Secure NLP Lifecycle Management Framework
Research Paper#NLP Security and Compliance🔬 Research|Analyzed: Jan 3, 2026 20:14•
Published: Dec 26, 2025 15:28
•1 min read
•ArXivAnalysis
This paper addresses a critical need for secure and compliant NLP systems, especially in sensitive domains. It provides a practical framework (SC-NLP-LMF) that integrates existing best practices and aligns with relevant standards and regulations. The healthcare case study demonstrates the framework's practical application and value.
Key Takeaways
- •Proposes a comprehensive framework (SC-NLP-LMF) for managing the lifecycle of NLP models securely and compliantly.
- •The framework aligns with leading AI governance standards and regulations (NIST, ISO, EU AI Act, MITRE ATLAS).
- •Includes practical methods for bias detection, privacy protection, secure deployment, explainability, and model decommissioning.
- •Demonstrates the framework's application with a healthcare case study, highlighting its ability to detect and address emerging terminology drift.
Reference / Citation
View Original"The 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."