AI Lifecycle Audit & Governance Framework (ALAGF)

The AI Lifecycle Audit & Governance Framework (ALAGF) establishes a comprehensive architecture for responsible, standards-aligned artificial intelligence (AI) governance across the entire AI lifecycle—from data collection and model design to deployment, monitoring, and decommissioning. Authored by Dale Rutherford (The Center for Ethical AI, University of Arkansas at Little Rock), ALAGF provides organizations with a modular and operationally scalable approach to managing ethical, regulatory, and performance risks in intelligent systems. Now in its second edition, ALAGF integrates and harmonizes with international standards including ISO/IEC 42001, the NIST AI Risk Management Framework, ISO/IEC 23053, and the EU AI Act. It introduces a metric-based governance layer built on diagnostic indicators such as the Bias Amplification Ratio (BAR), Echo Chamber Propagation Index (ECPI), Information Quality Decay (IQD), and Pre-Training Diversity Index (PTDI). Together, these metrics form a quantitative foundation for monitoring bias, misinformation, and epistemic degradation throughout system operation. As the architectural core of a broader governance ecosystem—including the Bias, Misinformation & Error Framework (BAAGF), the Multi-Dataset IQ Drift & Cost Optimization Tool (MIDCOT), and the SymPrompt structured interaction model—ALAGF unites ethical reasoning with measurable oversight. It bridges compliance, strategy, and technical execution through practical appendices such as governance checklists, role matrices, deployment templates, and KPI dashboards. Designed for both enterprise leaders and small to mid-sized organizations, ALAGF empowers practitioners to implement AI governance that is not merely reactive or regulatory, but structural, transparent, and symbiotic. It advances the concept of ethics as infrastructure, reframing governance as a catalyst for innovation, trust, and long-term resilience in intelligent systems.