Toward Secure and Compliant AI: Organizational Standards and Protocols for NLP Model Lifecycle Management

2025-12-26

AI Summary

High-risk NLP systems—particularly in healthcare, finance, and public administration—face critical challenges stemming from inadequate security, privacy, and regulatory compliance governance. To address this gap, we propose SC-NLP-LMF, the first framework integrating NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, the EU AI Act, and MITRE ATLAS to establish a six-stage, end-to-end lifecycle governance paradigm spanning development, deployment, monitoring, adaptation, decommissioning, and retirement. Our approach innovatively unifies terminology drift detection, compliance-driven dynamic model updating, and secure model retirement, augmented by differential privacy, federated learning, eXplainable AI (XAI), and bias auditing. Evaluated on a COVID-19 clinical NLP use case, SC-NLP-LMF successfully identified linguistic drift and enabled compliant iterative model refinement. The framework delivers actionable, cross-stage governance protocols that support accountable operations for high-risk NLP systems—bridging a pivotal gap at the intersection of RegTech and AI governance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Natural Language Processing (NLP) systems are increasingly used in sensitive domains such as healthcare, finance, and government, where they handle large volumes of personal and regulated data. However, these systems introduce distinct risks related to security, privacy, and regulatory compliance that are not fully addressed by existing AI governance frameworks. This paper introduces the Secure and Compliant NLP Lifecycle Management Framework (SC-NLP-LMF), a comprehensive six-phase model designed to ensure the secure operation of NLP systems from development to retirement. The framework, developed through a systematic PRISMA-based review of 45 peer-reviewed and regulatory sources, aligns with leading standards, including NIST AI RMF, ISO/IEC 42001:2023, the EU AI Act, and MITRE ATLAS. It integrates established methods for bias detection, privacy protection (differential privacy, federated learning), secure deployment, explainability, and secure model decommissioning. A healthcare case study illustrates how SC-NLP-LMF detects emerging terminology drift (e.g., COVID-related language) and guides compliant model updates. The framework offers organizations a practical, lifecycle-wide structure for developing, deploying, and maintaining secure and accountable NLP systems in high-risk environments.

Problem

• Addresses security and compliance risks in NLP systems
• Introduces a lifecycle framework for secure NLP management
• Provides methods for bias detection and privacy protection

Innovation

• A six-phase lifecycle framework for NLP systems
• Integrates bias detection, privacy methods, and secure deployment
• Aligns with NIST, ISO, EU AI Act, and MITRE standards