Industry: Telecommunications / Digital Infrastructure
Scope: Global security operations across a large, distributed enterprise environment with continuous exposure to evolving cyber threats
Industry: Telecommunications / Digital Infrastructure
Scope: Global security operations across a large, distributed enterprise environment with continuous exposure to evolving cyber threats
A global enterprise operating a large and complex digital infrastructure faced continuous exposure to a rapidly evolving threat landscape. Its Security Operations Center (SOC) was responsible for monitoring, detecting, and responding to threats across a highly distributed environment.
However, the volume and complexity of alerts created significant operational strain. Analysts were required to manually investigate incidents, analyze scripts such as PowerShell, correlate alerts across multiple systems, and validate potential threats; processes that were time-intensive and difficult to scale.
The organization also faced a shortage of highly skilled SOC analysts. Junior analysts required significant time to become effective, while senior analysts were increasingly consumed by repetitive investigative tasks rather than high-value threat analysis.
This resulted in slower response cycles, inconsistent analysis quality, and growing pressure on the SOC to improve efficiency without expanding headcount. The challenge was to enable faster, more accurate threat response while scaling analyst capability across the team.
To address these challenges, Futran designed and implemented an AI-augmented security operations model built on Microsoft Security Copilot, integrated with Microsoft’s security ecosystem.
The solution established a unified security architecture by integrating signals from endpoints, identities, cloud applications, and productivity environments into a centralized SIEM and XDR framework. This enabled real-time aggregation and correlation of threat data across the enterprise.
Microsoft Sentinel was used to centralize logs and alerts, while Defender XDR provided contextual visibility across security layers. Azure Logic Apps enabled automated orchestration of alert handling and response workflows, reducing manual intervention across the incident lifecycle.
On top of this foundation, Microsoft Security Copilot was deployed as an AI layer to augment analyst capabilities. It enabled natural language interaction for threat investigation, including support for generating and refining Kusto Query Language (KQL) queries, allowing analysts to perform advanced threat hunting without deep query expertise.
Security Copilot also introduced automated script analysis capabilities, enabling rapid interpretation of PowerShell and other scripts without requiring manual validation workflows. Analysts could receive contextual summaries, recommended actions, and remediation guidance in real time.
Additionally, incident summaries generated through Security Copilot were integrated into response workflows, allowing teams to quickly understand threats, validate findings, and take action without repeatedly analyzing raw data.
By embedding AI-driven assistance into the SOC workflow, the organization transformed its security operations from a manual, analyst-dependent model into an intelligent, scalable system that improved both speed and consistency of threat response.
Integrated security signals from endpoints, identity systems, cloud platforms, and productivity environments into a centralized SIEM and XDR model for end-to-end threat visibility and correlation
Ingested logs and alerts into Microsoft Sentinel to enable cross-source threat correlation and improve detection accuracy across complex environments
Implemented automated workflows to enrich alerts, trigger responses, and streamline incident handling across the SOC lifecycle
Enabled analysts to interact with security data using natural language, accelerating investigation and reducing dependency on manual analysis
Simplified threat hunting by allowing analysts to generate and refine KQL queries through natural language prompts, reducing reliance on specialized query expertise
Implemented AI-driven analysis of PowerShell and other scripts, eliminating the need for manual validation and significantly reducing investigation time
Enabled automatic generation of incident summaries and remediation guidance, improving decision-making speed and consistency across analysts
Embedded AI capabilities directly into existing SOC workflows, enabling junior analysts to perform advanced tasks and improving overall team efficiency
The AI-augmented SOC transformation improved operational efficiency, reduced manual workload, and enhanced the organization’s ability to respond to threats at scale.
AI-driven analysis of PowerShell and other scripts eliminated manual validation steps and accelerated investigation workflows
Automated correlation, AI-generated summaries, and contextual insights reduced time across the incident lifecycle
Analysts were able to handle higher volumes of alerts and incidents without increasing team size
Natural language query support and guided recommendations reduced the learning curve for advanced threat analysis
AI-assisted analysis enabled junior analysts to perform complex tasks with greater confidence and consistency
Structured summaries and contextual recommendations reduced variability in analysis across the SOC
The organization improved coverage and response capability while maintaining existing team size
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