SIEM and XDR
Real Use
Anomaly detection (statistical + ML), entity behavior analytics, alert correlation, automated triage summarization, natural-language-to-query translation (NL to KQL, SPL, UDM). Detection authoring assist — where the analyst describes a detection in plain English and the system drafts the rule — is now a standard feature on major platforms.
Hype Watch: SIEM/XDR Edition
Three claims to probe: (1) "AI-powered correlation" often means sequential rule evaluation with ML anomaly scoring as an add-on — ask which correlation steps involve learned models vs. written rules. (2) "Natural language to query" is real but vendor demos use vendor-crafted schemas; always test against your own field names and log sources before assuming parity. (3) "AI-driven alert reduction" — push for reduction methodology: is it suppression, deduplication, or genuine ML triage? Each has a very different false-negative risk profile.
The Evaluation Framework
When evaluating SIEM/XDR AI claims, use this framework: (1) Show me a detection you wrote with AI assist vs. without — what was the time difference? (2) Run me through the last false negative your rules missed — would the ML layer have caught it? (3) What is your labeling process for the ML models, and how often are they retrained? These questions separate platforms with real ML investment from those with ML branding on rule engines.
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