aceLLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions
This paper presents a systematic review of research (2020–2025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, parti…
LLMs coupled with scalable Big Data pipelines improved detection accuracy and reduced response latency for threat detection and incident response compared with traditional approaches.
Challenges persist for LLM-enabled cyberdefense including adversarial susceptibility, data leakage risks, computational overhead, and limited transparency.
Evidence
- Peer-reviewedInformation2025-11-04
How should this claim be treated?
Truvace Impact Record TRV-2026-0456, v1: “LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions.” Truvace, 2026-07-20. /record/TRV-2026-0456 (accessed at citation time). sha256 89fc5548dbf4fadd…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0456 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.