Natural Language Processing for Cybersecurity: Automating Threat Report Analysis

The rapid growth of cyber threats has led to an exponential increase in threat intelligence reports, incident logs, and security advisories, creating significant challenges for timely and effective analysis. Manual examination of these unstructured text sources is labor-intensive, error-prone, and often unable to keep pace with the speed of emerging threats. Natural Language Processing (NLP) offers a transformative approach to automating threat report analysis by leveraging advanced computational linguistics and machine learning techniques to extract, classify, and contextualize critical security information. This paper presents a comprehensive study of NLP-based methods for cybersecurity threat report analysis, emphasizing their capacity to enhance situational awareness, accelerate incident response, and support proactive defense strategies. We examine key NLP tasks applicable to cybersecurity, including named entity recognition for extracting indicators of compromise (IOCs), topic modeling for identifying threat themes, sentiment analysis for assessing attacker intent, and relation extraction for mapping threat actor behaviors. State-of-the-art models such as transformer-based architectures (e.g., BERT, RoBERTa, and domain-specific adaptations like CyberBERT) are evaluated for their performance in parsing and understanding complex, jargon-rich security texts. Empirical experiments on benchmark datasetsincluding threat intelligence feeds, MITRE ATT&CK descriptions, and open-source cyber incident reportsdemonstrate that NLP-driven pipelines outperform traditional keyword-matching systems in accuracy, scalability, and adaptability to novel threats. We further discuss the integration of NLP systems with Security Information and Event Management (SIEM) platforms, enabling automated alert generation, correlation of threat indicators, and prioritization of remediation efforts. Despite these advantages, challenges remain in handling data heterogeneity, preserving contextual accuracy, and mitigating model biases. We explore emerging research directions, including low-resource domain adaptation, explainable NLP for transparent decision-making, and multilingual processing to expand threat coverage across diverse linguistic sources. The findings underscore the strategic importance of NLP in modern cybersecurity operations, highlighting its role in transforming unstructured threat intelligence into actionable, real-time security insights that strengthen defensive postures against evolving cyber adversaries.