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

Source: arXiv - 2512.22060v1

Overview

The paper presents SC‑NLP‑LMF, a six‑phase lifecycle framework that helps organizations build, run, and retire NLP models while meeting security, privacy, and regulatory demands. By stitching together best‑in‑class standards (NIST AI RMF, ISO/IEC 42001, EU AI Act, MITRE ATLAS) with concrete technical controls, the authors give practitioners a road map for trustworthy NLP in high‑risk sectors such as healthcare and finance.

Key Contributions

• A unified lifecycle model (six phases) that covers everything from data collection to model decommissioning, explicitly targeting security and compliance gaps in existing AI governance work.
• Mapping to major standards (NIST, ISO, EU AI Act, MITRE ATLAS) with concrete checklists, making it easier for compliance teams to audit NLP pipelines.
• Integration of proven technical safeguards: bias detection, differential privacy, federated learning, secure deployment hardening, explainability, and safe retirement procedures.
• Systematic evidence base: PRISMA‑style review of 45 peer‑reviewed papers and regulatory documents to ensure the framework reflects the state‑of‑the‑art.
• Real‑world validation: a healthcare case study that shows how the framework catches terminology drift (e.g., COVID‑related slang) and triggers compliant model updates.

Methodology

1. Literature & Standards Review – The authors performed a PRISMA systematic review, extracting security, privacy, and compliance requirements from 45 sources (academic papers, NIST/ISO guidelines, EU AI Act drafts, MITRE ATLAS).
2. Gap Analysis – They compared existing AI governance models against the extracted requirements, identifying missing controls for NLP‑specific risks (e.g., data leakage through prompts, terminology drift).
3. Framework Synthesis – The six phases (Planning, Data Governance, Model Development, Secure Deployment, Monitoring & Explainability, Decommissioning) were built by aligning the identified controls with the standards’ “core functions” (Identify, Protect, Detect, Respond, Recover).
4. Case Study Implementation – A prototype NLP pipeline for clinical note classification was instrumented with the framework’s controls (differential privacy on training data, federated fine‑tuning, drift detection alerts). The authors measured how quickly the system detected new COVID‑related terms and how the compliance checklist guided the update process.

Results & Findings

• Compliance Coverage: The framework satisfies >90 % of the control items listed across the four major standards, far exceeding the coverage of typical MLOps toolkits.
• Drift Detection Speed: In the healthcare case study, terminology drift was flagged within 48 hours of emergence, enabling a model patch before any regulatory breach could occur.
• Privacy Overhead: Applying differential privacy added <5 % latency to training and reduced utility loss to <2 % on benchmark clinical classification tasks—an acceptable trade‑off for most regulated use‑cases.
• Operational Simplicity: Teams using the checklist reported a 30 % reduction in time spent on compliance documentation during model release cycles.

Practical Implications

• For DevOps / MLOps Teams: SC‑NLP‑LMF can be encoded into CI/CD pipelines (e.g., automated privacy‑budget checks, drift‑monitoring alerts) to make compliance a built‑in step rather than an after‑the‑fact audit.
• For Security Engineers: The framework supplies concrete hardening actions (model encryption at rest, secure inference APIs, threat‑modeling templates) that align with existing security tooling.
• For Product Managers in Regulated Industries: The lifecycle view clarifies when and how to involve legal/compliance stakeholders, reducing the risk of costly post‑deployment remediation.
• For Vendors: Cloud AI platforms can differentiate themselves by offering “SC‑NLP‑LMF‑ready” services—pre‑configured privacy‑preserving training, drift‑aware monitoring dashboards, and automated decommissioning scripts.

Limitations & Future Work

• Domain Specificity: The case study focuses on healthcare; additional pilots in finance, legal, and government are needed to validate cross‑domain applicability.
• Tooling Gaps: While the framework lists required controls, many organizations still lack mature, open‑source tools for automated compliance reporting and secure model retirement.
• Dynamic Regulation: The EU AI Act and other emerging laws are still evolving; the framework will need periodic updates to stay aligned with new legal interpretations.
• Scalability of Privacy Techniques: Differential privacy and federated learning can become computationally expensive at very large model scales; future research should explore more efficient privacy‑preserving algorithms.

Bottom line: SC‑NLP‑LMF offers a pragmatic, standards‑aligned blueprint for building NLP systems that are not only performant but also secure and compliant—an increasingly non‑negotiable requirement for any AI product that touches sensitive data.

Authors

• Sunil Arora
• John Hastings

Paper Information

• arXiv ID: 2512.22060v1
• Categories: cs.CR, cs.CL, cs.CY
• Published: December 26, 2025
• PDF: Download PDF