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Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

Perform systematic alert triage in Elastic Security SIEM to rapidly classify, prioritize, and investigate security alerts for SOC operations.

Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

Performs proactive threat hunting in Elastic Security SIEM using KQL/EQL queries, detection rules, and Timeline investigation to identify threats that evade automated detection. Use when SOC teams need to hunt for specific ATT&CK techniques, investigate anomalous behaviors, or validate detection coverage gaps using Elasticsearch and Kibana Security.

Performs tabletop exercises for SOC teams simulating security incidents through discussion-based scenarios to test incident response procedures, communication workflows, and decision-making under pressure without impacting production systems. Use when organizations need to validate IR playbooks, train analysts, or meet compliance requirements for incident response testing.

Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

Perform systematic SIEM false positive reduction through rule tuning, threshold adjustment, correlation refinement, and threat intelligence enrichment to combat alert fatigue.

Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

Performs User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.

Build a structured SOC escalation matrix defining severity tiers, response SLAs, escalation paths, and notification procedures for security incidents.