Abstract
The proliferation of artificial intelligence systems across critical infrastructure necessitates a paradigm shift from traditional software quality assurance toward probabilistic validation methodologies. This paper presents a comprehensive framework for AI model testing that synthesizes regulatory requirements across multiple jurisdictions—including the EU AI Act (Regulation 2024/1689), NIST AI Risk Management Framework 2.0, Federal Reserve SR 11-7, Colorado AI Act, NYC Local Law 144, and sector-specific regulations—into an integrated validation architecture applicable across financial services, healthcare, insurance, manufacturing, and retail sectors.
The framework encompasses twelve distinct testing dimensions: functional correctness, performance benchmarking, bias and fairness assessment, robustness and adversarial resilience, explainability validation, security and privacy protection, data quality assurance, regulatory compliance verification, agent safety evaluation, drift detection and monitoring, human-AI interaction testing, and documentation completeness. Technical implementation guidance includes statistical methodologies, tool ecosystems, 50+ metric definitions with mathematical formulations, and threshold calibration approaches aligned with ISO/IEC 42001, ISO/IEC 29119-11, and IEEE 7000 series standards.
Key contributions include: (1) unified regulatory compliance matrix mapping testing requirements across 15+ frameworks; (2) technical specifications for validation metrics; (3) industry-specific testing protocols for high-risk AI applications; (4) evaluation methodologies for large language models, generative AI, and autonomous agent systems; and (5) MLOps integration patterns for continuous validation in production environments. The framework addresses non-deterministic behaviors, emergent properties, distribution drift, adversarial vulnerabilities, and societal impact dimensions inherent to modern AI systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bharathan, Ramkumar, "Comprehensive Framework for Artificial Intelligence Model Testing: Integrated Validation Methodologies, Global Regulatory Compliance, and Enterprise Implementation Strategies for 25 years", Technical Disclosure Commons, (February 11, 2026)
https://www.tdcommons.org/dpubs_series/9319