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The rapid expansion of multi-tenant AI data centers has intensified the need to safeguard user state and contextual information generated during inference workloads. Unlike traditional computing models, modern AI systems accumulate and reuse conversational histories, prompt sequences, embeddings, retrieval outputs, vector representations, and KV/attention-cache artifacts that may implicitly persist across inference boundaries. These contextual residues create a heightened risk of cross-session leakage, cross-tenant exposure, behavioral contamination, and unintended disclosure through retrieval pipelines, cached activations, or logging systems.Existing protections—such as static access control lists, coarse container or pod separation, encryption-at-rest, or application-layer scoping—are insufficient due to the dynamic, opaque, and state-propagating characteristics of large-scale inference pipelines. To address these challenges, this work proposes an architectural framework for contextual isolation based on end-to-end compartmentalization, policy-bound retrieval enforcement, per-tenant and per-session cache and memory segmentation, privacy-constrained telemetry flows, redacted observability pipelines, and auditable data lifecycle boundaries spanning prompt ingestion through post-inference retention.This article is structured as follows. Section I motivates the problem and security implications of multi-tenant AI inference; Section II reviews related work in confidential inference, retrieval security, and isolation mechanisms; Section III formalizes the threat model; Section IV presents the proposed contextual isolation architecture; Section V defines the evaluation methodology and metrics; Section VI discusses deployment, compliance, and scalability considerations; and Section VII concludes with outcomes and future research directions.