# Enterprise observability architecture configuration
observability:
  global_config:
    retention_policies:
      metrics:
        high_resolution: "7d"    # 15s resolution
        medium_resolution: "30d" # 1m resolution  
        low_resolution: "365d"   # 5m resolution
      traces:
        detailed: "3d"
        sampled: "30d"
        aggregated: "90d"
      logs:
        critical: "90d"
        warning: "30d"
        info: "7d"
        debug: "1d"
    
    sampling_strategies:
      traces:
        default_strategy: "probabilistic"
        default_rate: 0.1
        per_service_strategies:
          - service: "payment-service"
            strategy: "rate_limiting"
            max_traces_per_second: 100
          - service: "auth-service"
            strategy: "adaptive"
            target_traces_per_second: 50
      
      metrics:
        high_cardinality_limit: 10000
        label_value_length_limit: 256
        metric_retention_days: 365

  collection_tier:
    edge_collectors:
      type: "otel-collector"
      deployment_strategy: "daemonset"
      resource_limits:
        cpu: "500m"
        memory: "1Gi"
      configuration:
        receivers:
          - otlp
          - prometheus
          - jaeger
          - zipkin
        processors:
          - memory_limiter
          - batch
          - resource_detection
          - k8s_attributes
        exporters:
          - otlp/regional
          - prometheus/local

    regional_gateways:
      type: "otel-gateway"
      deployment_strategy: "deployment"
      replicas: 3
      resource_limits:
        cpu: "2"
        memory: "4Gi"
      configuration:
        processors:
          - tail_sampling
          - span_metrics
          - transform
          - groupbytrace
        exporters:
          - otlp/central
          - jaeger/regional
          - prometheus/central

  storage_tier:
    metrics:
      primary:
        type: "prometheus"
        high_availability: true
        retention: "15d"
        storage_class: "ssd"
        resources:
          cpu: "4"
          memory: "16Gi"
          storage: "500Gi"
      
      long_term:
        type: "victoriametrics"
        cluster_mode: true
        retention: "2y"
        storage_class: "hdd"
        compression: "zstd"
        resources:
          cpu: "8"
          memory: "32Gi"
          storage: "10Ti"

    traces:
      type: "jaeger"
      backend: "elasticsearch"
      high_availability: true
      elasticsearch:
        cluster_size: 5
        storage_class: "ssd"
        retention_days: 30
        
    logs:
      type: "elasticsearch"
      cluster_config:
        master_nodes: 3
        data_nodes: 6
        ingest_nodes: 3
      storage:
        hot_tier: "ssd"
        warm_tier: "hdd"
        cold_tier: "s3"

  analysis_tier:
    correlation_engine:
      type: "custom"
      ai_enabled: true
      real_time_analysis: true
      
    anomaly_detection:
      type: "prometheus-anomaly-detector"
      machine_learning_backend: "tensorflow"
      
    predictive_analytics:
      type: "time-series-forecasting"
      models: ["arima", "lstm", "prophet"]

// Advanced telemetry collection framework
package telemetry

import (
    "context"
    "time"
    "sync"
    "github.com/prometheus/client_golang/prometheus"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/trace"
    "go.opentelemetry.io/otel/metric"
)

// EnterpriseCollector manages intelligent telemetry collection
type EnterpriseCollector struct {
    MetricsRegistry     prometheus.Registerer
    TracerProvider     trace.TracerProvider
    MeterProvider      metric.MeterProvider
    
    // Intelligent collection components
    AdaptiveSampler    *AdaptiveSampler
    AnomalyDetector    *AnomalyDetector
    CorrelationEngine  *CorrelationEngine
    CostOptimizer      *CostOptimizer
    
    // Storage and processing
    StreamProcessor    *StreamProcessor
    BatchProcessor     *BatchProcessor
    StorageManager     *StorageManager
    
    // Configuration
    Config             *CollectionConfig
}

// AdaptiveSampler dynamically adjusts sampling rates based on system behavior
type AdaptiveSampler struct {
    ServiceRates       map[string]*SamplingRate
    GlobalTargetRate   float64
    MinSampleRate      float64
    MaxSampleRate      float64
    AdjustmentInterval time.Duration
    
    // ML components
    PredictionModel    *PredictionModel
    FeatureExtractor   *FeatureExtractor
    
    mutex sync.RWMutex
}

type SamplingRate struct {
    Current        float64
    Target         float64
    ErrorRate      float64
    Latency        time.Duration
    ThroughputQPS  float64
    LastAdjusted   time.Time
}

// Adjust sampling rates based on system conditions
func (as *AdaptiveSampler) AdjustSamplingRates(ctx context.Context) error {
    as.mutex.Lock()
    defer as.mutex.Unlock()
    
    // Collect current system metrics
    systemMetrics := as.collectSystemMetrics(ctx)
    
    for serviceName, rate := range as.ServiceRates {
        // Extract features for ML model
        features := as.FeatureExtractor.ExtractFeatures(serviceName, systemMetrics)
        
        // Predict optimal sampling rate
        predictedRate, confidence := as.PredictionModel.Predict(features)
        
        // Apply adjustments based on confidence and system state
        newRate := as.calculateNewRate(rate, predictedRate, confidence, systemMetrics)
        
        // Enforce constraints
        newRate = math.Max(as.MinSampleRate, math.Min(as.MaxSampleRate, newRate))
        
        // Update sampling rate
        rate.Current = newRate
        rate.LastAdjusted = time.Now()
        
        // Apply to collectors
        if err := as.updateCollectorConfig(serviceName, newRate); err != nil {
            return fmt.Errorf("failed to update collector config for %s: %w", serviceName, err)
        }
    }
    
    return nil
}

// AnomalyDetector identifies unusual patterns in telemetry data
type AnomalyDetector struct {
    Models           map[string]*AnomalyModel
    AlertManager     *AlertManager
    HistoricalData   *HistoricalDataStore
    RealTimeAnalyzer *RealTimeAnalyzer
    
    // Detection strategies
    StatisticalDetection *StatisticalDetector
    MLDetection         *MLAnomalyDetector
    PatternDetection    *PatternDetector
}

type AnomalyModel struct {
    ServiceName      string
    ModelType        string    // "statistical", "ml", "pattern"
    Sensitivity      float64
    TrainingData     []DataPoint
    LastTrained      time.Time
    Accuracy         float64
    FalsePositiveRate float64
}

func (ad *AnomalyDetector) DetectAnomalies(ctx context.Context, dataPoint *DataPoint) (*AnomalyResult, error) {
    results := make([]*ModelResult, 0)
    
    // Run statistical detection
    if statResult, err := ad.StatisticalDetection.Analyze(dataPoint); err == nil {
        results = append(results, statResult)
    }
    
    // Run ML-based detection
    if mlResult, err := ad.MLDetection.Analyze(dataPoint); err == nil {
        results = append(results, mlResult)
    }
    
    // Run pattern-based detection
    if patternResult, err := ad.PatternDetection.Analyze(dataPoint); err == nil {
        results = append(results, patternResult)
    }
    
    // Correlate results
    anomalyResult := ad.correlateResults(results, dataPoint)
    
    // Generate alerts if anomaly detected
    if anomalyResult.IsAnomalous && anomalyResult.Confidence > ad.getAlertThreshold(dataPoint.ServiceName) {
        if err := ad.AlertManager.SendAnomaly(ctx, anomalyResult); err != nil {
            return anomalyResult, fmt.Errorf("failed to send anomaly alert: %w", err)
        }
    }
    
    return anomalyResult, nil
}

// CorrelationEngine finds relationships across different telemetry signals
type CorrelationEngine struct {
    TraceMetricsCorrelator  *TraceMetricsCorrelator
    LogMetricsCorrelator    *LogMetricsCorrelator
    CrossServiceCorrelator  *CrossServiceCorrelator
    TemporalCorrelator      *TemporalCorrelator
    
    // Graph analysis
    ServiceDependencyGraph  *ServiceGraph
    CorrelationGraph        *CorrelationGraph
    
    // Storage for correlation data
    CorrelationStore        *CorrelationStore
}

func (ce *CorrelationEngine) CorrelateIncident(ctx context.Context, incident *Incident) (*CorrelationResult, error) {
    result := &CorrelationResult{
        IncidentID:     incident.ID,
        Timestamp:     time.Now(),
        Correlations:  make([]*Correlation, 0),
        RootCauses:    make([]*RootCause, 0),
    }
    
    // Find temporal correlations
    timeWindow := incident.Duration.Add(5 * time.Minute)
    temporalCorrs, err := ce.TemporalCorrelator.FindCorrelations(
        incident.StartTime.Add(-timeWindow),
        incident.EndTime.Add(timeWindow),
        incident.Services,
    )
    if err != nil {
        return nil, fmt.Errorf("temporal correlation failed: %w", err)
    }
    result.Correlations = append(result.Correlations, temporalCorrs...)
    
    // Find cross-service correlations
    serviceCorrs, err := ce.CrossServiceCorrelator.FindCorrelations(incident)
    if err != nil {
        return nil, fmt.Errorf("cross-service correlation failed: %w", err)
    }
    result.Correlations = append(result.Correlations, serviceCorrs...)
    
    // Analyze service dependency impact
    dependencyImpact, err := ce.ServiceDependencyGraph.AnalyzeImpact(incident)
    if err != nil {
        return nil, fmt.Errorf("dependency analysis failed: %w", err)
    }
    result.DependencyImpact = dependencyImpact
    
    // Identify potential root causes
    rootCauses, err := ce.identifyRootCauses(result.Correlations, dependencyImpact)
    if err != nil {
        return nil, fmt.Errorf("root cause analysis failed: %w", err)
    }
    result.RootCauses = rootCauses
    
    return result, nil
}

// CostOptimizer manages observability costs while maintaining quality
type CostOptimizer struct {
    BudgetManager       *BudgetManager
    RetentionOptimizer  *RetentionOptimizer
    SamplingOptimizer   *SamplingOptimizer
    StorageTierManager  *StorageTierManager
    
    // Cost tracking
    CostTracker        *CostTracker
    UsageAnalyzer      *UsageAnalyzer
    ROICalculator      *ROICalculator
}

func (co *CostOptimizer) OptimizeCosts(ctx context.Context) (*CostOptimizationResult, error) {
    currentCosts := co.CostTracker.GetCurrentCosts()
    budget := co.BudgetManager.GetCurrentBudget()
    
    result := &CostOptimizationResult{
        CurrentCosts:    currentCosts,
        Budget:         budget,
        Optimizations:  make([]*Optimization, 0),
    }
    
    // Optimize retention policies
    retentionOpts, err := co.RetentionOptimizer.OptimizeRetention(currentCosts, budget)
    if err != nil {
        return nil, fmt.Errorf("retention optimization failed: %w", err)
    }
    result.Optimizations = append(result.Optimizations, retentionOpts...)
    
    // Optimize sampling rates
    samplingOpts, err := co.SamplingOptimizer.OptimizeSampling(currentCosts, budget)
    if err != nil {
        return nil, fmt.Errorf("sampling optimization failed: %w", err)
    }
    result.Optimizations = append(result.Optimizations, samplingOpts...)
    
    // Optimize storage tiers
    storageOpts, err := co.StorageTierManager.OptimizeStorage(currentCosts, budget)
    if err != nil {
        return nil, fmt.Errorf("storage optimization failed: %w", err)
    }
    result.Optimizations = append(result.Optimizations, storageOpts...)
    
    // Calculate projected savings
    result.ProjectedSavings = co.calculateProjectedSavings(result.Optimizations)
    result.ROI = co.ROICalculator.CalculateROI(result.Optimizations)
    
    return result, nil
}

// Enterprise distributed tracing implementation
package tracing

import (
    "context"
    "time"
    "go.opentelemetry.io/otel/trace"
    "go.opentelemetry.io/otel/attribute"
)

// EnterpriseTracing manages distributed tracing for enterprise environments
type EnterpriseTracing struct {
    TracerProvider      trace.TracerProvider
    SpanProcessor      *EnterpriseSpanProcessor
    SamplingManager    *EnterpriseSamplingManager
    CorrelationManager *TraceCorrelationManager
    
    // Analysis and intelligence
    TraceAnalyzer      *TraceAnalyzer
    PerformanceProfiler *PerformanceProfiler
    DependencyMapper   *DependencyMapper
    
    // Storage and retrieval
    TraceStore         *TraceStore
    QueryEngine        *TraceQueryEngine
}

// EnterpriseSpanProcessor handles span processing with enterprise features
type EnterpriseSpanProcessor struct {
    BatchProcessor     *BatchSpanProcessor
    EnrichmentProcessor *EnrichmentProcessor
    SanitizationProcessor *SanitizationProcessor
    CompressionProcessor *CompressionProcessor
    
    // Security and compliance
    PIIDetector       *PIIDetector
    AccessController  *AccessController
    AuditLogger       *AuditLogger
}

func (esp *EnterpriseSpanProcessor) OnStart(ctx context.Context, s trace.ReadWriteSpan) {
    // Enrich span with enterprise metadata
    esp.EnrichmentProcessor.EnrichSpan(s)
    
    // Detect and sanitize PII
    if esp.PIIDetector.DetectPII(s) {
        esp.SanitizationProcessor.SanitizeSpan(s)
        esp.AuditLogger.LogPIIDetection(s)
    }
    
    // Apply access controls
    esp.AccessController.ApplyAccessControls(s)
}

func (esp *EnterpriseSpanProcessor) OnEnd(s trace.ReadOnlySpan) {
    // Compress span data if configured
    compressedSpan := esp.CompressionProcessor.CompressSpan(s)
    
    // Send to batch processor
    esp.BatchProcessor.OnEnd(compressedSpan)
    
    // Update metrics
    esp.updateTraceMetrics(s)
}

// TraceAnalyzer provides intelligent analysis of trace data
type TraceAnalyzer struct {
    CriticalPathAnalyzer    *CriticalPathAnalyzer
    BottleneckDetector     *BottleneckDetector
    ErrorAnalyzer          *ErrorAnalyzer
    PerformanceRegression  *PerformanceRegressionDetector
    
    // Machine learning components
    PatternRecognition     *PatternRecognitionEngine
    AnomalyDetection       *TraceAnomalyDetector
    PredictiveAnalytics    *TracePredictiveAnalytics
}

func (ta *TraceAnalyzer) AnalyzeTrace(trace *Trace) (*TraceAnalysis, error) {
    analysis := &TraceAnalysis{
        TraceID:          trace.ID,
        StartTime:        trace.StartTime,
        EndTime:          trace.EndTime,
        TotalDuration:    trace.Duration,
        ServiceCount:     len(trace.Services),
        SpanCount:        len(trace.Spans),
    }
    
    // Analyze critical path
    criticalPath, err := ta.CriticalPathAnalyzer.FindCriticalPath(trace)
    if err != nil {
        return nil, fmt.Errorf("critical path analysis failed: %w", err)
    }
    analysis.CriticalPath = criticalPath
    
    // Detect bottlenecks
    bottlenecks, err := ta.BottleneckDetector.DetectBottlenecks(trace)
    if err != nil {
        return nil, fmt.Errorf("bottleneck detection failed: %w", err)
    }
    analysis.Bottlenecks = bottlenecks
    
    // Analyze errors
    errorAnalysis, err := ta.ErrorAnalyzer.AnalyzeErrors(trace)
    if err != nil {
        return nil, fmt.Errorf("error analysis failed: %w", err)
    }
    analysis.ErrorAnalysis = errorAnalysis
    
    // Check for performance regressions
    regressions, err := ta.PerformanceRegression.CheckRegression(trace)
    if err != nil {
        return nil, fmt.Errorf("regression detection failed: %w", err)
    }
    analysis.PerformanceRegressions = regressions
    
    // Pattern recognition
    patterns, err := ta.PatternRecognition.IdentifyPatterns(trace)
    if err != nil {
        return nil, fmt.Errorf("pattern recognition failed: %w", err)
    }
    analysis.Patterns = patterns
    
    return analysis, nil
}

// DependencyMapper builds service dependency graphs from traces
type DependencyMapper struct {
    DependencyGraph    *ServiceDependencyGraph
    RelationshipAnalyzer *RelationshipAnalyzer
    ImpactAnalyzer     *ImpactAnalyzer
    
    // Temporal analysis
    TemporalAnalyzer   *TemporalDependencyAnalyzer
    VersionTracker     *ServiceVersionTracker
}

func (dm *DependencyMapper) BuildDependencyMap(traces []*Trace) (*DependencyMap, error) {
    dependencyMap := &DependencyMap{
        Services:      make(map[string]*ServiceNode),
        Dependencies:  make([]*Dependency, 0),
        Clusters:      make([]*ServiceCluster, 0),
        LastUpdated:   time.Now(),
    }
    
    // Extract service relationships from traces
    for _, trace := range traces {
        relationships, err := dm.RelationshipAnalyzer.ExtractRelationships(trace)
        if err != nil {
            continue // Log error but continue processing
        }
        
        for _, rel := range relationships {
            dm.addRelationshipToMap(dependencyMap, rel)
        }
    }
    
    // Analyze dependency strength and criticality
    for _, dep := range dependencyMap.Dependencies {
        dep.Strength = dm.calculateDependencyStrength(dep, traces)
        dep.Criticality = dm.calculateDependencyCriticality(dep, dependencyMap)
    }
    
    // Identify service clusters
    clusters, err := dm.identifyServiceClusters(dependencyMap)
    if err != nil {
        return nil, fmt.Errorf("cluster identification failed: %w", err)
    }
    dependencyMap.Clusters = clusters
    
    return dependencyMap, nil
}

// Enterprise SLI/SLO management system
package slo

import (
    "context"
    "time"
    "math"
)

// SLOManager manages Service Level Objectives for enterprise services
type SLOManager struct {
    SLOStore           *SLOStore
    SLICollector       *SLICollector
    ErrorBudgetManager *ErrorBudgetManager
    AlertManager       *SLOAlertManager
    
    // Analysis and reporting
    BurnRateAnalyzer   *BurnRateAnalyzer
    TrendAnalyzer      *TrendAnalyzer
    ReportGenerator    *SLOReportGenerator
}

// SLO represents a Service Level Objective
type SLO struct {
    ID                string           `json:"id"`
    Name              string           `json:"name"`
    Description       string           `json:"description"`
    Service           string           `json:"service"`
    Owner             string           `json:"owner"`
    
    // SLI definition
    SLI               *SLI             `json:"sli"`
    
    // Objective definition
    Target            float64          `json:"target"`           // e.g., 99.9
    TimeWindow        string           `json:"time_window"`      // e.g., "30d"
    
    // Error budget
    ErrorBudgetPolicy *ErrorBudgetPolicy `json:"error_budget_policy"`
    
    // Alerting
    AlertingPolicy    *AlertingPolicy  `json:"alerting_policy"`
    
    // Metadata
    CreatedAt         time.Time        `json:"created_at"`
    UpdatedAt         time.Time        `json:"updated_at"`
    Version           int              `json:"version"`
    
    // Compliance and approval
    ComplianceLevel   ComplianceLevel  `json:"compliance_level"`
    ApprovalStatus    ApprovalStatus   `json:"approval_status"`
    Approvers         []string         `json:"approvers"`
}

// SLI represents a Service Level Indicator
type SLI struct {
    Type              SLIType          `json:"type"`
    Query             string           `json:"query"`
    GoodEventsQuery   string           `json:"good_events_query,omitempty"`
    TotalEventsQuery  string           `json:"total_events_query,omitempty"`
    ThresholdQuery    string           `json:"threshold_query,omitempty"`
    
    // Data source configuration
    DataSource        DataSourceConfig `json:"data_source"`
    
    // Processing configuration
    AggregationWindow time.Duration    `json:"aggregation_window"`
    ProcessingDelay   time.Duration    `json:"processing_delay"`
}

type SLIType string

const (
    SLITypeAvailability SLIType = "availability"
    SLITypeLatency     SLIType = "latency"
    SLITypeThroughput  SLIType = "throughput"
    SLITypeErrorRate   SLIType = "error_rate"
    SLITypeCustom      SLIType = "custom"
)

// ErrorBudgetPolicy defines how error budgets are managed
type ErrorBudgetPolicy struct {
    BurnRateThresholds map[string]float64 `json:"burn_rate_thresholds"`
    Actions           []ErrorBudgetAction `json:"actions"`
    ResetPolicy       ResetPolicy        `json:"reset_policy"`
}

type ErrorBudgetAction struct {
    Threshold        float64              `json:"threshold"`
    Action           ActionType           `json:"action"`
    Parameters       map[string]interface{} `json:"parameters"`
    NotificationChannels []string         `json:"notification_channels"`
}

type ActionType string

const (
    ActionAlert        ActionType = "alert"
    ActionBlock        ActionType = "block_deployment"
    ActionThrottle     ActionType = "throttle_traffic"
    ActionNotify       ActionType = "notify_team"
    ActionAutoScale    ActionType = "auto_scale"
)

// CalculateSLOCompliance calculates current SLO compliance
func (sm *SLOManager) CalculateSLOCompliance(ctx context.Context, slo *SLO) (*SLOCompliance, error) {
    // Parse time window
    timeWindow, err := parseDuration(slo.TimeWindow)
    if err != nil {
        return nil, fmt.Errorf("invalid time window: %w", err)
    }
    
    endTime := time.Now()
    startTime := endTime.Add(-timeWindow)
    
    // Collect SLI data
    sliData, err := sm.SLICollector.CollectSLI(ctx, slo.SLI, startTime, endTime)
    if err != nil {
        return nil, fmt.Errorf("failed to collect SLI data: %w", err)
    }
    
    // Calculate compliance
    compliance := &SLOCompliance{
        SLOID:          slo.ID,
        TimeWindow:     slo.TimeWindow,
        Target:         slo.Target,
        ActualValue:    sliData.Value,
        Compliance:     sliData.Value,
        ErrorBudget:    sm.calculateErrorBudget(slo, sliData),
        CalculatedAt:   time.Now(),
    }
    
    // Calculate burn rate
    burnRate, err := sm.BurnRateAnalyzer.CalculateBurnRate(slo, sliData)
    if err != nil {
        return nil, fmt.Errorf("burn rate calculation failed: %w", err)
    }
    compliance.BurnRate = burnRate
    
    // Check for violations
    if compliance.ActualValue < slo.Target {
        compliance.Status = SLOStatusViolated
        compliance.ViolationDetails = &ViolationDetails{
            StartTime:    findViolationStartTime(sliData, slo.Target),
            Severity:     calculateViolationSeverity(compliance.ActualValue, slo.Target),
            ImpactRadius: calculateImpactRadius(slo),
        }
    } else {
        compliance.Status = SLOStatusMet
    }
    
    return compliance, nil
}

// BurnRateAnalyzer analyzes error budget burn rates
type BurnRateAnalyzer struct {
    HistoricalData    *HistoricalDataStore
    PredictionModel   *BurnRatePredictionModel
    ThresholdManager  *BurnRateThresholdManager
}

func (bra *BurnRateAnalyzer) AnalyzeBurnRate(ctx context.Context, slo *SLO) (*BurnRateAnalysis, error) {
    // Calculate current burn rate
    currentBurnRate, err := bra.calculateCurrentBurnRate(slo)
    if err != nil {
        return nil, fmt.Errorf("current burn rate calculation failed: %w", err)
    }
    
    // Get historical burn rate data
    historicalData, err := bra.HistoricalData.GetBurnRateHistory(slo.ID, 30*24*time.Hour)
    if err != nil {
        return nil, fmt.Errorf("failed to get historical data: %w", err)
    }
    
    // Predict future burn rate
    prediction, err := bra.PredictionModel.PredictBurnRate(historicalData, 24*time.Hour)
    if err != nil {
        return nil, fmt.Errorf("burn rate prediction failed: %w", err)
    }
    
    analysis := &BurnRateAnalysis{
        SLOID:           slo.ID,
        CurrentBurnRate: currentBurnRate,
        PredictedBurnRate: prediction.BurnRate,
        PredictionConfidence: prediction.Confidence,
        TimeToExhaustion: bra.calculateTimeToExhaustion(currentBurnRate, slo),
        Recommendations: bra.generateRecommendations(currentBurnRate, prediction, slo),
        AnalyzedAt:     time.Now(),
    }
    
    return analysis, nil
}

// AlertManager handles SLO-based alerting
type SLOAlertManager struct {
    AlertingBackend   AlertingBackend
    EscalationManager *EscalationManager
    NotificationChannels map[string]NotificationChannel
    
    // Alert optimization
    AlertOptimizer    *AlertOptimizer
    FatigueManager    *AlertFatigueManager
}

func (sam *SLOAlertManager) ProcessSLOViolation(ctx context.Context, violation *SLOViolation) error {
    // Check if alert should be sent (fatigue management)
    if !sam.FatigueManager.ShouldAlert(violation) {
        return nil
    }
    
    // Generate alert
    alert := &Alert{
        ID:          generateAlertID(),
        Type:        AlertTypeSLOViolation,
        Severity:    violation.Severity,
        Title:       fmt.Sprintf("SLO Violation: %s", violation.SLOName),
        Description: violation.Description,
        Timestamp:   time.Now(),
        
        // SLO-specific metadata
        SLOID:       violation.SLOID,
        ErrorBudget: violation.ErrorBudget,
        BurnRate:    violation.BurnRate,
        
        // Runbook and context
        Runbook:     sam.getRunbook(violation.SLOID),
        Context:     sam.generateAlertContext(violation),
        
        // Escalation policy
        EscalationPolicy: sam.getEscalationPolicy(violation),
    }
    
    // Optimize alert content
    optimizedAlert, err := sam.AlertOptimizer.OptimizeAlert(alert)
    if err != nil {
        return fmt.Errorf("alert optimization failed: %w", err)
    }
    
    // Send alert
    if err := sam.AlertingBackend.SendAlert(ctx, optimizedAlert); err != nil {
        return fmt.Errorf("failed to send alert: %w", err)
    }
    
    // Track alert for fatigue management
    sam.FatigueManager.TrackAlert(optimizedAlert)
    
    return nil
}

// Enterprise incident management system
package incident

import (
    "context"
    "time"
)

// IncidentManager manages the complete incident lifecycle
type IncidentManager struct {
    DetectionEngine     *IncidentDetectionEngine
    CorrelationEngine   *IncidentCorrelationEngine
    ResponseOrchestrator *ResponseOrchestrator
    CommunicationManager *CommunicationManager
    
    // Analysis and learning
    RootCauseAnalyzer   *RootCauseAnalyzer
    PostmortemManager   *PostmortemManager
    LearningEngine      *IncidentLearningEngine
    
    // Integration
    TicketingSystem     TicketingSystemInterface
    ChatOpsIntegration  ChatOpsInterface
    RunbookEngine       *RunbookEngine
}

// IncidentDetectionEngine automatically detects incidents from various signals
type IncidentDetectionEngine struct {
    AlertCorrelator     *AlertCorrelator
    AnomalyDetector     *AnomalyDetector
    PatternMatcher      *PatternMatcher
    MLDetector          *MLIncidentDetector
    
    // Configuration
    DetectionRules      []*DetectionRule
    CorrelationRules    []*CorrelationRule
    EscalationThresholds map[Severity]time.Duration
}

func (ide *IncidentDetectionEngine) DetectIncident(ctx context.Context, signals []*Signal) (*Incident, error) {
    // Correlate incoming signals
    correlatedSignals, err := ide.AlertCorrelator.CorrelateSignals(signals)
    if err != nil {
        return nil, fmt.Errorf("signal correlation failed: %w", err)
    }
    
    // Check detection rules
    for _, rule := range ide.DetectionRules {
        if match := rule.Matches(correlatedSignals); match != nil {
            incident := &Incident{
                ID:          generateIncidentID(),
                Title:       match.Title,
                Description: match.Description,
                Severity:    match.Severity,
                StartTime:   match.StartTime,
                Status:      IncidentStatusOpen,
                
                // Signal information
                TriggerSignals: correlatedSignals,
                DetectionRule:  rule,
                
                // Service impact
                AffectedServices: match.AffectedServices,
                ImpactAssessment: match.ImpactAssessment,
                
                // Initial context
                InitialContext: ide.generateInitialContext(match),
            }
            
            return incident, nil
        }
    }
    
    // Use ML detection for complex patterns
    mlDetection, err := ide.MLDetector.DetectIncident(correlatedSignals)
    if err != nil {
        return nil, fmt.Errorf("ML detection failed: %w", err)
    }
    
    if mlDetection.IsIncident {
        incident := &Incident{
            ID:          generateIncidentID(),
            Title:       mlDetection.Title,
            Description: mlDetection.Description,
            Severity:    mlDetection.Severity,
            StartTime:   mlDetection.StartTime,
            Status:      IncidentStatusOpen,
            
            // ML-specific metadata
            MLConfidence:   mlDetection.Confidence,
            MLModel:        mlDetection.ModelUsed,
            TriggerSignals: correlatedSignals,
        }
        
        return incident, nil
    }
    
    return nil, nil // No incident detected
}

// ResponseOrchestrator coordinates incident response activities
type ResponseOrchestrator struct {
    ResponderManager    *ResponderManager
    TaskOrchestrator    *TaskOrchestrator
    AutomationEngine    *AutomationEngine
    EscalationManager   *EscalationManager
    
    // Runbook execution
    RunbookExecutor     *RunbookExecutor
    PlaybookLibrary     *PlaybookLibrary
    
    // Communication
    StatusPageManager   *StatusPageManager
    StakeholderNotifier *StakeholderNotifier
}

func (ro *ResponseOrchestrator) OrchestateResponse(ctx context.Context, incident *Incident) error {
    // Create incident response context
    responseCtx := &ResponseContext{
        Incident:        incident,
        StartTime:       time.Now(),
        Responders:      make([]*Responder, 0),
        Tasks:          make([]*ResponseTask, 0),
        Communications: make([]*Communication, 0),
    }
    
    // Assign incident commander
    commander, err := ro.ResponderManager.AssignIncidentCommander(incident)
    if err != nil {
        return fmt.Errorf("failed to assign incident commander: %w", err)
    }
    responseCtx.IncidentCommander = commander
    
    // Assemble response team
    responders, err := ro.ResponderManager.AssembleResponseTeam(incident)
    if err != nil {
        return fmt.Errorf("failed to assemble response team: %w", err)
    }
    responseCtx.Responders = responders
    
    // Execute automated response actions
    if err := ro.AutomationEngine.ExecuteAutomatedResponse(responseCtx); err != nil {
        // Log error but don't fail - manual response can continue
        log.Errorf("automated response failed: %v", err)
    }
    
    // Find and execute relevant runbooks
    runbooks, err := ro.PlaybookLibrary.FindApplicableRunbooks(incident)
    if err != nil {
        return fmt.Errorf("failed to find runbooks: %w", err)
    }
    
    for _, runbook := range runbooks {
        if err := ro.RunbookExecutor.ExecuteRunbook(responseCtx, runbook); err != nil {
            log.Errorf("runbook execution failed: %v", err)
        }
    }
    
    // Setup communication channels
    if err := ro.setupCommunication(responseCtx); err != nil {
        return fmt.Errorf("communication setup failed: %w", err)
    }
    
    // Start monitoring response progress
    go ro.monitorResponseProgress(responseCtx)
    
    return nil
}

// RootCauseAnalyzer performs automated root cause analysis
type RootCauseAnalyzer struct {
    CausalityAnalyzer   *CausalityAnalyzer
    TimelineReconstructor *TimelineReconstructor
    ChangeCorrelator    *ChangeCorrelator
    DependencyAnalyzer  *DependencyAnalyzer
    
    // Knowledge base
    KnownIssuesDB      *KnownIssuesDatabase
    SolutionLibrary    *SolutionLibrary
    
    // Machine learning
    CausalMLModel      *CausalMLModel
    PatternMatcher     *PatternMatcher
}

func (rca *RootCauseAnalyzer) AnalyzeRootCause(ctx context.Context, incident *Incident) (*RootCauseAnalysis, error) {
    analysis := &RootCauseAnalysis{
        IncidentID:    incident.ID,
        StartTime:     incident.StartTime,
        AnalysisTime:  time.Now(),
        PotentialCauses: make([]*PotentialCause, 0),
        Timeline:      make([]*TimelineEvent, 0),
    }
    
    // Reconstruct timeline of events
    timeline, err := rca.TimelineReconstructor.ReconstructTimeline(incident)
    if err != nil {
        return nil, fmt.Errorf("timeline reconstruction failed: %w", err)
    }
    analysis.Timeline = timeline
    
    // Analyze change correlation
    changeEvents, err := rca.ChangeCorrelator.FindCorrelatedChanges(incident)
    if err != nil {
        return nil, fmt.Errorf("change correlation failed: %w", err)
    }
    
    for _, change := range changeEvents {
        cause := &PotentialCause{
            Type:        CauseTypeChange,
            Description: fmt.Sprintf("Change event: %s", change.Description),
            Confidence:  change.CorrelationStrength,
            Evidence:    change.Evidence,
            ChangeEvent: change,
        }
        analysis.PotentialCauses = append(analysis.PotentialCauses, cause)
    }
    
    // Analyze dependency failures
    dependencyFailures, err := rca.DependencyAnalyzer.AnalyzeDependencyFailures(incident)
    if err != nil {
        return nil, fmt.Errorf("dependency analysis failed: %w", err)
    }
    
    for _, failure := range dependencyFailures {
        cause := &PotentialCause{
            Type:        CauseTypeDependency,
            Description: fmt.Sprintf("Dependency failure: %s", failure.Service),
            Confidence:  failure.ImpactLikelihood,
            Evidence:    failure.Evidence,
            DependencyFailure: failure,
        }
        analysis.PotentialCauses = append(analysis.PotentialCauses, cause)
    }
    
    // Use ML model for causality analysis
    mlCauses, err := rca.CausalMLModel.AnalyzeCausality(incident, timeline)
    if err != nil {
        return nil, fmt.Errorf("ML causality analysis failed: %w", err)
    }
    analysis.PotentialCauses = append(analysis.PotentialCauses, mlCauses...)
    
    // Check known issues database
    knownIssues, err := rca.KnownIssuesDB.FindSimilarIssues(incident)
    if err != nil {
        return nil, fmt.Errorf("known issues lookup failed: %w", err)
    }
    analysis.SimilarIncidents = knownIssues
    
    // Rank potential causes by confidence
    analysis.PotentialCauses = rca.rankCausesByConfidence(analysis.PotentialCauses)
    
    // Generate recommendations
    recommendations, err := rca.generateRecommendations(analysis)
    if err != nil {
        return nil, fmt.Errorf("recommendation generation failed: %w", err)
    }
    analysis.Recommendations = recommendations
    
    return analysis, nil
}

// PostmortemManager manages the postmortem process
type PostmortemManager struct {
    TemplateEngine      *PostmortemTemplateEngine
    CollaborationPlatform CollaborationPlatformInterface
    ActionItemTracker   *ActionItemTracker
    LessonsLearnedDB    *LessonsLearnedDatabase
    
    // Analysis tools
    TimelineGenerator   *TimelineGenerator
    ImpactCalculator    *ImpactCalculator
    MetricsAnalyzer     *MetricsAnalyzer
}

func (pm *PostmortemManager) GeneratePostmortem(ctx context.Context, incident *Incident) (*Postmortem, error) {
    // Generate initial postmortem from template
    template, err := pm.TemplateEngine.GetTemplate(incident.Severity, incident.Category)
    if err != nil {
        return nil, fmt.Errorf("failed to get template: %w", err)
    }
    
    postmortem := &Postmortem{
        IncidentID:    incident.ID,
        Title:         fmt.Sprintf("Postmortem: %s", incident.Title),
        CreatedAt:     time.Now(),
        Status:        PostmortemStatusDraft,
        Template:      template,
    }
    
    // Generate timeline
    timeline, err := pm.TimelineGenerator.GenerateTimeline(incident)
    if err != nil {
        return nil, fmt.Errorf("timeline generation failed: %w", err)
    }
    postmortem.Timeline = timeline
    
    // Calculate impact
    impact, err := pm.ImpactCalculator.CalculateImpact(incident)
    if err != nil {
        return nil, fmt.Errorf("impact calculation failed: %w", err)
    }
    postmortem.Impact = impact
    
    // Analyze metrics during incident
    metricsAnalysis, err := pm.MetricsAnalyzer.AnalyzeIncidentMetrics(incident)
    if err != nil {
        return nil, fmt.Errorf("metrics analysis failed: %w", err)
    }
    postmortem.MetricsAnalysis = metricsAnalysis
    
    // Populate template with incident data
    if err := pm.TemplateEngine.PopulateTemplate(postmortem, incident); err != nil {
        return nil, fmt.Errorf("template population failed: %w", err)
    }
    
    return postmortem, nil
}

#!/bin/bash
# Advanced monitoring automation framework

set -euo pipefail

# Configuration
AUTOMATION_CONFIG_DIR="/etc/monitoring/automation"
SCRIPTS_DIR="/opt/monitoring/scripts"
LOGS_DIR="/var/log/monitoring/automation"
STATE_DIR="/var/lib/monitoring/automation"

# Logging with structured output
log_automation_event() {
    local level="$1"
    local automation_type="$2"
    local action="$3"
    local result="$4"
    local details="$5"
    local timestamp=$(date -u +"%Y-%m-%dT%H:%M:%S.%3NZ")
    
    echo "{\"timestamp\":\"$timestamp\",\"level\":\"$level\",\"type\":\"$automation_type\",\"action\":\"$action\",\"result\":\"$result\",\"details\":\"$details\"}" >> "$LOGS_DIR/automation.jsonl"
}

# Self-healing automation
execute_self_healing() {
    local alert_name="$1"
    local service_name="$2"
    local severity="$3"
    local context="$4"
    
    log_automation_event "INFO" "self_healing" "started" "triggered" "Alert: $alert_name, Service: $service_name"
    
    # Load healing configuration
    local config_file="$AUTOMATION_CONFIG_DIR/self_healing/$service_name.yaml"
    if [[ ! -f "$config_file" ]]; then
        config_file="$AUTOMATION_CONFIG_DIR/self_healing/default.yaml"
    fi
    
    # Parse healing strategies
    local strategies=$(yq eval '.healing_strategies[]' "$config_file")
    
    while read -r strategy; do
        [[ -z "$strategy" ]] && continue
        
        local strategy_name=$(echo "$strategy" | yq eval '.name' -)
        local conditions=$(echo "$strategy" | yq eval '.conditions[]' -)
        local actions=$(echo "$strategy" | yq eval '.actions[]' -)
        
        # Check if strategy conditions are met
        if check_healing_conditions "$conditions" "$alert_name" "$context"; then
            log_automation_event "INFO" "self_healing" "strategy_selected" "success" "Strategy: $strategy_name"
            
            # Execute healing actions
            while read -r action; do
                [[ -z "$action" ]] && continue
                execute_healing_action "$action" "$service_name" "$context"
            done <<< "$actions"
            
            # Monitor healing effectiveness
            if monitor_healing_effectiveness "$strategy_name" "$service_name" "$alert_name"; then
                log_automation_event "INFO" "self_healing" "completed" "success" "Strategy: $strategy_name worked"
                return 0
            else
                log_automation_event "WARN" "self_healing" "strategy_failed" "partial" "Strategy: $strategy_name failed"
            fi
        fi
    done <<< "$strategies"
    
    # If no strategy worked, escalate
    escalate_healing_failure "$alert_name" "$service_name" "$severity"
    return 1
}

# Predictive scaling automation
execute_predictive_scaling() {
    local service_name="$1"
    local prediction_data="$2"
    
    log_automation_event "INFO" "predictive_scaling" "started" "triggered" "Service: $service_name"
    
    # Parse prediction data
    local predicted_load=$(echo "$prediction_data" | jq -r '.predicted_load')
    local confidence=$(echo "$prediction_data" | jq -r '.confidence')
    local time_horizon=$(echo "$prediction_data" | jq -r '.time_horizon')
    
    # Get current scaling configuration
    local current_replicas=$(kubectl get deployment "$service_name" -o jsonpath='{.spec.replicas}')
    local current_cpu_request=$(kubectl get deployment "$service_name" -o jsonpath='{.spec.template.spec.containers[0].resources.requests.cpu}')
    local current_memory_request=$(kubectl get deployment "$service_name" -o jsonpath='{.spec.template.spec.containers[0].resources.requests.memory}')
    
    # Calculate required resources
    local required_replicas=$(calculate_required_replicas "$predicted_load" "$current_cpu_request" "$current_memory_request")
    local required_cpu=$(calculate_required_cpu "$predicted_load" "$required_replicas")
    local required_memory=$(calculate_required_memory "$predicted_load" "$required_replicas")
    
    # Apply scaling with confidence threshold
    if (( $(echo "$confidence > 0.8" | bc -l) )); then
        # High confidence - apply scaling
        kubectl patch deployment "$service_name" -p "{\"spec\":{\"replicas\":$required_replicas}}"
        
        # Update resource requests if needed
        if [[ "$required_cpu" != "$current_cpu_request" ]] || [[ "$required_memory" != "$current_memory_request" ]]; then
            kubectl patch deployment "$service_name" -p "{\"spec\":{\"template\":{\"spec\":{\"containers\":[{\"name\":\"$service_name\",\"resources\":{\"requests\":{\"cpu\":\"$required_cpu\",\"memory\":\"$required_memory\"}}}]}}}}"
        fi
        
        log_automation_event "INFO" "predictive_scaling" "scaled" "success" "Replicas: $current_replicas -> $required_replicas"
    elif (( $(echo "$confidence > 0.6" | bc -l) )); then
        # Medium confidence - pre-warm resources
        prewarm_resources "$service_name" "$required_replicas" "$required_cpu" "$required_memory"
        log_automation_event "INFO" "predictive_scaling" "prewarmed" "success" "Confidence: $confidence"
    else
        # Low confidence - just monitor
        log_automation_event "INFO" "predictive_scaling" "monitored" "success" "Low confidence: $confidence"
    fi
}

# Intelligent alert filtering
filter_alerts_intelligently() {
    local alerts_batch="$1"
    local filtered_alerts=()
    
    # Load filtering rules
    local filtering_config="$AUTOMATION_CONFIG_DIR/alert_filtering.yaml"
    
    while read -r alert; do
        [[ -z "$alert" ]] && continue
        
        local alert_name=$(echo "$alert" | jq -r '.alert_name')
        local service=$(echo "$alert" | jq -r '.service')
        local severity=$(echo "$alert" | jq -r '.severity')
        local timestamp=$(echo "$alert" | jq -r '.timestamp')
        
        # Check for alert fatigue
        if is_alert_fatigued "$alert_name" "$service"; then
            log_automation_event "INFO" "alert_filtering" "filtered" "fatigue" "Alert: $alert_name"
            continue
        fi
        
        # Check for maintenance windows
        if in_maintenance_window "$service" "$timestamp"; then
            log_automation_event "INFO" "alert_filtering" "filtered" "maintenance" "Alert: $alert_name"
            continue
        fi
        
        # Check for known issues
        if is_known_issue "$alert_name" "$service"; then
            log_automation_event "INFO" "alert_filtering" "filtered" "known_issue" "Alert: $alert_name"
            continue
        fi
        
        # Check for correlation patterns
        if has_correlated_alerts "$alert" "$alerts_batch"; then
            # Group correlated alerts
            local correlated_group=$(group_correlated_alerts "$alert" "$alerts_batch")
            filtered_alerts+=("$correlated_group")
            log_automation_event "INFO" "alert_filtering" "correlated" "success" "Alert: $alert_name"
        else
            filtered_alerts+=("$alert")
        fi
        
    done <<< "$alerts_batch"
    
    # Output filtered alerts
    printf '%s\n' "${filtered_alerts[@]}" | jq -s '.'
}

# Automated capacity planning
execute_capacity_planning() {
    local service_name="$1"
    local planning_horizon="${2:-30d}"
    
    log_automation_event "INFO" "capacity_planning" "started" "triggered" "Service: $service_name, Horizon: $planning_horizon"
    
    # Collect historical metrics
    local metrics_data=$(collect_capacity_metrics "$service_name" "$planning_horizon")
    
    # Analyze growth trends
    local growth_analysis=$(analyze_growth_trends "$metrics_data")
    local cpu_growth_rate=$(echo "$growth_analysis" | jq -r '.cpu_growth_rate')
    local memory_growth_rate=$(echo "$growth_analysis" | jq -r '.memory_growth_rate')
    local traffic_growth_rate=$(echo "$growth_analysis" | jq -r '.traffic_growth_rate')
    
    # Project future capacity needs
    local capacity_projection=$(project_capacity_needs "$growth_analysis" "$planning_horizon")
    local projected_cpu=$(echo "$capacity_projection" | jq -r '.projected_cpu')
    local projected_memory=$(echo "$capacity_projection" | jq -r '.projected_memory')
    local projected_replicas=$(echo "$capacity_projection" | jq -r '.projected_replicas')
    
    # Generate capacity plan
    local capacity_plan=$(generate_capacity_plan "$service_name" "$capacity_projection")
    
    # Create capacity planning report
    cat > "$STATE_DIR/capacity_plans/$service_name.json" <<EOF
{
    "service": "$service_name",
    "planning_horizon": "$planning_horizon",
    "generated_at": "$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)",
    "current_capacity": {
        "cpu": "$(kubectl get deployment "$service_name" -o jsonpath='{.spec.template.spec.containers[0].resources.requests.cpu}')",
        "memory": "$(kubectl get deployment "$service_name" -o jsonpath='{.spec.template.spec.containers[0].resources.requests.memory}')",
        "replicas": $(kubectl get deployment "$service_name" -o jsonpath='{.spec.replicas}')
    },
    "projected_capacity": {
        "cpu": "$projected_cpu",
        "memory": "$projected_memory",
        "replicas": $projected_replicas
    },
    "growth_analysis": $growth_analysis,
    "capacity_plan": $capacity_plan
}
EOF
    
    log_automation_event "INFO" "capacity_planning" "completed" "success" "Plan generated for $service_name"
}

# Cost optimization automation
execute_cost_optimization() {
    local scope="${1:-cluster}"
    local optimization_target="${2:-20}"  # 20% cost reduction target
    
    log_automation_event "INFO" "cost_optimization" "started" "triggered" "Scope: $scope, Target: $optimization_target%"
    
    # Analyze current costs
    local cost_analysis=$(analyze_current_costs "$scope")
    local total_cost=$(echo "$cost_analysis" | jq -r '.total_cost')
    local cost_breakdown=$(echo "$cost_analysis" | jq -r '.breakdown')
    
    # Identify optimization opportunities
    local optimization_opportunities=$(identify_cost_optimizations "$cost_analysis")
    
    # Calculate potential savings
    local potential_savings=$(calculate_potential_savings "$optimization_opportunities")
    local total_potential_savings=$(echo "$potential_savings" | jq -r '.total_savings')
    
    # Apply optimizations if they meet target
    local target_savings=$(echo "$total_cost * $optimization_target / 100" | bc -l)
    
    if (( $(echo "$total_potential_savings >= $target_savings" | bc -l) )); then
        while read -r optimization; do
            [[ -z "$optimization" ]] && continue
            
            local optimization_type=$(echo "$optimization" | jq -r '.type')
            local savings=$(echo "$optimization" | jq -r '.savings')
            local risk_level=$(echo "$optimization" | jq -r '.risk_level')
            
            # Apply low-risk optimizations automatically
            if [[ "$risk_level" == "low" ]]; then
                apply_cost_optimization "$optimization"
                log_automation_event "INFO" "cost_optimization" "applied" "success" "Type: $optimization_type, Savings: $savings"
            else
                # Queue medium/high-risk optimizations for review
                queue_optimization_for_review "$optimization"
                log_automation_event "INFO" "cost_optimization" "queued" "review" "Type: $optimization_type, Risk: $risk_level"
            fi
        done <<< "$optimization_opportunities"
    fi
    
    # Generate cost optimization report
    generate_cost_optimization_report "$scope" "$cost_analysis" "$optimization_opportunities" "$potential_savings"
}

# Security monitoring automation
execute_security_monitoring() {
    local monitoring_scope="${1:-cluster}"
    
    log_automation_event "INFO" "security_monitoring" "started" "triggered" "Scope: $monitoring_scope"
    
    # Scan for security vulnerabilities
    local vulnerability_scan=$(scan_security_vulnerabilities "$monitoring_scope")
    local critical_vulns=$(echo "$vulnerability_scan" | jq -r '.critical_count')
    local high_vulns=$(echo "$vulnerability_scan" | jq -r '.high_count')
    
    # Check for suspicious activities
    local suspicious_activities=$(detect_suspicious_activities "$monitoring_scope")
    
    # Analyze network security
    local network_security=$(analyze_network_security "$monitoring_scope")
    
    # Check compliance status
    local compliance_status=$(check_compliance_status "$monitoring_scope")
    
    # Generate security alerts for critical findings
    if (( critical_vulns > 0 )) || (( high_vulns > 5 )); then
        generate_security_alert "vulnerabilities" "$vulnerability_scan"
    fi
    
    if [[ $(echo "$suspicious_activities" | jq -r '.suspicious_count') -gt 0 ]]; then
        generate_security_alert "suspicious_activity" "$suspicious_activities"
    fi
    
    # Generate security monitoring report
    cat > "$STATE_DIR/security_reports/$(date +%Y%m%d-%H%M%S).json" <<EOF
{
    "timestamp": "$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)",
    "scope": "$monitoring_scope",
    "vulnerability_scan": $vulnerability_scan,
    "suspicious_activities": $suspicious_activities,
    "network_security": $network_security,
    "compliance_status": $compliance_status
}
EOF
    
    log_automation_event "INFO" "security_monitoring" "completed" "success" "Critical vulns: $critical_vulns, High vulns: $high_vulns"
}

# Main automation dispatcher
main() {
    local automation_type="$1"
    shift
    
    # Ensure required directories exist
    mkdir -p "$LOGS_DIR" "$STATE_DIR"/{capacity_plans,security_reports,cost_reports}
    
    case "$automation_type" in
        "self_healing")
            execute_self_healing "$@"
            ;;
        "predictive_scaling")
            execute_predictive_scaling "$@"
            ;;
        "alert_filtering")
            filter_alerts_intelligently "$@"
            ;;
        "capacity_planning")
            execute_capacity_planning "$@"
            ;;
        "cost_optimization")
            execute_cost_optimization "$@"
            ;;
        "security_monitoring")
            execute_security_monitoring "$@"
            ;;
        *)
            echo "Unknown automation type: $automation_type"
            echo "Available types: self_healing, predictive_scaling, alert_filtering, capacity_planning, cost_optimization, security_monitoring"
            exit 1
            ;;
    esac
}

# Execute main function with all arguments
main "$@"

# SRE Career Progression
SRE_LEVELS = [
    "Junior Site Reliability Engineer",
    "Site Reliability Engineer",
    "Senior Site Reliability Engineer", 
    "Principal Site Reliability Engineer",
    "Distinguished Site Reliability Engineer",
    "SRE Architect"
]

# Monitoring Engineering Track
MONITORING_SPECIALIZATIONS = [
    "Observability Platform Engineering",
    "AIOps and Machine Learning Operations",
    "Security and Compliance Monitoring",
    "Cost Optimization and FinOps",
    "Distributed Systems Observability"
]

# Leadership Track
LEADERSHIP_PROGRESSION = [
    "Senior SRE → SRE Team Lead",
    "SRE Team Lead → SRE Manager", 
    "SRE Manager → Director of SRE",
    "Director of SRE → VP of Engineering"
]

# Example: Contributing to observability projects
prometheus_contributions:
  - "Improved memory efficiency in TSDB storage engine"
  - "Added support for native histograms in PromQL"
  - "Enhanced federation capabilities for multi-cluster deployments"

grafana_contributions:
  - "Developed custom panel plugin for SLO visualization" 
  - "Improved dashboard provisioning automation"
  - "Enhanced alerting rule management interface"

opentelemetry_contributions:
  - "Contributed to Go SDK instrumentation libraries"
  - "Improved trace sampling strategies"
  - "Enhanced OTLP collector processors"