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AI Insights & Predictive Analytics

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Overzicht

PulseGuard’s AI-gedreven inzichten gebruiken machine learning en predictive analytics om problemen te voorspellen voordat ze impact hebben op uw gebruikers. Het systeem leert van historische data en detecteert afwijkende patronen.

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Predictive Analytics

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Response Time Voorspellingen

{
  "response_time_predictions": {
    "model_type": "time_series_forecasting",
    "algorithm": "ARIMA + LSTM",
    "training_data_period": "90_days",
    "prediction_horizon": "24_hours",
    "confidence_intervals": [0.80, 0.95],
    "accuracy_metrics": {
      "mae": 45.2,  // Mean Absolute Error in milliseconds
      "rmse": 67.8, // Root Mean Square Error
      "mape": 8.5   // Mean Absolute Percentage Error
    }
  }
}

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Failure Prediction

{
  "failure_prediction": {
    "models": {
      "domain_failure": {
        "features": ["response_time_trend", "error_rate", "ssl_expiry_days", "dns_changes"],
        "algorithm": "Random Forest",
        "accuracy": 0.87,
        "false_positive_rate": 0.03
      },
      "service_failure": {
        "features": ["cpu_usage", "memory_usage", "connection_count", "error_logs"],
        "algorithm": "Neural Network",
        "accuracy": 0.92,
        "prediction_window": "2_hours"
      }
    }
  }
}

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Anomaly Detection

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Real-time Anomaly Detection

{
  "anomaly_detection": {
    "algorithms": {
      "statistical": {
        "method": "z_score",
        "threshold": 3.0,
        "seasonal_adjustment": true
      },
      "machine_learning": {
        "method": "isolation_forest",
        "contamination": 0.1,
        "features": ["cpu", "memory", "disk", "network"]
      }
    },
    "sensitivity_levels": {
      "low": { "false_positive_rate": 0.05 },
      "medium": { "false_positive_rate": 0.02 },
      "high": { "false_positive_rate": 0.01 }
    }
  }
}

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Pattern Recognition

{
  "pattern_recognition": {
    "recurring_patterns": {
      "daily_peak_load": {
        "pattern": "increased_load_9am_5pm",
        "confidence": 0.95,
        "impact": "cpu_usage +25%"
      },
      "weekend_maintenance": {
        "pattern": "service_restart_saturday_2am",
        "confidence": 0.88,
        "impact": "5_min_downtime"
      }
    },
    "seasonal_patterns": {
      "holiday_traffic": {
        "pattern": "traffic_spike_christmas",
        "prediction_accuracy": 0.91
      }
    }
  }
}

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Smart Alerting

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Context-aware Alerts

{
  "smart_alerts": {
    "context_analysis": {
      "business_hours": {
        "timezone": "Europe/Amsterdam",
        "working_hours": "09:00-17:00",
        "severity_multiplier": 1.5
      },
      "peak_hours": {
        "defined_peaks": ["12:00-13:00", "18:00-20:00"],
        "alert_suppression": false
      }
    },
    "suggested_actions": {
      "high_cpu": {
        "alert": "CPU usage above 90%",
        "suggestions": [
          "Scale up instance",
          "Check running processes",
          "Review recent deployments"
        ]
      },
      "memory_leak": {
        "alert": "Memory usage steadily increasing",
        "suggestions": [
          "Restart application",
          "Check for memory leaks",
          "Increase instance size"
        ]
      }
    }
  }
}

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Alert Prioritization

{
  "alert_prioritization": {
    "scoring_factors": {
      "severity": 0.4,
      "affected_users": 0.3,
      "business_impact": 0.2,
      "historical_frequency": 0.1
    },
    "priority_levels": {
      "critical": { "score_threshold": 0.8, "escalation_time": "5_min" },
      "high": { "score_threshold": 0.6, "escalation_time": "15_min" },
      "medium": { "score_threshold": 0.4, "escalation_time": "1_hour" },
      "low": { "score_threshold": 0.2, "escalation_time": "4_hours" }
    }
  }
}

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Root Cause Analysis

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Automated RCA

{
  "root_cause_analysis": {
    "correlation_engine": {
      "time_window": "1_hour",
      "correlation_threshold": 0.7,
      "metrics_analyzed": ["cpu", "memory", "disk", "network", "logs"]
    },
    "causality_graph": {
      "nodes": ["web_server", "database", "load_balancer", "cdn"],
      "edges": [
        {"from": "web_server", "to": "database", "correlation": 0.85},
        {"from": "database", "to": "load_balancer", "correlation": 0.72}
      ]
    }
  }
}

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AI-generated Explanations

{
  "ai_explanations": {
    "incident_analysis": {
      "incident_id": "inc-123",
      "root_cause": "Database connection pool exhaustion due to traffic spike",
      "confidence": 0.89,
      "evidence": [
        "Connection count increased 300% in 10 minutes",
        "Database CPU usage reached 95%",
        "Application logs show 'connection timeout' errors",
        "Similar pattern occurred 2 weeks ago after marketing campaign"
      ],
      "recommended_actions": [
        "Increase connection pool size from 50 to 100",
        "Implement connection pooling circuit breaker",
        "Add database performance monitoring"
      ]
    }
  }
}

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Performance Insights

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Bottleneck Detection

{
  "bottleneck_detection": {
    "performance_analysis": {
      "slowest_components": [
        {
          "component": "database_queries",
          "avg_response_time": 1200,
          "bottleneck_probability": 0.75,
          "recommendations": ["Add database indexes", "Optimize queries"]
        },
        {
          "component": "external_api_calls",
          "avg_response_time": 800,
          "bottleneck_probability": 0.60,
          "recommendations": ["Implement caching", "Use async calls"]
        }
      ]
    }
  }
}

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Capacity Planning

{
  "capacity_planning": {
    "resource_forecasting": {
      "cpu_forecast": {
        "current_usage": 65,
        "predicted_usage_30d": 78,
        "recommended_capacity": 85
      },
      "memory_forecast": {
        "current_usage": 70,
        "predicted_usage_30d": 82,
        "recommended_capacity": 90
      }
    },
    "scaling_recommendations": {
      "vertical_scaling": {
        "cpu_cores": "increase to 8",
        "memory_gb": "increase to 32",
        "cost_impact": "+$50/month"
      },
      "horizontal_scaling": {
        "additional_instances": 2,
        "load_balancer_config": "round_robin",
        "cost_impact": "+$120/month"
      }
    }
  }
}

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Learning & Adaptation

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Model Training

{
  "model_training": {
    "continuous_learning": {
      "enabled": true,
      "retraining_interval": "7_days",
      "data_retention": "1_year",
      "feedback_loop": {
        "user_feedback": true,
        "accuracy_tracking": true,
        "model_improvement": true
      }
    },
    "custom_models": {
      "business_specific": {
        "training_data": "company_logs_2023.json",
        "model_type": "custom_anomaly_detector",
        "accuracy_target": 0.95
      }
    }
  }
}

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False Positive Reduction

{
  "false_positive_reduction": {
    "adaptive_thresholds": {
      "enabled": true,
      "learning_rate": 0.1,
      "minimum_samples": 1000,
      "confidence_threshold": 0.85
    },
    "feedback_system": {
      "user_corrections": true,
      "auto_learning": true,
      "model_recalibration": true
    }
  }
}

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Integration met AI Providers

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Google AI Integration

{
  "ai_providers": {
    "google_ai": {
      "models": ["gemini-pro", "palm-2"],
      "use_cases": ["natural_language_processing", "predictive_analytics"],
      "api_key_configured": true,
      "rate_limits": {
        "requests_per_minute": 60,
        "requests_per_day": 1000
      }
    }
  }
}

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OpenAI Integration

{
  "openai_integration": {
    "models": ["gpt-4", "gpt-3.5-turbo"],
    "use_cases": ["incident_analysis", "chat_interface"],
    "api_key_configured": true,
    "fine_tuning": {
      "custom_model": "pulseguard-incident-analyzer",
      "training_data": "incident_logs_2023.json",
      "accuracy_improvement": 0.15
    }
  }
}

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AI Chat Interface

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Conversational Monitoring

{
  "chat_interface": {
    "capabilities": {
      "natural_language_queries": true,
      "voice_commands": false,
      "multi_language_support": ["nl", "en", "de"],
      "context_awareness": true
    },
    "example_queries": [
      "Is my website down?",
      "Show me CPU usage for the last hour",
      "What caused the incident yesterday?",
      "Predict when I'll need to scale my servers"
    ]
  }
}

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Proactive Insights

{
  "proactive_insights": {
    "automated_reports": {
      "daily_insights": {
        "enabled": true,
        "delivery_time": "09:00",
        "content": ["performance_summary", "anomaly_highlights", "recommendations"]
      },
      "weekly_reports": {
        "enabled": true,
        "content": ["trend_analysis", "capacity_planning", "risk_assessment"]
      }
    }
  }
}

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Privacy & Security

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Data Privacy

{
  "data_privacy": {
    "ai_data_handling": {
      "data_minimization": true,
      "anonymization": true,
      "retention_limits": "2_years",
      "user_consent": true,
      "gdpr_compliance": true
    },
    "model_security": {
      "encrypted_models": true,
      "access_controls": true,
      "audit_logging": true
    }
  }
}

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Explainable AI

{
  "explainable_ai": {
    "model_transparency": {
      "feature_importance": true,
      "decision_explanations": true,
      "confidence_scores": true,
      "bias_detection": true
    },
    "user_trust": {
      "confidence_intervals": true,
      "uncertainty_flags": true,
      "human_override": true
    }
  }
}

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API Integration

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AI Insights API

# Predictive analytics
GET /api/ai/predictions/domain/{id}

# Anomaly detection results
GET /api/ai/anomalies?period=24h

# AI-generated insights
GET /api/ai/insights/summary

# Root cause analysis
POST /api/ai/analyze-incident/{incident_id}

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Model Management

# Model status
GET /api/ai/models/status

# Retrain model
POST /api/ai/models/{model_id}/retrain

# Model performance metrics
GET /api/ai/models/{model_id}/metrics

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Best Practices

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Implementation Strategy

  1. Start Small: Begin met basis anomaly detection
  2. Validate Results: Controleer AI voorspellingen handmatig eerst
  3. Gradual Adoption: Breid AI features geleidelijk uit
  4. Monitor Accuracy: Track en verbeter model performance

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Data Quality

  1. Clean Data: Zorg voor accurate, consistente data
  2. Feature Engineering: Selecteer relevante features
  3. Data Labeling: Label incidenten voor supervised learning
  4. Continuous Monitoring: Monitor data quality continu

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Human-AI Collaboration

  1. AI as Assistant: Gebruik AI voor inzichten, niet beslissingen
  2. Human Override: Altijd mogelijkheid tot manual override
  3. Feedback Loop: Geef feedback aan AI systemen
  4. Training: Train team in AI interpretatie

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Performance Optimization

  1. Model Optimization: Balance accuracy vs performance
  2. Caching: Cache AI resultaten waar mogelijk
  3. Batch Processing: Process grote datasets efficient
  4. Resource Management: Monitor AI resource usage