The bank’s legacy SIEM infrastructure generated an extremely high volume of alerts with nearly 90% identified as false positives. Security analysts spent significant time investigating low-priority events, delaying response to genuine threats and increasing operational overhead.

The existing QRadar environment lacked scalability to efficiently process growing event volumes generated across cloud platforms, digital banking systems, APIs, and endpoint environments. Detection logic was heavily rule-based, limiting the ability to identify sophisticated behavioral anomalies and advanced attack patterns.

Incident response processes were also highly manual. Tier-1 analysts relied on repetitive workflows for triage, enrichment, and escalation activities, resulting in slow Mean Time to Detect (MTTD) and inconsistent remediation processes.

Additionally, the bank needed stronger alignment with MITRE ATT&CK frameworks and modern SOC practices to improve threat visibility, detection coverage, and regulatory readiness across global operations.

The organization required a scalable, AI-driven SIEM modernization strategy capable of reducing alert fatigue, improving threat detection accuracy, and automating security operations workflows.