Alert Queue Triage

Security Operations Centers (SOC) face crippling alert fatigue from high-volume SIEM and EDR exports. This skill processes raw alert data to deduplicate noise, cluster related signals into incidents, and prioritize the queue based on risk and kill-chain progression.

What it does

• Alert clustering groups related alerts by entity and time window to collapse alert storms into single incidents.
• Risk-based prioritization ranks threats by combining native severity with asset criticality and entity risk scores.
• Noise reduction identifies high-volume, low-fidelity rules and generates specific tuning or suppression suggestions.
• Attack chain detection maps alerts to MITRE ATT&CK tactics to highlight multi-stage lateral movement or persistence.
• Standardized triaging provides analysts with a consistent checklist and disposition codes for structured investigation.

How it works

1. Data ingestion reads alerts from CSV or JSON files, mapping common fields like timestamps, rules, hosts, and IPs.
2. Context enrichment optionally ingests asset lists to elevate alerts affecting mission-critical infrastructure.
3. Analysis execution runs the triage script to cluster events and calculate priority scores.
4. Reporting generates a Markdown queue or a tuning report identifying noisy detection logic.

Frameworks & tools

The skill uses a Python-based analysis engine designed to interface with SIEM, EDR, XDR, and IDS exports. It supports standard security data formats and aligns with the MITRE ATT&CK framework.

Why this beats prompting it yourself

Generic LLM prompts often fail to maintain the state of thousands of alerts or miss the temporal relationship between host-level events. This skill uses structured scripts to ensure every alert is accounted for while applying consistent cybersecurity logic to the entire dataset.

Use cases

• Triage a weekend backlog of EDR alerts to find the most critical incidents.
• Generate a weekly SOC tuning report to identify and suppress noisy false positives.
• Standardize the investigation workflow for a distributed team of analysts.
• Correlate disparate IDS and cloud security logs into a unified timeline.

Known limitations

Requires specific column headers or aliases in input files to map data correctly. Performance depends on the volume of the export and the defined time window for clustering.