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September 10, 2025Open Access

Semantic Compliance Architecture for Multi-Tenant Telehealth

Key Points

The framework improves compliance logic in telehealth platforms, enhancing accountability and transparency in AI use.
Key implementation showcased ability to detect policy violations and provide interpretable reasoning traces for regulatory compliance.
This approach employs methods like Bayesian updating and likelihood ratio testing, ensuring robust compliance across evolving legal norms.
The modular architecture allows updates to compliance logic independently, adapting to jurisdiction-specific regulations seamlessly.

Abstract

As AI systems become increasingly embedded in multi-tenant platforms, ensuring regulatory compliance and accountability requires more than ad hoc rule encoding. In the absence of a formal mathematical foundation, compliance logic cannot be guaranteed to remain consistent under evolving legal norms. This paper introduces a decision-theoretic semantic compliance framework that integrates a rigorous probabilistic reasoning model with ontology- and rule-based representations. Legal obligations are encoded in OWL ontologies and SWRL rules, while compliance judgments are derived through a mathematically grounded chain that includes prior probability estimation, Bayesian updating, likelihood ratio testing, and log-likelihood ratio decomposition. The architecture is modular and extensible, encapsulating compliance logic in a semantic layer that can be updated independently of the core platform. A prototype implementation in a healthcare scenario demonstrates the framework’s ability to detect policy violations, provide interpretable reasoning traces, and adapt to jurisdiction-specific regulations. The approach offers a verifiable and reusable compliance model applicable to a wide range of high-stakes, regulation-intensive domains, enhancing both transparency and trust in AI-enabled decision-making.

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