Case Study – Agentic AI Investigation
Improving SOC investigation consistency and reducing MTTR by 60%+ with agentic AI
DOWNLOAD THE PDFAbout the client
The client is a U.S.-based IT services organization delivering technology and managed services to enterprise customers. As part of its security operations strategy, the company relies on CyberProof to provide Managed Extended Detection and Response (MXDR) services.
CyberProof operates the client’s security operations center (SOC), monitoring alerts and investigating potential security incidents across the client’s environment. The SOC handles a high volume of alerts and cases on a daily basis, requiring efficient investigation workflows and consistent escalation processes.
The client’s challenge
The client’s SOC environment generated more than 2,000 alerts each month. This volume of alerts was taking its toll. While the team was experienced, manual workflows created inefficiencies and inconsistencies that impacted overall performance as well as the ability to investigate effectively.
Key challenges included:
- High alert volumes: Continuous influx of alerts requiring rapid triage and investigation
- Slow response times: Manual data collection and analysis increased Mean Time to Respond (MTTR)
- Inconsistent escalations: Variability in detail and recommendations across analysts
The client needed to reduce investigation time while ensuring consistent, standardized escalation outputs.
Benefits
- Implementation completed in less than one week: Fast implementation of an agentic investigation capability within the SOC workflow.
- Significantly faster response times: Average mean time to respond (MTTR) was reduced by approximately 62.5% from 16 minutes to 6 minutes.
- 100% escalation consistency: Investigations and escalation reports now follow a consistent structure and level of detail across cases.
- Improved explainability and transparency: Investigation steps and conclusions are clearly documented, allowing analysts to review and validate the agent’s reasoning.
- High investigation accuracy: Over a 90-day period, the deployment recorded zero hallucinations and no false negatives, with QA reviews confirming accuracy.
Our solution
CyberProof implemented an agentic AI-driven investigation capability within the SOC workflow to automate and standardize investigations.
The AI agent initiates investigations as alerts are generated, executing predefined steps, collecting relevant data, and producing structured findings before cases reach analysts. Analysts then review the results and determine appropriate actions, ensuring faster and more informed decision-making.
A human-in-the-loop model ensures that all escalations are validated by an analyst before being communicated, maintaining full oversight and accountability.
The solution was deployed in less than one week with no disruption to operations. Existing QA processes were maintained, including periodic case reviews and the ability to reopen cases if new evidence emerges, ensuring ongoing accuracy and reliability.
Results
The implementation of agentic investigation delivered a significant improvement in SOC performance, reducing MTTR by approximately 62.5%, from 16 minutes to 6 minutes. It eliminated variability in escalation outputs, achieving full consistency and explainability in how incidents are documented and communicated. Over a 90-day period, the solution demonstrated high reliability, with zero hallucinations and no false negatives recorded, as confirmed through ongoing quality assurance reviews.
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