What question did this study set out to answer?

The aim is to frame the governance practices of AI throughout its lifecycle, highlighting governance gaps.

March 7, 2026Open Access

Four Stages of AI Governance: A Stage-Based Framing of Contemporary Practices

Key Points

The aim is to frame the governance practices of AI throughout its lifecycle, highlighting governance gaps.
Proposed a stage-based framework for AI governance practices
Identified four stages of AI governance
Analyzed contemporary governance mechanisms
Noted a concentration on runtime and post-hoc controls
Found engineering-stage governance to be underdeveloped
Clarified conceptual boundaries of governance approaches

Abstract

Abstract This paper proposes a stage-based framing of AI governance practices.Rather than focusing solely on runtime controls or post-deployment oversight,we examine governance mechanisms across the lifecycle of AI-assisted software development. We identify four stages of AI governance: 1. Constraint Formation2. Development-Stage Governance3. Runtime Governance4. Post-Hoc Oversight The analysis highlights that many contemporary governance mechanismsconcentrate on runtime and post-hoc controls,while governance during the engineering stage remains structurally underdeveloped. This framing clarifies the conceptual boundaries of governance approachesand provides a foundation for future engineering-stage governance research.

Four Stages of AI Governance: A Stage-Based Framing of Contemporary Practices

Key Points

Abstract

Cite This Study

Also Consider

Also Consider