WAIG Academy Learning Platform

Introduces artificial intelligence concepts, risk taxonomy, and governance vocabulary for non-technical leaders and new practitioners — the entry point for all WAIG training pathways.

Hands-on ML lifecycle training covering data pipelines, model validation, drift monitoring, and governance gates in MLOps — aligned with UAAF Model and Data Assurance domains.

Comprehensive LLM track covering architecture, retrieval-augmented generation, agent orchestration, evaluation, and production guardrails for enterprise and sovereign deployments.

Specialised track for multimodal models — vision-language, speech, and document AI — with emphasis on deepfake risk, content authenticity, and sector-specific controls (health, finance, media).

Dedicated AI security program covering adversarial ML, LLM attack surfaces, supply chain risk, and continuous security testing — directly linked to WAIG AI Testing Lab methodologies.

Stream-aligned modules for CS, IT, and software engineering students — embedding responsible AI, MLOps governance, and assurance project work into existing semester structures.

Curriculum modules emphasising data lineage, bias analysis, model documentation, and statistical rigour — the data assurance backbone of UAAF Domain A2.

Non-technical track for future policymakers, legal counsel, and managers — EU AI Act, DPDPA, contract clauses for AI procurement, and board governance.

Sector-specific curriculum for health AI — clinical validation, patient safety, diagnostic AI governance, and alignment with Health & Pharma conclave themes.

End-to-end enterprise program for CIO, CISO, CRO, and AI CoE teams — operating model design, control libraries, assurance cadence, and ISO 42001 certification readiness.

Sector pack for banking, insurance, and fintech — model risk management, explainability for credit decisions, and regulator-ready documentation.

Training for ministries, state IT departments, smart city programs, and public-sector AI initiatives — sovereign deployment patterns, procurement governance, and citizen trust.

Dedicated pathway for women professionals and students entering AI governance, assurance, and policy — combining technical modules, mentorship from WAIG advisors, and leadership coaching.

Structured internships and fellowships with WAIG Foundation — contributing to assurance assessments, whitepaper research, chapter events, open-source governance tools, and community programs.

Community educators and volunteers who deliver introductory AI governance workshops in schools, colleges, and local institutions under WAIG chapter guidance.

Foundational program covering ISO/IEC 42001 AI Management System (AIMS) requirements — context, leadership, planning, support, operation, performance evaluation, and improvement.

Practitioner program for implementing ISO 42001 AIMS — gap analysis, control design, documentation, training, and pre-certification readiness.

Advanced auditor program aligned with ISO 19011 — audit planning, evidence collection, nonconformity classification, and certification audit simulation for AI Management Systems.

Specialized track for AIMS audit execution — combining ISO 42001 clause review with AI-specific evidence (models, data, LLM agents, monitoring).

Applies ISO 31000 risk management principles to AI — risk criteria, assessment, treatment, monitoring, and board reporting integrated with AI governance.

Comprehensive EU AI Act program — prohibited practices, high-risk requirements, GPAI obligations, conformity assessment, and post-market monitoring.

Hands-on NIST AI Risk Management Framework adoption — profiles, controls, metrics, and continuous improvement for AI systems.

Bridges NIST SP 800-37 and AI-specific risk — authorization boundaries, control baselines, and continuous monitoring for AI-enabled systems.

Introduction to AI testing — test planning, datasets, metrics, regression suites, and defect taxonomy for ML and LLM systems.

Methodology for conducting AI assessments — scoping, control evaluation, maturity scoring, and board-ready assessment reports.

QA/QC program for AI — data quality control, model validation gates, release checklists, and production monitoring QC.

Advanced practitioner track for WAIG AI Testing Lab — red-team exercises, multi-engine testing, and assurance report generation.

Licensed practitioner program for UAAF — seven domains, control libraries, assessment workflows, and operational deployment (license required for production use).

Maps classic OWASP Top 10 risks to AI application stacks — APIs, auth, injection, and data exposure in LLM-enabled web apps.

Dedicated program for OWASP LLM Top 10 and AI security risks — prompt injection, training data poisoning, supply chain, and excessive agency.

Practitioner program for OWASP AIVSS — scoring AI-specific vulnerabilities, misuse scenarios, and remediation prioritization.

Build and maintain AIBOMs — model provenance, dataset lineage, plugin inventories, and third-party AI API dependencies.

Design secure AI systems from inception — threat modeling, secure defaults, zero-trust for agents, and security gates in MLOps.

Integrate governance checkpoints into design, development, and deployment — policy-as-code, approval workflows, and evidence automation.

Secure coding practices for AI — input validation, output encoding, secrets hygiene, sandboxing tools, and secure RAG patterns.

Govern rapid AI-assisted coding — prompt patterns, review gates, test generation, and policy checks when using AI coding assistants.

Navigate open-source licenses in AI — MIT, Apache, GPL implications, model licenses, dataset terms, and compliance workflows.

Understanding proprietary assurance IP — UAAF license tiers, restricted use, partner agreements, and operational boundaries.

Intellectual property for AI platforms — trademarks, trade dress, brand usage guidelines, and enforcement basics.

Patent fundamentals for AI — patentability, prior art, provisional vs complete applications, PCT strategy, and trade secret vs patent decisions.

Executive capstone integrating ISO 42001, EU AI Act, NIST AI RMF, UAAF, and board governance — for leaders running enterprise AI assurance programs.