AI and Quality Governance
Build the governance, quality gates, and risk controls your organization needs to scale AI responsibly.
How AI & Quality Governance
Elevates Your Team.
Nearly three in four organizations are giving agentic AI access to their data and processes. Just 20% have a tested AI incident response plan. When AI can act, governance can’t be an afterthought.
What your team
walks away with.
Everything your team
will actually cover.
01 Foundations of AI Quality Governance
02 AI Regulatory & Standards Landscape: EU AI Act, NIST AI RMF & ISO 42001
03 AI Risk Identification & Assessment
04 Data Governance & Quality Controls for AI Systems
05 Model Evaluation & Assurance: Red Teaming to RAG Metrics
06 AI Governance Operating Model: Review Boards & Policies
07 Responsible AI & Ethics in Practice
08 AI Monitoring, Observability & Incident Response
09 AI Governance Maturity, Metrics & Rollout Roadmap
Hear from teams
we've trained.
“We received a solid foundation covering generative AI and RAG. The trainer never cut content to stick to the scheduled time and went beyond the allotted hours to cover everything we asked for.”
“The team was able to arrive at the same level of understanding. We're looking forward to launching agents and agentic workflows in the next few sprints, and we would look forward to collaborating again for another engagement.”
Sample trainer
profiles.
Seventeen years spanning data, cloud, automation and DevSecOps, now leading applied AI engineering at a global financial-data enterprise. Published author on generative AI.
Runs cross-functional GenAI programs for QA, dev, DevOps, data and leadership teams across four countries. Internationally certified AI trainer, Singapore-accredited.
Twelve years of hands-on automation across fintech and payments-scale platforms. Speaks and organizes meetups across the European QA community, with 863 learner reviews to date.
Profiles are anonymized at this stage. Full profiles are shared once we scope your program.
REQUEST TRAINER PROFILESChoose how
your team learns.
What makes
us different.
THE TEST TRIBEFrequently asked
questions.
What is AI quality governance?
AI quality governance is the set of roles, policies, risk assessments and controls an organization uses to make sure AI systems are accurate, robust, fair, safe and explainable, before and after they reach production, not just at launch.
How is AI governance different from traditional QA or IT governance?
Traditional QA and IT governance assume deterministic systems with fixed pass/fail tests. AI systems are non-deterministic and drift over time, so governance has to add ongoing risk assessment, model and data lineage tracking, and continuous monitoring, not a one-time sign-off.
What's the difference between the EU AI Act, NIST AI RMF and ISO/IEC 42001?
The EU AI Act is binding law that classifies AI systems into risk tiers with specific legal obligations. The NIST AI Risk Management Framework is a voluntary US framework for identifying and managing AI risk. ISO/IEC 42001 is an international standard for running an AI management system, similar in structure to ISO 27001 for information security. Many organizations use all three together.
Is this training only for QA teams and testers?
No. This program is built for anyone accountable for AI quality and risk: compliance and risk officers, engineering and product leaders, data protection officers, and QA/test leaders, not testers exclusively. The governance operating model and regulatory modules are written for cross-functional review boards, not a single team.
What is an AI review board, and does my organization need one?
An AI review board is the governance body that approves, monitors and re-approves AI systems as they change, similar to a change advisory board, but scoped to model risk, data risk and regulatory obligations. Most organizations deploying AI at scale need one once more than a handful of AI systems are in production.
Does this course cover AI red teaming and adversarial testing?
Yes. The Model Evaluation & Assurance module covers red teaming, adversarial testing and jailbreak testing as part of a broader evaluation suite, alongside benchmarking, golden datasets and RAG-specific metrics like hallucination and groundedness.
How does the program address Responsible AI?
The program covers fairness, transparency, disclosure, consent, human oversight, bias, contested decisions, and ethical trade-offs, helping participants understand how responsible AI principles can be applied in practice.
Are there any prerequisites for this training?
No prerequisites are required. The program is designed to introduce participants to AI governance concepts, frameworks, risk assessment, quality assurance, responsible AI, and production governance from the ground up.
How do you govern an AI system after it goes into production?
You’ll learn about production monitoring for drift, model degradation, cost, latency, traceability, audit trails, guardrails, and runtime policy enforcement, as well as AI incident classification, escalation, and postmortems.
Will I learn how to build an AI risk register?
Yes. You’ll learn how to create an AI system inventory and risk register, identify model, data, integration, human, and third-party risks, and assess risks using impact and likelihood scoring.
Let's
talk
Tell us where your team is today. We'll map the shortest path according to your team needs, with the right curriculum, trainers and outcomes committed before we start.