Enterprise AI Analysis
Analysing the role of LLMs in cybersecurity incident management
Gavin Jones, et al. – International Journal of Information Security
Published: October 30, 2025
Executive Impact & Key Findings
This study evaluates the effectiveness of Large Language Models (LLMs) in cybersecurity incident management. It finds that specific LLMs, like GPT-40 and GPT-3.5, excel in real-time tasks due to high clarity and consistency, while others such as GPT-01 and GPT-4 are superior for analytical tasks like preparation and post-incident analysis, offering stronger reasoning. The research highlights both opportunities for AI integration in cybersecurity and critical limitations, including token context constraints and ethical concerns about workforce skills and security posture. It emphasizes the need for a human-in-the-loop approach for high-impact actions.
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Incident Response Efficiency Gain
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Threat Detection Accuracy
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Reduced Mitigation Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
| LLM Model | Real-time Tasks (Containment, Recovery) | Analytical Tasks (Preparation, Post-Incident) |
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| GPT-40 & GPT-3.5 |
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| GPT-01 & GPT-4 |
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Human-in-the-Loop AI in Cybersecurity
The integration of LLMs in cybersecurity incident management introduces both significant opportunities and ethical challenges. Over-reliance on AI responses without proper human oversight can lead to a devaluation of human expertise and critical thinking skills within security teams. The study recommends a 'human-in-the-loop' model, where high-impact actions require analyst approval and irreversible steps have dual control. This approach maintains accountability, promotes continuous human learning, and mitigates risks associated with AI-driven errors or biases.
Enhanced Incident Response Process with LLMs
Preparation (LLM: Vulnerability Assessment, Training)
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Detection & Analysis (LLM: Data Correlation, Anomaly ID)
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Containment (LLM: Action Prioritization, Isolation Steps)
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Eradication (LLM: Root Cause Analysis, Threat Removal)
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Recovery (LLM: System Restoration, Validation)
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Post-Incident Activity (LLM: Lessons Learned, Policy Update)
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GPT-4's context window compared to GPT-3.5, highlighting a key limitation for complex incident data.
Advanced ROI Calculator
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A strategic path to integrating LLMs into your security operations, ensuring long-term success and resilience.
Phase 1: Discovery & Strategy
Assess current incident response capabilities, identify key areas for LLM integration, and define measurable objectives aligned with business goals. Establish a governance framework and ethical guidelines.
Phase 2: Pilot & Integration
Implement LLMs for specific, low-risk tasks such as initial threat triage or automated report generation. Integrate with existing security tools and train security teams on new workflows and AI interaction protocols.
Phase 3: Scaling & Optimization
Expand LLM capabilities to broader incident management phases, including advanced analysis and containment. Continuously monitor performance, gather feedback, and fine-tune models for accuracy and efficiency.
Phase 4: Monitoring & Evolution
Establish ongoing evaluation processes for AI effectiveness and ethical compliance. Adapt LLM strategies to new threat landscapes and technological advancements, fostering a culture of continuous learning and improvement.
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