Authors - K S Shubham, Uma Mudengudi, Ujwala Patil Abstract - Secure, compliant, and interoperable data sharing remains a core bottleneck for cross-organizational analytics and AI, particularly under evolving privacy regulations, contractual obligations, and adversarial threats. This paper introduces HARMONIA, a pluggable, risk-aware data sharing framework that integrates policy-as-code enforcement, continuous compliance monitoring, provenance-grade evidence, and revocation with machine unlearning. HARMONIA is inspired by the iterative Analyzer–Mechanic and Conductor–Observer operational pattern described in the HARMONIA strategic perspective, generalizing its quality-gate-and-repair loop to a policyand- risk-gated release lifecycle. We formalize an architecture that separates governance, control, and data planes; define a release-mode lattice that enables explainable fallbacks among raw export, masking, kanonymity, differential privacy, synthetic data, query-only access, and federated compute; and propose an evidence model aligned with W3C PROV. We provide a proof-of-concept (POC) blueprint implemented with commodity components (OPA, OAuth2/OIDC, PostgreSQL, and object storage) and specify interfaces that support end-to-end request-to-release-to-revocation workflows, including batch-scoped unlearning for model derivatives. The paper concludes with an evaluation methodology and a standards-aligned roadmap for deployment in sovereign data spaces.
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