Governing AI When Capability Exceeds Control explores whether human institutions can still govern technologies that exceed their control.

When Geoffrey Hinton left Google to warn about extinction risks, institutions responded with ethics committees. Meanwhile, authentication systems fall to deepfakes costing millions. Workforce measurements fail as AI displaces labor. Disinformation scales while platforms profit from engagement. These current crises demonstrate systematic governance collapse.

Basil C. Puglisi presents three operational frameworks validated through sixteen years of enterprise AI transformation.

The Factics Methodology transforms governance into measurable implementation. Facts establish verifiable evidence. Tactics scale from individual actions through corporate implementations to policy interventions. Key Performance Indicators determine whether governance functions. This structure examines surveillance failures, biosecurity threats, autonomous weapons, and operational breakdowns across twelve chapters.

The HAIA-RECCLIN Framework demonstrates systematic oversight through distributed AI collaboration. Seven roles (Researcher, Editor, Coder, Calculator, Liaison, Ideator, Navigator) maintain human arbitration while accessing AI capacity. The framework prevents capability drift, preserves dissent, and maintains audit trails. Applications include healthcare governance (PPTO Framework) and institutional review (Robodebt case).

Checkpoint-Based Governance establishes measurable human arbitration points across content production, policy development, and organizational decisions. CBG provides concrete implementation with verifiable compliance markers.

The book defines temporal inseparability: institutions failing operational governance today cannot manage superintelligence oversight tomorrow. Organizations unable to handle deepfake authentication, algorithmic bias, or workforce measurement demonstrate the deficit existential risk prevention requires.

This framework emerged through operational practice. The manuscript validates governance through documented production using multiple AI platforms while human judgment arbitrated every decision. Chapter Eleven provides audit trails demonstrating distributed authority and checkpoint oversight under real constraints.

For policymakers, corporate leaders, technical researchers, and governance practitioners, this book delivers systematic oversight mechanisms proven through practice. Written in clear prose, it bridges policy analysis, enterprise implementation, and academic rigor.

Governing AI When Capability Exceeds Control provides verifiable oversight systems ready for deployment, demonstrating that governing intelligence at scale requires systematic accountability over aspirational ethics.