Modern QRadar Rule Engineering

AI-Powered Anomaly Detection

QRadar’s new Cognitive Threat Analysis engine uses transformer-based models to:

• Detect zero-day credential stuffing attacks (98% accuracy)
• Identify anomalous data exfiltration patterns
• Predict potential MITRE ATT&CK tactic progression

Custom Rule with ML Integration

when EventName MATCHES "Authentication Failure"
AND RiskScore() > 0.85
THEN escalateToThreatHuntingTeam("Suspected Brute Force")

Cloud-Native Deployment

module "qradar_aws" {
source = "ibm/qradar/aws"
version = "2025.3"

vpc_id = "vpc-123456"
threat_intel_feeds = [
"aws:guardduty:findings",
"crowdstrike:falcon:indicators"
]

auto_scaling = {
min = 3
max = 12
metric = "EPS"
}
}

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