Strategy and Solutions

Zymr implemented an AI-native detection and response framework to automate alert triage and accelerate remediation workflows. Using transformer-based NLP models, we classified and prioritized alerts based on MITRE ATT&CK mappings, increasing signal-to-noise ratios. For automated response, we orchestrated playbooks using Cortex XSOAR and Python scripts to handle repetitive L1 and L2 activities such as log correlation, endpoint isolation, and ticket closure. A unified MongoDB-based threat intelligence hub aggregated data from MISP, VirusTotal, and internal honeypots, while ML clustering techniques were used to correlate related threats. For visibility, we developed Kibana-based SOC dashboards with drill-down capabilities for cross-functional teams.

About the Client

A California-based cybersecurity SaaS company providing threat intelligence and Security Operations Center (SOC) automation tools to enterprise clients worldwide. The client sought to embed AI into its platform to accelerate incident response and reduce operational overhead in managing L1 and L2 security events.

Key Outcomes

Achieved a 6x improvement in incident response speed.

Analyst productivity and retention improved through automation of repetitive investigations.

Business Challenges

The client’s SOC platform faced issues with high alert fatigue due to static rule-based systems and overwhelming alert volumes. Security analysts were burdened with manual triage and investigation workflows, causing delays in Mean Time to Response (MTTR). The lack of a unified threat intelligence layer limited the contextualization of threats across feeds, while existing incident response playbooks were difficult to scale across diverse customer environments.

Business Impacts / Key Results Achieved

The solution reduced average MTTR from 10 hours to under 1 hour and enabled always-on monitoring with auto-remediation flows. Alert fatigue was reduced by 65%, allowing analysts to focus on complex threats and strategic defence planning.