Policy- and Tenant-Aware AI Fabric: Embedding Trustworthy Machine Learning into Enterprise SaaS at Scale

This paper introduces PTAAF, a policy- and tenant-aware framework designed for the integration of machine learning into extensive multi-tenant Software as a Service (SaaS) platforms. PTAAF combines a governed data/feature fabric, privacy and fairness constraints (DP-SGD, shape-constrained models), and a Policy- and Tenant-Aware Value Optimizer (PTAVO) that chooses the model, route, and explanation strategy for each request based on latency, cost, carbon, and audit constraints. To use generated enterprise-like event streams and cleaned up online corpora (job/skills, invoices, demand signals) to test four typical use cases. PTAAF boosts AUC by 7–9 points and F1 by about 10% compared to baselines. It also cuts p95 latency by about 45%, doubles throughput through cache-aware routing, and lowers inference cost by about 35% while cutting carbon emissions by about 44%. Governance results show that (ε,δ)-DP = (3.2, 1e-5), there is a 73% drop in demographic-parity gaps, a 95% pass rate for audit checks, and drift indices that are three times lower, which means that retraining happens half as often. A lineage DAG connects data, features, models, DP budgets, and explanations in a way that can be replayed. The results show that value-optimized, constraint-aware AI can be used safely, reliably, and responsibly in enterprise SaaS. This gives organizations a model for how to use ML while still following rules about compliance, fairness, and reliability.