mev-beta/docs/planning/11_LOW-003_Monitoring_Observability_Plan.md

LOW-003: Monitoring & Observability - Detailed Fix Plan

Issue ID: LOW-003
Category: Observability
Priority: Low
Status: Not Started
Generated: October 9, 2025
Estimate: 6-8 hours

Overview

This plan implements comprehensive monitoring and observability features including security event metrics, anomaly detection for unusual transaction patterns, security audit log analysis tools, and performance monitoring for security operations. The goal is to enhance visibility into system security and performance.

Current Implementation Issues

• Lack of security event metrics and dashboards
• No anomaly detection for unusual transaction patterns
• Missing security audit log analysis tools
• Absence of performance monitoring for security operations

Implementation Tasks

1. Add Security Event Metrics and Dashboards

Task ID: LOW-003.1
Time Estimate: 1.5 hours
Dependencies: None

Implement comprehensive security event metrics and visualization:

• Track security-relevant events (failed authentications, blocked transactions, etc.)
• Create Prometheus metrics for security events
• Design Grafana dashboards for security monitoring
• Implement alerting for security metric thresholds

import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promauto"
)

var (
    // Security-related metrics
    securityEvents = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Name: "security_events_total",
            Help: "Total number of security events by type",
        },
        []string{"event_type", "severity"},
    )
    
    rateLimitExceeded = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Name: "rate_limit_exceeded_total",
            Help: "Total number of rate limit exceeded events by endpoint",
        },
        []string{"endpoint"},
    )
    
    failedAuthentications = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Name: "failed_authentications_total",
            Help: "Total number of failed authentication attempts by source",
        },
        []string{"source", "reason"},
    )
    
    blockedTransactions = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Name: "blocked_transactions_total",
            Help: "Total number of blocked transactions by reason",
        },
        []string{"reason", "chain_id"},
    )
    
    securityOperationDuration = promauto.NewHistogramVec(
        prometheus.HistogramOpts{
            Name:    "security_operation_duration_seconds",
            Help:    "Duration of security operations",
            Buckets: prometheus.DefBuckets,
        },
        []string{"operation", "status"},
    )
)

// Example usage in security functions
func ValidateTransaction(tx *Transaction) error {
    start := time.Now()
    defer func() {
        duration := time.Since(start)
        securityOperationDuration.WithLabelValues("transaction_validation", "completed").Observe(duration.Seconds())
    }()
    
    // Validation logic here
    if err := validateNonce(tx); err != nil {
        blockedTransactions.WithLabelValues("invalid_nonce", tx.ChainId().String()).Inc()
        return err
    }
    
    if err := validateGasLimit(tx); err != nil {
        blockedTransactions.WithLabelValues("gas_limit_exceeded", tx.ChainId().String()).Inc()
        return err
    }
    
    return nil
}

// Example for rate limiting
func (rl *RateLimiter) Allow(key string) bool {
    start := time.Now()
    defer func() {
        duration := time.Since(start)
        securityOperationDuration.WithLabelValues("rate_limit_check", "completed").Observe(duration.Seconds())
    }()
    
    allowed := rl.impl.Allow(key)
    if !allowed {
        rateLimitExceeded.WithLabelValues(extractEndpoint(key)).Inc()
    }
    return allowed
}

2. Implement Anomaly Detection for Unusual Transaction Patterns

Task ID: LOW-003.2
Time Estimate: 2 hours
Dependencies: LOW-003.1

Create anomaly detection system for identifying unusual transaction patterns:

• Analyze transaction frequency, amounts, and patterns
• Implement statistical models for baseline behavior
• Detect potential MEV attacks or unusual activity
• Generate alerts for detected anomalies

import (
    "time"
    "math"
)

type AnomalyDetector struct {
    metrics       *MetricsClient
    alertSystem   *AlertSystem
    baselines     map[string]*BaselineProfile
    mu            sync.RWMutex
    windowSize    time.Duration  // Time window for pattern analysis
}

type BaselineProfile struct {
    avgTransactions float64  // Average transactions per window
    stdDev          float64  // Standard deviation
    recentValues    []float64 // Recent values for trend analysis
    lastUpdated     time.Time
}

type AnomalyEvent struct {
    Timestamp     time.Time     `json:"timestamp"`
    Type          string        `json:"type"`
    Severity      string        `json:"severity"`
    Description   string        `json:"description"`
    Context       interface{}   `json:"context"`
    Score         float64       `json:"score"`  // 0.0-1.0 anomaly score
}

func NewAnomalyDetector(metrics *MetricsClient, alertSystem *AlertSystem) *AnomalyDetector {
    return &AnomalyDetector{
        metrics:     metrics,
        alertSystem: alertSystem,
        baselines:   make(map[string]*BaselineProfile),
        windowSize:  1 * time.Hour,
    }
}

func (ad *AnomalyDetector) AnalyzeTransactionPattern(tx *Transaction, accountAddress string) {
    // Gather transaction statistics
    currentRate := ad.getCurrentTransactionRate(accountAddress)
    baseline, exists := ad.baselines[accountAddress]
    
    if !exists {
        ad.updateBaseline(accountAddress, currentRate)
        return
    }
    
    // Calculate z-score to determine anomaly level
    zScore := math.Abs(currentRate-baseline.avgTransactions) / baseline.stdDev
    
    // If z-score exceeds threshold, consider it an anomaly
    if zScore > 3.0 { // Using 3 standard deviations as threshold
        ad.reportAnomaly(&AnomalyEvent{
            Timestamp:   time.Now(),
            Type:        "transaction_rate_anomaly",
            Severity:    ad.getSeverity(zScore),
            Description: fmt.Sprintf("Unusual transaction rate detected: %.2fx baseline for account %s", 
                                     currentRate/baseline.avgTransactions, accountAddress),
            Context: map[string]interface{}{
                "account_address": accountAddress,
                "current_rate":    currentRate,
                "baseline_rate":   baseline.avgTransactions,
                "z_score":         zScore,
            },
            Score: zScore / 10.0, // Normalize to 0-1 scale
        })
    }
    
    // Update baseline for next analysis
    ad.updateBaseline(accountAddress, currentRate)
}

func (ad *AnomalyDetector) getCurrentTransactionRate(accountAddress string) float64 {
    // Query metrics to get transaction count in recent window
    // This would typically come from a metrics backend like Prometheus
    query := fmt.Sprintf(
        `increase(transaction_count_total{account="%s"}[1h])`, 
        accountAddress,
    )
    
    result, err := ad.metrics.Query(query)
    if err != nil {
        // Log error but don't fail the detection
        return 0
    }
    
    if len(result) > 0 {
        return result[0].Value
    }
    return 0
}

func (ad *AnomalyDetector) updateBaseline(accountAddress string, newValue float64) {
    ad.mu.Lock()
    defer ad.mu.Unlock()
    
    baseline, exists := ad.baselines[accountAddress]
    if !exists {
        // Initialize new baseline
        ad.baselines[accountAddress] = &BaselineProfile{
            avgTransactions: newValue,
            stdDev:          0,
            recentValues:    []float64{newValue},
            lastUpdated:     time.Now(),
        }
        return
    }
    
    // Update rolling average and standard deviation
    baseline.recentValues = append(baseline.recentValues, newValue)
    
    // Keep only last N values for rolling calculation
    maxHistory := 24 // last 24 hours worth of data
    if len(baseline.recentValues) > maxHistory {
        baseline.recentValues = baseline.recentValues[len(baseline.recentValues)-maxHistory:]
    }
    
    // Recalculate baseline statistics
    baseline.avgTransactions = ad.calculateMean(baseline.recentValues)
    baseline.stdDev = ad.calculateStdDev(baseline.recentValues, baseline.avgTransactions)
    baseline.lastUpdated = time.Now()
}

func (ad *AnomalyDetector) calculateMean(values []float64) float64 {
    if len(values) == 0 {
        return 0
    }
    
    sum := 0.0
    for _, v := range values {
        sum += v
    }
    return sum / float64(len(values))
}

func (ad *AnomalyDetector) calculateStdDev(values []float64, mean float64) float64 {
    if len(values) <= 1 {
        return 0
    }
    
    sum := 0.0
    for _, v := range values {
        diff := v - mean
        sum += diff * diff
    }
    variance := sum / float64(len(values)-1)
    return math.Sqrt(variance)
}

func (ad *AnomalyDetector) reportAnomaly(event *AnomalyEvent) {
    // Log the anomaly
    log.WithFields(log.Fields{
        "timestamp": event.Timestamp,
        "type":      event.Type,
        "severity":  event.Severity,
        "score":     event.Score,
    }).Warn("Anomaly detected: " + event.Description)
    
    // Send to metrics system
    anomalyScore.WithLabelValues(event.Type, event.Severity).Set(event.Score)
    
    // Trigger alert if severity is high enough
    if ad.shouldAlert(event.Severity) {
        ad.alertSystem.SendAlert("Security Anomaly Detected", map[string]interface{}{
            "event": event,
        })
    }
}

func (ad *AnomalyDetector) getSeverity(score float64) string {
    switch {
    case score > 5.0:
        return "critical"
    case score > 3.0:
        return "high"
    case score > 2.0:
        return "medium"
    default:
        return "low"
    }
}

func (ad *AnomalyDetector) shouldAlert(severity string) bool {
    return severity == "critical" || severity == "high"
}

3. Create Security Audit Log Analysis Tools

Task ID: LOW-003.3
Time Estimate: 1.5 hours
Dependencies: LOW-003.1

Develop tools for analyzing security audit logs:

• Create parsers for security-relevant log entries
• Implement aggregation and analysis functions
• Build summary reports for security events
• Create search and filtering capabilities

import (
    "encoding/json"
    "time"
    "strings"
    "regexp"
)

type SecurityAuditLogAnalyzer struct {
    logParser  *LogParser
    storage    StorageBackend
}

type SecurityEvent struct {
    Timestamp   time.Time              `json:"timestamp"`
    Level       string                 `json:"level"`
    Message     string                 `json:"message"`
    Fields      map[string]interface{} `json:"fields"`
    Source      string                 `json:"source"`
    Category    string                 `json:"category"`
}

type SecurityReport struct {
    PeriodStart    time.Time           `json:"period_start"`
    PeriodEnd      time.Time           `json:"period_end"`
    TotalEvents    int                 `json:"total_events"`
    CriticalEvents int                 `json:"critical_events"`
    ByCategory     map[string]int      `json:"by_category"`
    BySeverity     map[string]int      `json:"by_severity"`
    Anomalies      []AnomalySummary    `json:"anomalies"`
}

type AnomalySummary struct {
    Type        string    `json:"type"`
    Count       int       `json:"count"`
    FirstSeen   time.Time `json:"first_seen"`
    LastSeen    time.Time `json:"last_seen"`
    Description string    `json:"description"`
}

func NewSecurityAuditLogAnalyzer(storage StorageBackend) *SecurityAuditLogAnalyzer {
    return &SecurityAuditLogAnalyzer{
        logParser: NewLogParser(),
        storage:   storage,
    }
}

func (sala *SecurityAuditLogAnalyzer) ParseSecurityEvents(logFile string, startDate, endDate time.Time) ([]*SecurityEvent, error) {
    var events []*SecurityEvent
    
    logEntries, err := sala.logParser.ParseLogFile(logFile)
    if err != nil {
        return nil, fmt.Errorf("failed to parse log file: %w", err)
    }
    
    for _, entry := range logEntries {
        // Filter by date range
        if entry.Timestamp.Before(startDate) || entry.Timestamp.After(endDate) {
            continue
        }
        
        // Check if this is a security-relevant event
        if sala.isSecurityEvent(entry) {
            event := sala.createSecurityEvent(entry)
            events = append(events, event)
        }
    }
    
    // Sort by timestamp
    sort.Slice(events, func(i, j int) bool {
        return events[i].Timestamp.Before(events[j].Timestamp)
    })
    
    return events, nil
}

func (sala *SecurityAuditLogAnalyzer) isSecurityEvent(logEntry *LogEntry) bool {
    // Define patterns for security-relevant messages
    securityKeywords := []string{
        "authentication", "authorization", "blocked", "failed", "denied",
        "unauthorized", "malicious", "attack", "intrusion", "breach",
        "validation", "signature", "key", "transaction", "nonce",
    }
    
    message := strings.ToLower(logEntry.Message)
    for _, keyword := range securityKeywords {
        if strings.Contains(message, keyword) {
            return true
        }
    }
    
    // Check for security-related fields
    securityFields := []string{"error", "status", "outcome", "result"}
    for field := range logEntry.Fields {
        for _, secField := range securityFields {
            if strings.Contains(strings.ToLower(field), secField) {
                return true
            }
        }
    }
    
    return false
}

func (sala *SecurityAuditLogAnalyzer) createSecurityEvent(logEntry *LogEntry) *SecurityEvent {
    // Categorize the event based on message content
    category := sala.categorizeSecurityEvent(logEntry)
    
    return &SecurityEvent{
        Timestamp: logEntry.Timestamp,
        Level:     logEntry.Level,
        Message:   logEntry.Message,
        Fields:    logEntry.Fields,
        Source:    logEntry.Source,
        Category:  category,
    }
}

func (sala *SecurityAuditLogAnalyzer) categorizeSecurityEvent(logEntry *LogEntry) string {
    message := strings.ToLower(logEntry.Message)
    
    // Define category patterns
    categories := map[string]*regexp.Regexp{
        "authentication": regexp.MustCompile(`(?i)(auth|login|logout|session|token|credential|password)`),
        "authorization":  regexp.MustCompile(`(?i)(permission|access|allow|deny|forbidden|unauthorized|privilege)`),
        "validation":     regexp.MustCompile(`(?i)(validate|validation|error|invalid|malformed|check|verify)`),
        "transaction":    regexp.MustCompile(`(?i)(transaction|block|revert|fail|error|nonce|gas|contract|call)`),
        "network":        regexp.MustCompile(`(?i)(connection|ip|port|network|request|response|timeout)`),
        "crypto":         regexp.MustCompile(`(?i)(signature|sign|verify|key|private|public|crypto|hash|encrypt|decrypt)`),
    }
    
    for category, pattern := range categories {
        if pattern.MatchString(message) {
            return category
        }
    }
    
    // If no specific category matches, classify as general security
    return "general"
}

func (sala *SecurityAuditLogAnalyzer) GenerateSecurityReport(startDate, endDate time.Time) (*SecurityReport, error) {
    events, err := sala.ParseSecurityEvents("security.log", startDate, endDate)
    if err != nil {
        return nil, fmt.Errorf("failed to parse events for report: %w", err)
    }
    
    report := &SecurityReport{
        PeriodStart: startDate,
        PeriodEnd:   endDate,
        ByCategory:  make(map[string]int),
        BySeverity:  make(map[string]int),
    }
    
    for _, event := range events {
        report.TotalEvents++
        
        // Count by category
        report.ByCategory[event.Category]++
        
        // Count by severity
        severity := sala.eventSeverity(event)
        report.BySeverity[severity]++
        
        // Count critical events specifically
        if severity == "critical" || severity == "high" {
            report.CriticalEvents++
        }
    }
    
    // Generate anomaly summaries
    report.Anomalies = sala.generateAnomalySummaries(events)
    
    return report, nil
}

func (sala *SecurityAuditLogAnalyzer) eventSeverity(event *SecurityEvent) string {
    // Determine severity based on log level and content
    level := strings.ToLower(event.Level)
    
    switch level {
    case "error", "critical", "fatal":
        return "high"
    case "warn", "warning":
        return "medium"
    case "info":
        // Check message content for severity indicators
        msg := strings.ToLower(event.Message)
        if strings.Contains(msg, "blocked") || strings.Contains(msg, "denied") {
            return "low"
        }
        return "info"
    default:
        return "info"
    }
}

func (sala *SecurityAuditLogAnalyzer) generateAnomalySummaries(events []*SecurityEvent) []AnomalySummary {
    // Group events by type and summarize
    eventCounts := make(map[string]*AnomalySummary)
    
    for _, event := range events {
        key := event.Category // Use category as the primary grouping type
        
        if summary, exists := eventCounts[key]; exists {
            summary.Count++
            if event.Timestamp.After(summary.LastSeen) {
                summary.LastSeen = event.Timestamp
            }
        } else {
            eventCounts[key] = &AnomalySummary{
                Type:      key,
                Count:     1,
                FirstSeen: event.Timestamp,
                LastSeen:  event.Timestamp,
                Description: fmt.Sprintf("Security events in category: %s", key),
            }
        }
    }
    
    // Convert map to slice and sort by count
    var summaries []AnomalySummary
    for _, summary := range eventCounts {
        summaries = append(summaries, *summary)
    }
    
    // Sort by count descending
    sort.Slice(summaries, func(i, j int) bool {
        return summaries[i].Count > summaries[j].Count
    })
    
    return summaries
}

4. Add Performance Monitoring for Security Operations

Task ID: LOW-003.4
Time Estimate: 1 hour
Dependencies: LOW-003.1, LOW-003.2, LOW-003.3

Implement monitoring for security operation performance:

• Track execution time of security-critical functions
• Monitor resource usage during security operations
• Alert on performance degradation of security features
• Create dashboards showing security operation performance

import (
    "context"
    "time"
)

type SecurityPerformanceMonitor struct {
    metrics      *MetricsClient
    alertSystem  *AlertSystem
    thresholds   PerformanceThresholds
}

type PerformanceThresholds struct {
    MaxValidationTime     time.Duration  // Max time for transaction validation
    MaxSignatureTime      time.Duration  // Max time for signature verification
    MaxRateLimitTime      time.Duration  // Max time for rate limiting check
    MaxEncryptionTime     time.Duration  // Max time for encryption operations
}

func NewSecurityPerformanceMonitor(metrics *MetricsClient, alertSystem *AlertSystem) *SecurityPerformanceMonitor {
    return &SecurityPerformanceMonitor{
        metrics:     metrics,
        alertSystem: alertSystem,
        thresholds: PerformanceThresholds{
            MaxValidationTime: 50 * time.Millisecond,
            MaxSignatureTime:  100 * time.Millisecond,
            MaxRateLimitTime:  10 * time.Millisecond,
            MaxEncryptionTime: 50 * time.Millisecond,
        },
    }
}

// Monitored transaction validation function
func (spm *SecurityPerformanceMonitor) ValidateTransactionWithMonitoring(ctx context.Context, tx *Transaction) error {
    start := time.Now()
    
    // Create a context with timeout for this operation
    ctx, cancel := context.WithTimeout(ctx, spm.thresholds.MaxValidationTime*2)
    defer cancel()
    
    err := spm.validateTransactionInternal(ctx, tx)
    
    duration := time.Since(start)
    
    // Record metric
    securityOperationDuration.WithLabelValues("transaction_validation", getStatusLabel(err)).Observe(duration.Seconds())
    
    // Check if operation took too long
    if duration > spm.thresholds.MaxValidationTime {
        spm.recordPerformanceViolation("transaction_validation", duration, spm.thresholds.MaxValidationTime)
    }
    
    return err
}

func (spm *SecurityPerformanceMonitor) validateTransactionInternal(ctx context.Context, tx *Transaction) error {
    // Run validation in a goroutine to allow timeout
    resultChan := make(chan error, 1)
    
    go func() {
        defer close(resultChan)
        
        // Actual validation logic here
        if err := validateNonce(tx); err != nil {
            resultChan <- err
            return
        }
        
        if err := validateGasLimit(tx); err != nil {
            resultChan <- err
            return
        }
        
        if err := validateSignature(tx); err != nil {
            resultChan <- err
            return
        }
        
        resultChan <- nil
    }()
    
    select {
    case result := <-resultChan:
        return result
    case <-ctx.Done():
        securityOperationDuration.WithLabelValues("transaction_validation", "timeout").Observe(
            spm.thresholds.MaxValidationTime.Seconds())
        return fmt.Errorf("transaction validation timed out: %w", ctx.Err())
    }
}

// Monitored signature verification
func (spm *SecurityPerformanceMonitor) VerifySignatureWithMonitoring(ctx context.Context, tx *Transaction) (bool, error) {
    start := time.Now()
    
    ctx, cancel := context.WithTimeout(ctx, spm.thresholds.MaxSignatureTime*2)
    defer cancel()
    
    valid, err := spm.verifySignatureInternal(ctx, tx)
    
    duration := time.Since(start)
    
    // Record metric
    status := "success"
    if err != nil {
        status = "error"
    } else if !valid {
        status = "invalid_signature"
    }
    
    securityOperationDuration.WithLabelValues("signature_verification", status).Observe(duration.Seconds())
    
    // Check if operation took too long
    if duration > spm.thresholds.MaxSignatureTime {
        spm.recordPerformanceViolation("signature_verification", duration, spm.thresholds.MaxSignatureTime)
    }
    
    return valid, err
}

func (spm *SecurityPerformanceMonitor) recordPerformanceViolation(operation string, actual, threshold time.Duration) {
    // Log performance violation
    log.WithFields(log.Fields{
        "operation": operation,
        "actual":    actual.Seconds(),
        "threshold": threshold.Seconds(),
    }).Warn("Security operation performance threshold exceeded")
    
    // Increment violation counter
    performanceViolations.WithLabelValues(operation).Inc()
    
    // Send alert if this is significantly above threshold
    if actual > threshold*2 {
        spm.alertSystem.SendAlert("Security Performance Degradation", map[string]interface{}{
            "operation": operation,
            "actual":    actual.Seconds(),
            "threshold": threshold.Seconds(),
            "exceeded_by": actual.Seconds() - threshold.Seconds(),
        })
    }
}

// Helper function to get status label for metrics
func getStatusLabel(err error) string {
    if err != nil {
        return "error"
    }
    return "success"
}

// Performance monitoring middleware for HTTP endpoints
func (spm *SecurityPerformanceMonitor) SecurityMiddleware(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        start := time.Now()
        
        // Capture response to get status code
        wrapped := &responseWriter{ResponseWriter: w, statusCode: 200}
        
        // Call the next handler
        next.ServeHTTP(wrapped, r)
        
        duration := time.Since(start)
        
        // Record metrics for security endpoints
        if isSecurityEndpoint(r.URL.Path) {
            securityEndpointDuration.WithLabelValues(
                r.URL.Path, 
                fmt.Sprintf("%d", wrapped.statusCode),
                r.Method,
            ).Observe(duration.Seconds())
            
            // Check threshold for security endpoints
            if duration > spm.thresholds.MaxRateLimitTime*10 { // 10x threshold for endpoints
                spm.recordPerformanceViolation(
                    fmt.Sprintf("http_%s_%s", r.Method, r.URL.Path), 
                    duration, 
                    spm.thresholds.MaxRateLimitTime*10,
                )
            }
        }
    })
}

func isSecurityEndpoint(path string) bool {
    securityPaths := []string{
        "/auth", "/login", "/logout",
        "/transaction", "/sign", "/validate",
        "/security", "/admin",
    }
    
    for _, secPath := range securityPaths {
        if strings.HasPrefix(path, secPath) {
            return true
        }
    }
    return false
}

// Response writer wrapper to capture status code
type responseWriter struct {
    http.ResponseWriter
    statusCode int
}

func (rw *responseWriter) WriteHeader(code int) {
    rw.statusCode = code
    rw.ResponseWriter.WriteHeader(code)
}

Implementation Integration

Integration with Existing Components

// Initialize monitoring in main application
func InitializeSecurityMonitoring() error {
    // Initialize metrics client
    metricsClient := initMetricsClient()
    
    // Initialize alert system
    alertSystem := initAlertSystem()
    
    // Create security performance monitor
    perfMonitor := NewSecurityPerformanceMonitor(metricsClient, alertSystem)
    
    // Create anomaly detector
    anomalyDetector := NewAnomalyDetector(metricsClient, alertSystem)
    
    // Create audit log analyzer
    auditAnalyzer := NewSecurityAuditLogAnalyzer(nil) // Use appropriate storage backend
    
    // Store in global context or pass to services that need monitoring
    globalSecurityMonitor = &SecurityMonitor{
        Performance: perfMonitor,
        Anomaly:     anomalyDetector,
        Audit:       auditAnalyzer,
    }
    
    return nil
}

Testing Strategy

• Unit tests for each monitoring component
• Integration tests for metrics collection
• Load testing to verify monitoring doesn't impact performance
• Test alerting functionality with mock systems

Code Review Checklist

• Security event metrics properly implemented and labeled
• Anomaly detection algorithms are appropriate for the data
• Audit log analysis tools handle edge cases properly
• Performance monitoring doesn't impact system performance
• Alerting thresholds are reasonable
• Metrics are properly exported to monitoring system
• Tests cover monitoring functionality

Rollback Strategy

If monitoring implementation causes issues:

1. Disable new monitoring components via configuration
2. Remove new metrics collection temporarily
3. Investigate and fix performance impacts

Success Metrics

• Security event metrics available in monitoring dashboard
• Anomaly detection identifies actual unusual patterns
• Audit log analysis tools provide actionable insights
• Performance monitoring shows no degradation
• Alert system properly notifies of security events
• All new monitoring tests pass consistently