Data Flow & Processing

Data Ingestion Pipeline

The platform ingests security data from multiple sources through a robust collection system:

1. Data Collection

• Real-time Logs: Security events and access logs from various providers
• Batch Data: Historical security data and periodic reports
• API Feeds: Third-party threat intelligence and security feeds
• Custom Integrations: Organization-specific security tools and systems

2. Data Normalization

All incoming data is normalized to a standard format:

• Schema Validation: Ensures data quality and consistency
• Field Mapping: Converts provider-specific fields to standard format
• Data Enrichment: Adds metadata and context information
• Timestamp Standardization: Normalizes time formats across sources

Processing Workflow

Data Processing Steps

1. Input Validation

   • Schema compliance checking
   • Data quality assessment
   • Duplicate detection

2. Normalization

   • Field standardization
   • Unit conversion
   • Format unification

3. Enrichment

   • Geographic data addition
   • Threat intelligence correlation
   • Historical context

4. Analysis

   • Pattern recognition
   • Anomaly detection
   • Threat correlation

Scoring Algorithm

The platform uses a multi-factor scoring system:

Score Components

• Threat Level: Severity of detected threats
• Confidence: Reliability of threat detection
• Context: Environmental factors and history
• Impact: Potential business impact

Scoring Formula

Final Score = (Threat Level × Confidence × Impact) + Context Bonus