Munisekhar Katta
Production deployments of enterprise Business Process Management (BPM)applications in regulated financial services environments carry significant operational risk: deployment failures can cause customer-facing disruptions, revenue loss, and regulatory scrutiny.Despite advances in CI/CD automation, deployment risk assessment on enterprise BPM platforms remains a largely manual, experiencedependent process that does not systematically use the telemetry already produced by modern deployment orchestration tools.This paper proposes DRPA (Deployment Risk Prediction Architecture), a machine learning framework designed to integrate with enterprise BPM deployment pipelines as an automated risk-scoring quality gate prior to production promotion.DRPA is designed to combine four signal dimensions: (1) structural complexity of the deployment artifact, (2) configuration drift between staging and production environments, (3) learned patterns from historical deployment outcomes, and (4) dependency-graph analysis of cascading impact across interconnected application components.We describe the proposed architecture in detail and report an illustrative, author-constructed simulation intended to test the internal logic of the scoring approach and surface design questions, informed by the author's operational experience administering multi-stage deployment pipelines in a commercial lending BPM environment.The simulation is not an empirical evaluation: it uses synthetic, hand-parameterized data rather than measured production telemetry, and its directional findings -that combining artifact-level, environment-level, historical, and dependency-graph signals appears more informative than any single dimension alone -should be treated as motivation for a real pilot study rather than as evidence of real-world performance.We describe the audit-traceability properties the design is intended to support for SOX-and PCI-DSS-relevant change management, and we lay out the empirical validation this proposal still requires.