Security and Compliance
CarbonHelix needed to deliver highly customized managed security services across diverse customer environments while improving threat-detection speed and accuracy beyond what human analysts and manual workflows could handle alone.
Reported impact
CarbonHelix improved detection and response speed, expanded the range of customer environments it can secure, and gave analysts better context so they can deliver highly customized services with greater accuracy.
CarbonHelix uses IBM QRadar Suite as the observability and analytics backbone for its 24x7 managed security operations center. QRadar gives the company full visibility across heterogeneous customer environments, including on-premises, air-gapped, virtual, AWS, Azure, IBM AS/400, IBM AIX and mainframe systems, and supports integration with homegrown and third-party applications that many SIEM tools struggle to ingest. On top of that visibility, CarbonHelix applies QRadar's machine-learning-based user behavior analytics and network threat analytics to detect anomalous activity at a scale human analysts cannot match. It then feeds UBA signals into QRadar SOAR so risky user behavior can trigger faster automated response actions. The company is also expanding its stack with IBM Guardium and third-party AI-driven endpoint detection and response, making QRadar both the correlation layer for today's SOC workflows and the integration hub for new AI-accelerated response capabilities.
Logs, flow data, user activity and other telemetry from customer environments are ingested into QRadar Suite, machine-learning analytics identify anomalies and risky behavior, and validated findings are passed into QRadar SOAR and adjacent EDR tooling to speed response.
"The machine learning is able to analyze activity patterns on a scale that no human analyst is capable of"
"We can deploy QRadar in any form or fashion-on-premises, in air-gapped environments, if we host it, if a customer hosts it, hardware, virtual, AWS, Azure"
"We're using AI to feed our SOC better information, quicker"
"integrating UBA into the IBM QRadar SOAR component"
"there's nothing that we have come across that we have not been able to integrate into QRadar"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
IBM Sample Sep 11, 2025 Lenovo PCCW Solutions (LPS) LPS needed a more unified way to monitor, secure, and scale hybrid environments for enterprise clients because its existing monitoring tools only offered siloed snapshots and could not support proactive incident… 24×7 service-level commitments IBM Instana · IBM LinuxONE · IBM Turbonomic IBM Sample Jul 12, 2024 Sanwa Comtec K.K. Sanwa Comtec needed to add AI-based harassment detection to on-premises chat deployments for customers that cannot send sensitive conversations to external networks, while still achieving enough accuracy to make alerts… > 95％ accuracy of AI-based harassment judgments in closed networks IBM Watson natural language processing · IBM watsonx.ai IBM Sample Jun 7, 2024 boxxe boxxe needed to detect and respond to faster-moving cyber threats across diverse customer environments without overloading its security analysts or relying on fragmented dashboards and manual reporting. 20 hours of manual monthly reporting eliminated IBM Security QRadar SIEM · IBM Security QRadar SOAR 