Toward Secure and Compliant AI: Organizational Standards and Protocols for NLP Model Lifecycle Management
Sunil Arora, John Hastings
TL;DR
The paper addresses the lack of NLP-specific lifecycle governance for secure and compliant deployments in high-risk domains. It proposes SC-NLP-LMF, a six-phase framework that integrates Security-by-Design, bias auditing, privacy preservation, continuous monitoring, and lifecycle governance, aligning with standards such as the NIST AI RMF, ISO 42001, EU AI Act, and MITRE ATLAS. Through a PRISMA-based literature review and a healthcare case study, it demonstrates practical applicability, including terminology-drift detection and compliant retraining, deployment, and decommissioning procedures. The significance lies in providing a domain-tailored, actionable blueprint for organizations to operationalize trustworthy NLP at scale in regulated environments, while acknowledging integration and adoption challenges and outlining directions for automation and policy alignment.
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.