How to read this whitepaper
UAAF is WAIG Foundation's operational assurance framework for boards, risk officers, and AI program leads. Each section builds on the last — start with the executive summary, then architecture and domains, before maturity and certification.
- Executives & boards — Sections 01, 04 (AITI), and 07 (certification tiers)
- Risk & compliance — Sections 03 (seven domains) and alignment with ISO 42001, NIST AI RMF, EU AI Act
- Engineering & security — Sections 02 (architecture), 05 (maturity), and 06 (continuous analytics)
License: UAAF is proprietary. Contact WAIG before using it in audits, product claims, or partner offerings.
Artificial Intelligence is transforming industries, governments, and societies at unprecedented speed. The global AI market is projected to exceed $1.8 trillion by 2030, with over 72% of enterprises deploying at least one AI system in production.
However, trust in AI systems remains fragmented. Current approaches focus on model testing, security validation, compliance documentation—yet there is no unified operational framework that continuously assures AI systems throughout their lifecycle.
WAIG Foundation introduces UAAF – Unified AI Assurance Framework for responsible AI governance and assurance.
- •Fragmented governance tools
- •No continuous assurance
- •Reactive risk management
- •No unified trust metric
- •Siloed compliance
- ✓Unified assurance framework
- ✓Continuous lifecycle monitoring
- ✓Proactive risk intelligence
- ✓AI Trust Index (AITI)
- ✓Integrated compliance mapping
UAAF Architecture
UAAF connects live AI systems to assurance outcomes through a continuous flow: telemetry feeds evaluation engines, controls reduce risk, scoring produces the AI Trust Index, and certification plus board reporting close the loop.
Telemetry → Evaluation → Controls → Risk → Scoring → Certification & Board
The Seven Pillars
Seven weighted assurance domains form the foundation of UAAF scoring and certification. Security and governance carry the highest weight because they anchor board-level accountability.
- • AI policy management
- • Risk appetite definition
- • Model inventory governance
- • Board reporting & RACI
- • Data quality validation
- • Data lineage tracking
- • Bias analysis at source
- • Consent & privacy governance
- • Accuracy validation
- • Explainability (XAI)
- • Robustness testing
- • Model drift management
- • Prompt injection testing
- • Adversarial AI testing
- • Data leakage prevention
- • OWASP LLM Top 10
- • CI/CD governance for AI
- • Deployment controls
- • Rollback mechanisms
- • Performance observability
- • Human-in-the-loop validation
- • Ethical oversight committee
- • Fairness monitoring
- • Impact assessment
- • Runtime telemetry
- • Continuous risk scoring
- • Incident management
- • Re-certification triggers
- • Anomaly detection
- • Automated escalation
AI Trust Index (AITI)
A board-level metric quantifying AI trustworthiness as a composite score across all seven domains.
UAAF Maturity Model
Most enterprises at L2 — UAAF targets L3+ within 18 months
Analytics Dashboard
WAIG-Certified AI
Four-tier certification from foundational assurance to continuous trusted AI.
- • Basic security
- • Initial governance
- • Model docs
- • Maturity L1–L2
- • Full security test
- • Bias assessment
- • Defined governance
- • Maturity L2–L3
- • Comprehensive val
- • Continuous monitor
- • Explainability
- • Maturity L3–L4
- • Real-time assurance
- • Self-healing
- • Board trust intel
- • Maturity L4–L5
Proprietary framework — approval required
The Unified AI Assurance Framework (UAAF), AI Trust Index (AITI), maturity model, domain weightings, certification criteria, and related methodologies are proprietary to WAIG Foundation.
No license is granted by viewing or downloading this whitepaper. Organisations must obtain written approval from WAIG Foundation prior to: adopting UAAF operationally; referencing UAAF in procurement or marketing; delivering UAAF-based assessments; claiming WAIG certification alignment; or creating derivative assurance frameworks.