{
  "post_launch_monitoring": {
    "version": "1.0",
    "created": "2026-02-05T00:00:00Z",
    "system": "Project Starlight Steganography Detection",
    "checklist_items": [
      {
        "id": "TRAJ_001",
        "category": "Trajectory Verification",
        "title": "Model Performance Trajectory Check",
        "description": "Verify model accuracy trends align with expected performance trajectory",
        "frequency": "Hourly",
        "priority": "HIGH",
        "metrics": {
          "accuracy_threshold": 0.85,
          "false_positive_rate_max": 0.05,
          "detection_confidence_min": 0.80
        },
        "verification_steps": [
          "Compare current detection accuracy against baseline",
          "Check false positive rate trends",
          "Validate confidence score distributions",
          "Verify model drift detection alerts"
        ],
        "escalation": {
          "threshold_breach": "Immediate notification to ML team",
          "consecutive_failures": "Automatic model rollback after 3 failures"
        }
      },
      {
        "id": "TRAJ_002", 
        "category": "Trajectory Verification",
        "title": "Data Ingestion Trajectory Validation",
        "description": "Monitor data pipeline throughput and quality trajectory",
        "frequency": "15 minutes",
        "priority": "HIGH",
        "metrics": {
          "processing_rate_min": 1000,
          "data_quality_score_min": 0.95,
          "queue_depth_max": 500
        },
        "verification_steps": [
          "Check processing rate against expected trajectory",
          "Validate data quality scores",
          "Monitor queue depth trends",
          "Verify data source connections"
        ]
      },
      {
        "id": "SYS_001",
        "category": "System Status Monitoring",
        "title": "Resource Utilization Check",
        "description": "Monitor CPU, memory, and storage utilization",
        "frequency": "5 minutes",
        "priority": "MEDIUM",
        "metrics": {
          "cpu_threshold": 80,
          "memory_threshold": 85,
          "disk_threshold": 90
        },
        "checks": [
          "CPU usage percentage",
          "Memory consumption",
          "Disk space availability", 
          "Network I/O rates"
        ]
      },
      {
        "id": "SYS_002",
        "category": "System Status Monitoring", 
        "title": "Service Health Verification",
        "description": "Check all critical services are operational",
        "frequency": "2 minutes",
        "priority": "HIGH",
        "services": [
          "steganography_detector",
          "model_inference_engine",
          "data_preprocessor",
          "api_gateway",
          "database_connection"
        ],
        "health_checks": [
          "HTTP endpoint responsiveness",
          "Database connection status",
          "Model loading status",
          "Queue processor health"
        ]
      },
      {
        "id": "ABORT_001",
        "category": "Abort Procedures",
        "title": "Critical Failure Abort Protocol",
        "description": "Procedure for immediate system shutdown on critical failures",
        "trigger_conditions": [
          "Detection accuracy below 50%",
          "System resource usage above 95%",
          "Data corruption detected",
          "Security breach identified"
        ],
        "steps": [
          "Immediate traffic redirection to backup system",
          "Graceful shutdown of detection services",
          "Preserve system state and logs",
          "Notify incident response team",
          "Activate disaster recovery protocol"
        ],
        "rollback_procedure": "Switch to last known good model checkpoint"
      },
      {
        "id": "ABORT_002",
        "category": "Abort Procedures",
        "title": "Model Performance Degradation Abort",
        "description": "Abort and rollback when model performance degrades significantly",
        "degradation_threshold": {
          "accuracy_drop": 0.15,
          "fp_rate_increase": 0.10,
          "consecutive_failures": 5
        },
        "actions": [
          "Stop current model serving",
          "Load previous stable model version",
          "Validate rollback model performance",
          "Log degradation incident",
          "Trigger model retraining pipeline"
        ]
      },
      {
        "id": "CONT_001",
        "category": "Contingency Planning",
        "title": "Model Drift Mitigation",
        "description": "Contingency plan for handling model drift scenarios",
        "drift_indicators": [
          "Gradual accuracy decline over 24 hours",
          "Increased prediction variance",
          "Feature distribution shifts",
          "Concept drift alerts"
        ],
        "mitigation_steps": [
          "Enable ensemble model voting",
          "Increase model validation frequency",
          "Trigger automated retraining",
          "Fall back to conservative detection thresholds",
          "Notify ML engineering team"
        ]
      },
      {
        "id": "CONT_002",
        "category": "Contingency Planning",
        "title": "Data Pipeline Failure Recovery",
        "description": "Recovery procedures for data processing failures",
        "failure_scenarios": [
          "Source data unavailable",
          "Processing bottlenecks",
          "Quality control failures",
          "Network connectivity issues"
        ],
        "recovery_actions": [
          "Switch to cached data sources",
          "Scale processing resources",
          "Enable simplified processing pipeline",
          "Activate data replication protocols"
        ]
      },
      {
        "id": "CONT_003",
        "category": "Contingency Planning", 
        "title": "Security Incident Response",
        "description": "Response plan for security-related incidents",
        "incident_types": [
          "Adversarial attack detection",
          "Data poisoning attempts",
          "Unauthorized access attempts",
          "Model extraction attacks"
        ],
        "response_actions": [
          "Isolate affected system components",
          "Enable enhanced monitoring",
          "Switch to hardened detection mode",
          "Preserve forensic evidence",
          "Engage security team"
        ]
      }
    ],
    "monitoring_configuration": {
      "alert_channels": {
        "email": ["ops-team@starlight.org", "ml-team@starlight.org"],
        "slack": "#starlight-alerts",
        "pagerduty": "starlight-ops",
        "sms": ["+1-555-EMERG1", "+1-555-EMERG2"]
      },
      "escalation_levels": {
        "level_1": "Automatic alerts and logging",
        "level_2": "Team notification within 5 minutes",
        "level_3": "Manager escalation after 15 minutes",
        "level_4": "Executive notification after 30 minutes"
      },
      "dashboard_refresh": "30 seconds",
      "log_retention": "30 days",
      "metrics_retention": "90 days"
    }
  }
}