Governance, Reliability, and Security Convergence in Enterprise AI Cloud Platforms

Governance, reliability, and security are no longer separable concerns for enterprise AI on the cloud; they share a substrate, a failure surface, and a regulatory mandate. This paper consolidated field-tested practices for each and organized them through the GRS convergence framework, which reuses a small set of platform mechanisms — lineage, shared telemetry, zero-trust identity, and pipeline-enforced policy — to serve all three at once. An illustrative assessment suggested that convergence shortens detection and mitigation time and reduces cross-team handoffs, particularly for ambiguous events such as poisoning that resembles drift. The practices can be adopted incrementally: begin with shared telemetry, add lineage and enforced approval gates, then converge access control and rollback. We encourage practitioners to validate the framework with measured data from their own platforms and against the requirements of their target venue and regulators.

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