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Version: Next 🚧

Model Training Security

Secure your AI model training processes from data poisoning, unauthorized access, and pipeline vulnerabilities.

Training Security Features​

Data Validation​

Ensure training data integrity:

  • Source verification
  • Data quality checks
  • Anomaly detection in datasets
  • Poisoning attack detection
  • Label verification

Pipeline Security​

Protect your training pipelines:

  • Access control enforcement
  • Execution monitoring
  • Code integrity verification
  • Dependency scanning
  • Resource isolation

Model Versioning​

Track and secure model versions:

  • Cryptographic signing
  • Version history
  • Change tracking
  • Rollback capability
  • Audit logging

Access Control​

Manage who can train models:

  • Role-based permissions
  • Training resource quotas
  • Approval workflows
  • Activity logging
  • Segregation of duties

Setting Up Training Security​

Registering Training Pipelines​

  1. Navigate to AI Security → Model Training
  2. Click Add Pipeline
  3. Configure pipeline details:
    • Name and description
    • Pipeline location
    • Data sources
    • Output destinations
  4. Set security policies
  5. Enable monitoring

Configuring Data Sources​

For each data source:

  • Source Type - Database, file, API, etc.
  • Access Method - How data is retrieved
  • Validation Rules - Data quality requirements
  • Security Level - Sensitivity classification
  • Monitoring - Track data access

Security Policies​

Define policies for:

  • Who can initiate training
  • Required approvals
  • Data access restrictions
  • Resource limits
  • Output handling

Monitoring Training Jobs​

Active Jobs Dashboard​

View all running training jobs:

  • Job status and progress
  • Resource consumption
  • Data access patterns
  • Anomaly indicators
  • Estimated completion

Job Security Metrics​

For each training job:

  • Data volume processed
  • Access patterns
  • Resource usage
  • Security events
  • Compliance status

Alerts​

Receive alerts for:

  • Unauthorized access attempts
  • Data anomalies
  • Resource abuse
  • Policy violations
  • Pipeline failures

Data Poisoning Protection​

Prevention Measures​

  • Data source validation
  • Input sanitization
  • Anomaly detection
  • Statistical analysis
  • Label verification

Detection Indicators​

Signs of potential poisoning:

  • Unusual data distributions
  • Label inconsistencies
  • Performance degradation
  • Unexpected model behavior
  • Data quality anomalies

Response Actions​

When poisoning is suspected:

  1. Pause training immediately
  2. Isolate affected data
  3. Investigate source
  4. Clean compromised data
  5. Re-train from clean baseline

Training Audit Log​

All training activities are logged:

  • Job initiation (who, when)
  • Data access events
  • Resource allocation
  • Configuration changes
  • Completion status

Accessing Logs​

  1. Go to Model Training → Audit Log
  2. Filter by date, user, or job
  3. Export logs as needed
  4. Set retention policies

Best Practices​

Before Training​

  • Validate all data sources
  • Verify pipeline integrity
  • Check dependencies for vulnerabilities
  • Ensure proper access controls
  • Document training parameters

During Training​

  • Monitor resource usage
  • Watch for anomalies
  • Check intermediate outputs
  • Maintain audit trails
  • Respond to alerts promptly

After Training​

  • Validate model integrity
  • Sign and version model
  • Document training run
  • Archive training data reference
  • Update model registry

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