidence, model limitations, and accountability |
| Human Oversight | Human review triggers, escalation, override authority, and appeal processes |
| AI Assurance | Documentation, explainability, traceability, audit readiness, and governance evidence |
In this project, I acted as the AI Governance Lead for a fictional financial services company evaluating a high-risk AI-powered loan underwriting system.
The system used a third-party AI model to evaluate small business loan applications and produce one of three outcomes:
- Auto-approve
- Auto-deny
- Route to manual review
Because the system affected access to credit and automated approximately 94% of application decisions, I assessed it as a high-risk AI use case.