From Policy to Practice: A Systematic Literature Review of AI-Driven Knowledge Base Construction for Cybersecurity Compliance

Managing the lifecycle of cybersecurity policies from high-level governance documents to low-level technical implementation remains prone to human error and operational inefficiency. With the rapid evolution of Infrastructure as Code (IaC) and the increasing volume of regulatory standards, there is a critical need for automated methods to bridge the gap between "what is written" (Policy) and "what is executed" (Practice). This systematic literature review examines the state of the art in Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques for automating cybersecurity knowledge management. This study was conducted in accordance with the PRISMA 2020 statement for reporting systematic reviews. We systematically identified and analyzed 55 peer-reviewed studies published between 2020 and 2025, selected from an initial corpus of 230 papers sourced from IEEE, ACM Digital Library, Scopus, ScienceDirect, and Springer Journals. Our analysis reveals a transition from discriminative information extraction to hybrid architectures integrating deep learning-driven extraction with symbolic logic formalisms for verified knowledge graph construction. We categorize existing approaches into four key themes: (1) Automated Policy Extraction, (2) Knowledge Graph Construction, (3) Policy-as-Code Compliance, and (4) Dynamic Threat Translation. The review identifies a significant gap in "neuro-symbolic" verification methods capable of mitigating stochastic failure modes and hallucination risks inherent in probabilistic LLM-driven compliance pipelines, outlining a roadmap for future research in automated security governance. Beyond a descriptive summary, this review provides a critical comparative analysis of extraction accuracy, reasoning scalability, and the risks inherent in Large Language Model (LLM) approaches.