Large Language Models for Cybersecurity Policy Compliance and Risk Mitigation
Bibliographic record
Abstract
The rapid digitization of critical business processes has heightened the importance of effective cybersecurity policy compliance and proactive risk mitigation. Large Language Models (LLMs), with their advanced natural language processing and reasoning capabilities, present a transformative opportunity to enhance compliance management, regulatory interpretation, and security decision-making. This study explores the application of LLMs in automating policy analysis, monitoring adherence to industry-specific standards, and facilitating real-time risk assessment. Leveraging extensive training on diverse text corpora, LLMs can interpret complex regulatory frameworks such as GDPR, HIPAA, NIST, and ISO 27001, translating them into actionable technical controls. By integrating with security information and event management (SIEM) systems, LLMs can contextualize alerts, identify potential policy violations, and recommend remediation steps aligned with organizational governance requirements. The research highlights key capabilities, including automated compliance audits, intelligent mapping of policies to operational procedures, and continuous control monitoring across heterogeneous IT and operational technology environments. Case studies illustrate how LLM-powered systems have improved response efficiency in identifying misconfigurations, insider threats, and third-party compliance risks, thereby reducing mean time to detect (MTTD) and mean time to respond (MTTR). The study also addresses challenges, including ensuring model interpretability, managing domain-specific fine-tuning, mitigating hallucinations, and securing sensitive data during inference. Proposed solutions include prompt engineering best practices, integration of explainable AI (XAI) techniques, reinforcement learning from human feedback (RLHF), and the application of privacy-preserving methods such as federated learning. Performance evaluation in simulated enterprise scenarios demonstrates that LLM-enabled compliance tools achieve higher accuracy in regulatory mapping and lower rates of false positives compared to traditional rule-based systems. The findings underscore the potential of LLMs to serve as dynamic compliance advisors, enabling organizations to proactively adapt to evolving cybersecurity regulations while minimizing operational and reputational risks. Future research will explore multimodal LLMs for integrating text, code, and network telemetry, as well as collaborative AI-human governance models to balance automation with oversight. This study positions LLMs as a pivotal technology in advancing cybersecurity policy compliance and risk mitigation in complex, regulated environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".