Security and Compliance
Quantiphi needed to consolidate security data spread across multiple clouds because analysts were stuck triaging alerts in isolated dashboards and dealing with severe alert fatigue instead of proactively addressing threats.
Reported impact
Quantiphi unified dispersed log sources, reduced analyst alert fatigue, accelerated rule and playbook creation, and positioned its security operations for auto-remediation and self-healing workflows.
Quantiphi modernized its security operations on Google Cloud by consolidating multicloud telemetry into a unified Google SecOps operating model. The company uses Google Security Operations SOAR to create a single data model and automate response workflows, while Google Security Operations (SecOps) provides the centralized threat-detection surface that replaces fragmented native logging systems from multiple clouds. Analysts use Gemini in SecOps to generate correlation rules and SOAR playbooks from natural-language prompts instead of writing them manually, and Mandiant Threat Intelligence adds external threat context to the environment. Together, these components reduce engineering time, cut alert fatigue, and let the security team move toward auto-remediation and self-healing security systems. The resulting platform improves data protection and supports the regulatory trust Quantiphi needs for its global client work.
Logs from multiple cloud environments are consolidated into Google SecOps, normalized through the SOAR data model, enriched with Mandiant intelligence, and acted on through Gemini-generated rules and playbooks that automate triage and remediation.
Built SOC correlation rules on the fly with Gemini in SecOps
Combined with Mandiant Threat Intelligence, Google Security Operations (SecOps) enables Quantiphi to pursue instantaneous auto-remediation and self-healing systems using agentic AI.
Quantiphi deployed Google Security Operations SOAR (Security Orchestration, Automation, and Response) to establish a unified data model.
Reduced analyst alert fatigue by automating Google SecOps SOAR
Unified dispersed log sources using Google SecOps
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.
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