Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0456
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T11:02:35.453498Z
status: published
lens: g_space
sector: crime
headline: LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions
dek: 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…
gain_title: LLMs coupled with scalable Big Data pipelines improved detection accuracy and reduced response latency for threat detection and incident response compared with traditional approaches.
problem_title: (none)
trace_subject: (none)
gain_reading: LLMs coupled with scalable Big Data pipelines improved detection accuracy and reduced response latency for threat detection and incident response compared with traditional approaches.
gain_evidence: improve detection accuracy and reduce response latency compared with traditional approaches | support threat detection, incident response, vulnerability assessment, and cyber threat intelligence
problem_reading: (none)
problem_evidence: (none)
quick_read: As of 4 November 2025, this systematic review synthesized 235 peer-reviewed studies from 2020-2025 on LLMs such as GPT-4 and BERT applied to cybersecurity within Big Data infrastructures. It reported that coupling LLMs with scalable pipelines improved detection accuracy and reduced response latency for tasks including threat detection, incident response, and vulnerability assessment.

The findings matter because faster, more accurate detection directly affects prevention and mitigation of cybercrime, but the review also documents unresolved risks that could undermine deployment. Questions remain about how to address adversarial susceptibility, data leakage, computational costs, and limited transparency while advancing domain-specific models and explainability.
limitation: Challenges persist for LLM-enabled cyberdefense including adversarial susceptibility, data leakage risks, computational overhead, and limited transparency.
tag: Evidence-backed gain
key_points: Systematic review of 235 peer-reviewed studies from 2020-2025 following PRISMA-2020 with risk of bias assessment and random-effects syntheses. | Evaluated models including GPT-4, BERT, and domain-specific variants integrated into Big Data infrastructures. | Proposed unified taxonomy and identified future priorities: robustness, bias mitigation, explainability, domain-specific models, and distributed integration.
rundown: The review curated 235 peer-reviewed studies from 2020 to 2025, last searched 30 April 2025, using PRISMA-2020 methods with risk of bias assessment and random-effects syntheses.

It examined integration of general and domain-specific LLMs into scalable Big Data pipelines for cyber threat intelligence and vulnerability assessment, consolidating fragmented research into a taxonomy and outlining priorities around robustness, bias, explainability, and distributed integration.
sources:
- peer_reviewed | Information | https://doi.org/10.3390/info16110957 | 2025-11-04
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

89fc5548dbf4fadd981556b90b96f6e73852c9147ed060ba52e2752088fc8d7e

previous

0000000000000000000000000000000000000000000000000000000000000000