Secure and Compliant AI Enables Sensitive Data Handling across 6 Lifecycle Phases

The growing use of Natural Language Processing (NLP) in critical sectors like healthcare, finance, and government presents significant challenges to data security, privacy, and regulatory compliance, areas where current governance frameworks fall short. Sunil Arora and John Hastings, from Dakota State University, along with their colleagues, address this need by introducing a comprehensive framework for managing NLP systems throughout their entire lifecycle. Their research culminates in the Secure and Compliant NLP Lifecycle Management Framework (SC-NLP-LMF), a six-phase model built upon a systematic review of leading standards and regulatory guidance. This framework offers organisations a practical structure for building, deploying, and maintaining accountable NLP systems, and importantly, demonstrates how to proactively address emerging risks such as terminology drift, ultimately fostering trust and responsible innovation in high-risk environments.

This framework proactively manages the risks associated with AI, ensuring security and building trust in systems throughout their entire lifecycle, from design and development to deployment and monitoring. It emphasizes transparency and explainability, utilising techniques to provide insights into model predictions, and integrates established methods for bias detection and privacy protection, such as differential privacy and federated learning., The framework aligns with leading standards, including NIST AI RMF, ISO/IEC 42001:2023, and the EU AI Act, and encourages adherence to established ethical principles. Implementing this framework increases trust and adoption of AI systems, reduces risk and liability, improves model performance and reliability, and enhances accountability and auditability. This work integrates established methods for bias detection, privacy protection, secure deployment, explainability, and secure model decommissioning, drawing upon a systematic review of peer-reviewed sources and leading standards. The framework addresses critical security, privacy, and regulatory compliance risks inherent in NLP systems handling sensitive data., The Secure Model Training phase prioritises fairness and security, requiring models to undergo rigorous bias audits and adversarial robustness testing. Techniques like secret sharing and data masking enhance security, while SHAP and LIME generate detailed explanations of model behaviour.

Compliance checkpoints, aligned with relevant regulations, are embedded throughout the training process. During Deployment Governance, formal documentation precedes model release, and secure deployment practices mitigate risks of misuse. Developed through a systematic review of existing research, industry standards, and regulatory guidelines, the framework addresses critical security, privacy, and compliance risks inherent in deploying NLP technologies, particularly within sensitive sectors like healthcare and finance. The framework details six key phases, from initial data governance to secure model decommissioning, offering practical strategies for integrating ethical oversight and technical rigor into NLP workflows., The team demonstrates the framework’s applicability through case studies, showing its ability to detect evolving language patterns and guide compliant model updates, thereby establishing a robust and actionable governance roadmap for organizations. By moving beyond static documentation, the framework promotes dynamic and continuous governance, ensuring transparency, fairness, and security are maintained throughout the NLP system’s operation. Future research will focus on scaling and automating the framework’s deployment and collaborating with policymakers to establish standardized lifecycle management practices.