Who judges the judges? Governance from metrics: a runtime framework for continuous LLM compliance monitoring
arXiv:2605.24737v1 Announce Type: new Abstract: Current approaches to AI compliance treat conformity as a binary, audit-time verdict rather than a continuous, measurable property of production systems. We argue that this compliance fiction is structurally ill-suited to the requirements of the EU AI Act, which demands ongoing human oversight and the detection of emergent behavioural drift in deployed systems. We introduce governance from metrics, a principle whereby regulatory compliance is derived as a continuous signal from runtime observability rather than from static assessments. Building o
The increasing deployment of AI systems, particularly large language models (LLMs), in critical applications is driving urgent calls for robust, continuous compliance monitoring, especially with emerging regulations like the EU AI Act.
This paper highlights a critical gap in current AI governance, proposing a paradigm shift from static audits to continuous, runtime compliance monitoring, which is essential for managing the emergent behavior of complex AI systems.
The focus for AI compliance and regulation shifts from point-in-time assessments to ongoing observability and 'governance from metrics,' forcing developers and regulators to adopt new technical and operational frameworks.
- · AI compliance software vendors
- · Observability platforms
- · Regulatory bodies
- · Ethical AI researchers
- · Companies relying on static AI audits
- · AI deployers ignoring continuous monitoring
- · Traditional legal firms specializing in ex-post compliance
AI systems will be built with integrated monitoring and reporting capabilities from the outset, changing development lifecycles.
Regulatory frameworks will evolve to explicitly mandate and integrate continuous monitoring, potentially creating new compliance burdens but also clearer operational guidelines.
This shift could foster greater public trust in deployed AI by demonstrating verifiable, ongoing adherence to ethical and safety standards, potentially accelerating broader AI adoption.
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Read at arXiv cs.CL