AI‑Driven Meta‑Governance: How Autonomous Systems Can Govern Other AI Models in Real Time

Abstract

The increasing deployment of autonomous artificial intelligence (AI) systems has created a need for governance mechanisms capable of monitoring, evaluating, and controlling AI behavior in real time. Traditional governance approaches often rely on predefined policies, periodic audits, and human supervision, which may be insufficient for complex, rapidly evolving, and multi-agent environments. This paper examines AI-driven meta-governance, an emerging approach in which autonomous supervisory systems oversee other AI models and agents to enforce policies, detect risks, and support responsible decision-making. It proposes a layered conceptual framework integrating continuous monitoring, policy-as-code, automated risk assessment, adaptive enforcement, auditability, and human oversight. The framework considers governance performance across cybersecurity, healthcare, enterprise digital twins, and decentralized AI ecosystems. Key evaluation dimensions include policy compliance, violation-detection accuracy, response latency, scalability, transparency, and accountability. The study also examines challenges involving cascading failures, conflicting policies, adversarial manipulation, excessive autonomy, and the reliability of AI-based supervisory decisions. It argues that effective meta-governance requires a balance between autonomous enforcement and human control, supported by clear accountability structures, independent validation, and transparent intervention mechanisms. By connecting AI governance principles with operational control architectures, this study contributes a conceptual foundation for developing adaptive, secure, and trustworthy governance systems for increasingly autonomous AI ecosystems.