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Version: Next 🚧

AI Assets

Manage AI models, infrastructure, and agents with comprehensive security monitoring, version control, and governance.

AI Asset Categories​

AI Models​

Machine learning models:

  • Classification models
  • Regression models
  • NLP models
  • Computer vision models
  • Generative AI models

AI Infrastructure​

Compute and storage for AI:

  • GPU clusters
  • Training infrastructure
  • Inference endpoints
  • Model storage
  • Feature stores

AI Agents​

Autonomous AI systems:

  • Security agents
  • Automation agents
  • Analysis agents
  • Custom agents

AI Data​

Training and operational data:

  • Training datasets
  • Validation datasets
  • Production data
  • Feature data

AI Dashboard​

Overview​

  • Total AI assets
  • Model count
  • Infrastructure health
  • Agent status

AI Security​

  • Model security status
  • Vulnerability findings
  • Anomaly alerts
  • Compliance status

Managing AI Models​

Registering Models​

  1. Navigate to Asset Management → AI Assets
  2. Click Add AI Model
  3. Enter model details:
    • Name and version
    • Model type
    • Purpose/use case
    • Owner
  4. Configure security settings
  5. Save model

Model Details​

For each model:

  • Basic information
  • Version history
  • Security assessment
  • Performance metrics
  • Access controls
  • Deployment status

Model Versioning​

  • Version tracking
  • Change history
  • Rollback capability
  • A/B testing support

Model Security​

  • Vulnerability scanning
  • Backdoor detection
  • Input validation
  • Output monitoring

AI Infrastructure Management​

Infrastructure Types​

  • Cloud GPU instances
  • On-premise clusters
  • Serverless inference
  • Edge deployment

Infrastructure Monitoring​

  • Resource utilization
  • Performance metrics
  • Cost tracking
  • Availability

Security Monitoring​

  • Access controls
  • Network security
  • Data protection
  • Compliance

AI Agent Management​

Agent Registry​

Track all AI agents:

  • Active agents
  • Agent purposes
  • Performance metrics
  • Security status

Agent Configuration​

For each agent:

  • Behavior settings
  • Access permissions
  • Monitoring rules
  • Alert thresholds

Agent Governance​

  • Approval workflows
  • Change management
  • Audit logging
  • Compliance tracking

AI Security Features​

Model Security Scanning​

  • Malware detection
  • Backdoor scanning
  • Weight analysis
  • Dependency checking

Runtime Protection​

  • Input validation
  • Output monitoring
  • Anomaly detection
  • Attack prevention

Access Control​

  • Model access permissions
  • Data access controls
  • API security
  • Audit logging

Compliance​

  • AI regulations
  • Industry standards
  • Ethical guidelines
  • Documentation

AI Lifecycle Management​

Development Phase​

  • Model development tracking
  • Experiment logging
  • Testing documentation
  • Approval workflows

Deployment Phase​

  • Deployment approvals
  • Environment management
  • Rollout strategies
  • Monitoring setup

Production Phase​

  • Performance monitoring
  • Security monitoring
  • Drift detection
  • Incident response

Retirement Phase​

  • Deprecation process
  • Migration support
  • Archive procedures
  • Documentation

Reporting​

AI Reports​

  • Model inventory
  • Security status
  • Performance reports
  • Compliance reports

Governance Reports​

  • Model usage
  • Access audit
  • Change history
  • Risk assessment

Best Practices​

  1. Register all AI assets - Complete inventory
  2. Version everything - Track all changes
  3. Scan for security - Before deployment
  4. Monitor continuously - Watch for issues
  5. Control access - Least privilege
  6. Document thoroughly - Purpose, data, decisions
  7. Plan for lifecycle - From development to retirement

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