AI-Powered Security Operations
Revolutionizing Alert Investigation with Agentic LLMs
Our analysis demonstrates a structured LLM-assisted workflow that significantly enhances the accuracy and efficiency of security alert investigations, reducing manual workload and improving incident response capabilities.
Executive Impact
Key Metrics from Our Agentic Workflow
The proposed agentic workflow significantly outperforms baseline methods, delivering high accuracy in identifying true positives and optimizing resource allocation.
0%
Average True Positive Accuracy
0%
Max False Negative Rate
0%
GPT-5-mini Malicious Accuracy
0%
Reduction in Manual Triage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Flow
Performance Insights
Future Directions
The Agentic Investigation Loop
Our agentic workflow mimics a security analyst's iterative process, leveraging coordinated LLMs to gather, synthesize, and decide on alerts. This modular design allows for flexible integration of additional components and data sources.
Comparative Performance
The agentic approach consistently outperforms the baseline, especially in identifying malicious activity. Models like GPT-5-mini achieved 100% accuracy on malicious alerts, demonstrating the workflow's robustness.
Expanding Capabilities
Future work will focus on incorporating more diverse real-world security data, extensive LLM tuning, dynamic thresholds, specialized agents for specific domains, and robust adversarial protection to further enhance the system.
Enterprise Process Flow: Alert Investigation Workflow
Overview Query + Alert
→
Agentic Security Investigation Loop
→
High Confidence Verdict
→
Document Findings
93%
Average Accuracy in Identifying Malicious Behavior (RQ2)
Our workflow demonstrates high accuracy in detecting true positives, significantly improving upon baseline models. This reliability minimizes false negatives, a critical factor for enterprise security.
| Feature | Baseline Approach | Agentic Workflow |
|---|---|---|
| Data Context | Limited (Overview query + Alert text) | Rich (Overview, Query results, Summaries) |
| Malicious Accuracy | 0% (Incorrectly classified as benign) | Up to 100% (GPT-5-mini) |
| False Positive Handling | Often empty/nuanced analysis lacking | Prioritizes uncertainty over incorrect benign verdicts |
| Investigation Depth | Direct verdict, no iterative analysis | Iterative querying and synthesis (up to 2 loops) |
| Analyst Workload | Minimal reduction | Significant reduction in early-stage triage |
Calculate Your Potential AI Savings
Estimate the operational efficiencies and cost reductions your organization could achieve by automating security alert investigations with Agentic AI.
Your Roadmap to Agentic AI Security
A phased approach to integrate intelligent automation into your security operations, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy
Assess current SOC workflows, identify key automation opportunities, and define tailored agentic AI strategies. This includes data source integration planning and initial prompt engineering.
Phase 2: Pilot & Integration
Deploy a pilot agentic workflow with selected LLMs and log sources. Validate performance, gather feedback, and begin seamless integration into existing SIEM/XDR platforms.
Phase 3: Expansion & Optimization
Scale the agentic solution across more alert types and data sources. Implement continuous learning loops, advanced prompt tuning, and refine agent behaviors for peak efficiency and accuracy.
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