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This business use case showcases how B-yond deployed their innovative solution, "Continuous Assurance" (CA), to transform the Security Operations Center (SOC) operations of Ooredoo Qatar. CA utilizes advanced analytics, artificial intelligence (AI) and machine learning (ML) to monitor Ooredoo's network health and customer experience in real-time. The solution's unique capabilities include early anomaly detection, root cause analysis and identification of impacted customers, resulting in quicker incident response times and improved troubleshooting.


Ooredoo Qatar's network was rapidly expanding, particularly with the deployment of 5G, to ensure a seamless customer experience during popular sports events. The growing network presented several challenges for the SOC team, including:

  • Managing the vast amount of data generated by the SOC, such as network logs, alerts and performance metrics, leads to delays in incident detection and response.
  • Limited visibility into overall network performance, with SOC operators dealing with specific network element domains instead of having a comprehensive view.
  • Inability to prioritize highly impactful incidents due to a lack of visibility into the service and customer impact of incidents.

These challenges necessitated the adoption of AI/ML-based solutions to effectively monitor and manage the complexity and scale of modern networks and deliver a high-quality customer experience.

Solution and Innovation:

B-yond's Continuous Assurance solution was deployed to provide real-time monitoring and analysis of Ooredoo Qatar's network performance, configuration management, faults and transactional data. The solution utilized multiple machine-learning algorithms, including supervised and unsupervised approaches, for early anomaly detection and differentiation between data and network anomalies across various network domains.

The anomaly classification engine was employed to identify each anomaly's impact on the service and customer, even identifying impacted VIPs and customers. Continuous Assurance also performed cross-domain correlation to identify root causes of anomalies causing other issues. This early detection and root cause analysis helped improve network performance and customer experience significantly.

Measurable Impact:

The implementation of Continuous Assurance resulted in measurable improvements for Ooredoo Qatar:

  • Enhanced Early Detection: The SOC team improved Mean Time to Detect (MTTD) by up to 45 minutes, allowing faster detection and response to network issues, reducing the impact on customers.
  • Increased Hidden Issue Detection: Hidden issues, such as sleeping cells and availability problems, were detected up to 40% more effectively, minimizing the escalation of network issues.
  • Reduced Troubleshooting Time: The solution reduced Mean Time to Resolve (MTTR) incidents by up to 70%, enabling quick identification of root causes and prioritization of incident resolution.

These improvements led to a 30% increase in both MTTD and MTTR metrics, ultimately resulting in higher customer satisfaction scores, reduced manual processes and decreased network downtime costs.

TM Forum Standards and Frameworks:

B-yond and Ooredoo adhered to the TM Forum's Framework, including the Information Framework (SID), Business Process Framework (eTOM) and Application Framework (TAM). These standards ensured the alignment of Continuous Assurance with industry best practices.

Moreover, TM Forum's Open APIs were used to integrate the solution seamlessly with Ooredoo Qatar's existing systems and applications, facilitating data flow and analysis.


By implementing B-yond's Continuous Assurance solution, Ooredoo Qatar successfully transformed its SOC operations, leveraging AI/ML technologies to enhance early detection, improve troubleshooting efficiency and deliver a seamless customer experience during high-traffic events. The adoption of TM Forum Standards further ensured the solution's alignment with industry best practices, making it a unique and effective approach to SOC operations optimization.

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