Operationalizing Data Subject Rights in the AI Era: An SRE-Inspired, Socio-Technical Framework for Ethical Reliability

Data subject rights (access, rectification, erasure) are central to modern privacy law, but in AI-intensive enterprises they are often fulfilled via workflows that do not reliably cover derived artifacts such as features, embeddings, logs, and model-related outputs. This gap turns formally granted rights into inconsistently delivered outcomes and undermines trust. We propose a socio-technical framework that treats data subject rights request response (DSRR) as a reliability problem analogous to production services. The framework introduces a control plane that maintains canonical request state, scope decisions, and evidence, and a data plane that executes collection, deletion, and correction across heterogeneous data stores and AI pipelines using lineage and policy. We define service-level indicators and objectives for timeliness, coverage, and response accuracy, and show how error budgets and continuous improvement practices from Site Reliability Engineering can be applied to rights operations. A limited simulated evaluation illustrates phased adoption effects on AI-system coverage and timeliness while preserving accuracy. We also discuss AI-specific erasure challenges, including machine unlearning and partial erasure, and outline directions for empirical validation and governance.