面向安全与合规的人工智能：NLP模型生命周期管理的组织标准与协议
Toward Secure and Compliant AI: Organizational Standards and Protocols for NLP Model Lifecycle Management
AI总结：
提出SC-NLP-LMF六阶段框架，通过系统综述整合NIST AI RMF等标准，结合偏差检测、差分隐私等方法，保障NLP系统从开发到退役的安全与合规，并以医疗案例验证其有效性。
摘要：
自然语言处理系统越来越多地用于医疗、金融和政府等敏感领域，处理大量个人和受监管数据。然而，这些系统引入了与安全、隐私和法规遵从相关的独特风险，现有的人工智能治理框架未能完全解决这些问题。本文介绍了安全合规的NLP生命周期管理框架（SC-NLP-LMF），这是一个全面的六阶段模型，旨在确保NLP系统从开发到退役的安全运行。该框架通过对45篇同行评审和监管来源进行基于PRISMA的系统综述而开发，与领先标准（包括NIST AI RMF、ISO/IEC 42001:2023、欧盟AI法案和MITRE ATLAS）保持一致。它集成了偏差检测、隐私保护（差分隐私、联邦学习）、安全部署、可解释性和安全模型退役等成熟方法。一个医疗案例研究展示了SC-NLP-LMF如何检测新兴术语漂移（例如，与COVID相关的语言）并指导合规的模型更新。该框架为组织提供了一个实用的、覆盖全生命周期的结构，用于在高风险环境中开发、部署和维护安全且负责任的NLP系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
