Zero Trust, Full Intelligence: PI/SPI/PHI/NPI/PCI Redaction Strategies for Agentic and Next-Gen AI Ecosystems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805017Keywords:
Zero Trust, Data Redaction, Artificial Intelligence, Generative AI, Agentic AI, Data Privacy, Data Security, PI, SPI, PHI, NPI, PCI, Privacy-Preserving AI, GDPR, HIPAA, PCI DSS, Data Protection, IEEE StandardsAbstract
The proliferation of Artificial Intelligence (AI), Generative AI (GenAI), and autonomous Agentic AI systems promises unprecedented analytical capabilities but simultaneously exacerbates data privacy and security risks. These systems often require access to vast datasets containing highly sensitive information, including Personal Information (PI), Sensitive Personal Information (SPI), Protected Health Information (PHI), Nonpublic Personal Information (NPI), and Payment Card Information (PCI). Traditional perimeter-based security models are inadequate for the dynamic, distributed nature of modern AI ecosystems. This paper proposes integrating Zero Trust principles with advanced data redaction strategies to create a robust framework for safeguarding sensitive data while maximizing its utility for AI. We analyze technical approaches for identifying and redacting PI, SPI, PHI, NPI, and PCI across diverse industry contexts (healthcare, finance, telecommunications). We explore the application of techniques ranging from rule-based pattern matching and Named Entity Recognition (NER) to context-aware language models and tokenization. The paper presents a conceptual Zero Trust Data Redaction Lifecycle and discusses its implementation within complex AI pipelines, including challenges specific to GenAI and Agentic AI, such as prompt injection risks and ensuring agent privacy compliance. We provide a comparative analysis of redaction methods and highlight opportunities for developing privacy-aware AI systems. The proposed framework offers a pathway to achieving "Full Intelligence" from data assets without compromising the imperative of Zero Trust data protection
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