Reducing False Positives in Security Alerting by 85%

Overview

Security teams often face alert fatigue - too many alerts, most of which are false positives. At Solairus, the IT security team was drowning in alerts, spending most of their time investigating incidents that turned out to be benign. This led to burnout and, more critically, real threats getting lost in the noise.

The Challenge

• Over 500 security alerts per day, 90%+ were false positives
• Analysts spending 6+ hours daily investigating non-threats
• Alert fatigue leading to missed real security incidents
• No correlation between different security data sources
• High analyst turnover due to repetitive, frustrating work

The Solution

I designed an intelligent alert correlation system that dramatically reduced noise while improving threat detection:

• Built a data correlation engine that cross-referenced alerts with asset inventory, user behavior, and network context
• Implemented rule-based scoring to prioritize alerts based on risk and confidence
• Created automated enrichment that added context to each alert before analyst review
• Developed auto-remediation workflows for common false positive patterns
• Built a feedback loop where analyst decisions improved future scoring

Tech Stack

Key Learnings

• 1Context is everything in security - isolated alerts are nearly useless
• 2Build feedback loops to continuously improve accuracy
• 3Automate the boring stuff so humans can focus on judgment calls
• 4Measure not just quantity but quality of analyst work