Trusted Enterprise AI: Governance, Explainability, and Compliance for Mission-Critical Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I3P116Keywords:
Trusted Enterprise AI, AI Governance, Explainable AI, Responsible AI, AI Compliance, AI Auditability, Decision Traceability, Human-in-the-loop, Model Governance, Model Risk Management, AI Observability, Enterprise Architecture, Mission-Critical Systems, Continuous ComplianceAbstract
Deploying AI in audited, business-critical enterprise systems requires more than model accuracy. Decisions must be traceable, explainable, governed and defensible to business stakeholders and auditors, because an incorrect decision does not stay inside the application: it propagates into accounting records, regulatory reports and operational actions. This paper proposes a Trusted Enterprise AI reference architecture that embeds governance, explainability, human oversight, continuous monitoring and compliance controls across the AI lifecycle rather than attaching them after deployment. The contribution is not a new explainability algorithm or compliance standard; it is an architectural pattern for making AI decisions governable by design. Each consequential decision generates a traceable evidence chain connecting model and version, input context, explanation, confidence, applicable policies, control checks, human intervention where required, and the resulting business action, with continuous monitoring extending those controls afterward to detect drift, abnormal behavior and policy violations. Trust is decomposed into governance, explainability and compliance, each with a concrete control, a named owner, and evidence of execution rather than evidence of intent. The framework has not been implemented end to end as a single production system, and no governance outcomes are claimed as measured.
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