Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement

• URL: http://arxiv.org/abs/2508.18765v2
• Date: Wed, 27 Aug 2025 10:16:27 GMT
• Title: Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
• Authors: Suyash Gaurav, Jukka Heikkonen, Jatin Chaudhary,
• Abstract summary: We introduce Governance-as-a-Service (G): a policy-driven enforcement layer that regulates agent outputs at runtime.<n>G employs declarative rules and a Trust Factor mechanism that scores agents based on compliance and severity of violations.<n>Results show that G reliably blocks or redirects high-risk behaviors while preserving throughput.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural risk. Existing oversight mechanisms are reactive, brittle, and embedded within agent architectures, making them non-auditable and hard to generalize across heterogeneous deployments. We introduce Governance-as-a-Service (GaaS): a modular, policy-driven enforcement layer that regulates agent outputs at runtime without altering model internals or requiring agent cooperation. GaaS employs declarative rules and a Trust Factor mechanism that scores agents based on compliance and severity-weighted violations. It enables coercive, normative, and adaptive interventions, supporting graduated enforcement and dynamic trust modulation. To evaluate GaaS, we conduct three simulation regimes with open-source models (LLaMA3, Qwen3, DeepSeek-R1) across content generation and financial decision-making. In the baseline, agents act without governance; in the second, GaaS enforces policies; in the third, adversarial agents probe robustness. All actions are intercepted, evaluated, and logged for analysis. Results show that GaaS reliably blocks or redirects high-risk behaviors while preserving throughput. Trust scores track rule adherence, isolating and penalizing untrustworthy components in multi-agent systems. By positioning governance as a runtime service akin to compute or storage, GaaS establishes infrastructure-level alignment for interoperable agent ecosystems. It does not teach agents ethics; it enforces them.

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