Enterprise-Scale PII De-Identification with Microsoft Presidio Anonymizer: Architecture, Use Cases, and Best Practices
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P120Keywords:
Microsoft Presidio, Presidio Anonymizer, PII, De-Identification, Anonymization, Data Privacy, LLM Safety, GDPR, HIPAAAbstract
Stricter privacy regulations and the rapid adoption of AI and analytics have increased the need for robust, repeatable mechanisms to detect and de-identify personally identifiable information (PII) across heterogeneous data sources. Microsoft Presidio is an open-source framework that provides context-aware PII detection and anonymization for text, images, and other modalities. This paper presents a practical architecture and implementation blueprint for enterprise-scale PII de-identification using the Presidio Anonymizer. We describe patterns for anonymizing production logs and telemetry, constructing privacy-preserving datasets for machine learning and large language models (LLMs), enabling safe data sharing with vendors, supporting non-production environments, meeting regulatory requirements (GDPR, HIPAA, PCI, and others), protecting data sent to LLMs and SaaS tools, and redacting PII in documents and images. For each use case, we outline threat models, design decisions, operator choices, and integration patterns with modern data and AI stacks. We also discuss operational considerations such as performance, extensibility, reversibility, and governance, making this a reusable reference for large organizations and a concrete demonstration of technical leadership in privacy-by-design systems
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