# PACT Monitor CLI - Architecture This document describes the technical architecture, design patterns, and implementation details of the PACT Monitor CLI system for real-time monitoring, AI-powered threat detection, and compliance analysis. ## Table of Contents - [System Overview](#system-overview) - [Architecture Patterns](#architecture-patterns) - [Core Components](#core-components) - [AI Integration](#ai-integration) - [Real-time Monitoring](#real-time-monitoring) - [Threat Detection Engine](#threat-detection-engine) - [Vector Analysis System](#vector-analysis-system) - [Data Processing Pipeline](#data-processing-pipeline) - [Security Architecture](#security-architecture) - [Performance Optimization](#performance-optimization) - [Extensibility Framework](#extensibility-framework) ## System Overview PACT Monitor CLI is architected as a sophisticated real-time monitoring and AI-powered threat detection system, designed for compliance surveillance, anomaly detection, and behavioral analysis in blockchain and financial environments. ### High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────────────┐ │ PACT Monitor CLI │ ├─────────────────────────────────────────────────────────────────────┤ │ CLI Interface Layer (Commander.js) │ ├─────────────────────────────────────────────────────────────────────┤ │ Command Layer │ │ ┌─────────────┬─────────────┬─────────────┬─────────────────────┐ │ │ │ stream │ watch │ alert │ analyze │ │ │ │ export │ status │ hunt │ (AI-powered) │ │ │ └─────────────┴─────────────┴─────────────┴─────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ AI & Analytics Layer │ │ ┌─────────────┬─────────────┬─────────────┬─────────────────────┐ │ │ │ Anomaly │ Fraud Ring │ AI Report │ Vector Math │ │ │ │ Detector │ AI │ Generator │ Engine │ │ │ └─────────────┴─────────────┴─────────────┴─────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ Monitoring & Processing Layer │ │ ┌─────────────┬─────────────┬─────────────┬─────────────────────┐ │ │ │ File System │ Pattern │ Temporal │ Event │ │ │ │ Watcher │ Matcher │ Analysis │ Processor │ │ │ └─────────────┴─────────────┴─────────────┴─────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ Data Integration Layer │ │ ┌─────────────┬─────────────┬─────────────┬─────────────────────┐ │ │ │ Supabase │ Vector │ Event │ Configuration │ │ │ │ Connector │ Store │ Queue │ Manager │ │ │ └─────────────┴─────────────┴─────────────┴─────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ External Services │ │ ┌─────────────┬─────────────┬─────────────┬─────────────────────┐ │ │ │ PostgreSQL │ OpenAI │ Webhook │ Notification │ │ │ │ (Supabase) │ GPT-4 │ Services │ Services │ │ │ └─────────────┴─────────────┴─────────────┴─────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ``` ## Architecture Patterns ### 1. Event-Driven Architecture The system employs event-driven patterns for real-time monitoring and response: ```typescript interface MonitoringEvent { type: EventType; source: string; data: any; timestamp: string; metadata: Record; } class EventEmitter { private listeners = new Map(); emit(event: MonitoringEvent): void { const eventListeners = this.listeners.get(event.type) || []; eventListeners.forEach(listener => listener.handle(event)); } subscribe(eventType: string, listener: EventListener): void { if (!this.listeners.has(eventType)) { this.listeners.set(eventType, []); } this.listeners.get(eventType)!.push(listener); } } ``` ### 2. Strategy Pattern for Analysis Different analysis strategies for various threat types: ```typescript interface AnalysisStrategy { analyze(data: AnalysisData): Promise; } class SanctionsEvasionStrategy implements AnalysisStrategy { async analyze(data: AnalysisData): Promise { // Sanctions-specific analysis logic return this.detectSanctionsPatterns(data); } } class FraudPatternStrategy implements AnalysisStrategy { async analyze(data: AnalysisData): Promise { // Fraud-specific analysis logic return this.detectFraudPatterns(data); } } class ThreatAnalyzer { private strategies = new Map(); registerStrategy(type: string, strategy: AnalysisStrategy): void { this.strategies.set(type, strategy); } async analyze(type: string, data: AnalysisData): Promise { const strategy = this.strategies.get(type); if (!strategy) { throw new Error(`No strategy found for threat type: ${type}`); } return strategy.analyze(data); } } ``` ### 3. Observer Pattern for Monitoring File system monitoring with observer pattern: ```typescript interface FileSystemObserver { onFileChange(event: FileChangeEvent): void; onFileAdd(event: FileAddEvent): void; onFileDelete(event: FileDeleteEvent): void; } class FileSystemWatcher { private observers: FileSystemObserver[] = []; private watcher: FSWatcher; addObserver(observer: FileSystemObserver): void { this.observers.push(observer); } private notifyObservers(event: FileSystemEvent): void { this.observers.forEach(observer => { switch (event.type) { case 'change': observer.onFileChange(event); break; case 'add': observer.onFileAdd(event); break; case 'delete': observer.onFileDelete(event); break; } }); } } ``` ### 4. Pipeline Pattern for Data Processing Multi-stage data processing pipeline: ```typescript interface ProcessingStage { process(input: T): Promise; } class DataPipeline { private stages: ProcessingStage[] = []; addStage(stage: ProcessingStage): DataPipeline { this.stages.push(stage); return this as any; } async execute(input: T): Promise { let result = input; for (const stage of this.stages) { result = await stage.process(result); } return result; } } // Usage example const threatDetectionPipeline = new DataPipeline() .addStage(new DataNormalizationStage()) .addStage(new AnomalyDetectionStage()) .addStage(new ThreatClassificationStage()) .addStage(new RiskScoringStage()); ``` ## Core Components ### 1. CLI Interface Layer **Technology:** Commander.js with TypeScript **Responsibility:** Command parsing, validation, help generation, global configuration ```typescript // cli.ts const program = new Command(); program .name('pact-monitor') .description('PACT Monitor CLI - Real-time monitoring agent') .version(packageJson.version) .hook('preAction', async (thisCommand) => { // Initialize monitoring context await initializeMonitoringContext(thisCommand); }) .hook('postAction', async () => { // Cleanup monitoring resources await cleanupMonitoringResources(); }); ``` ### 2. Command Layer Each command implements specialized monitoring and analysis functionality: ```typescript // commands/stream.ts export const stream = { async action(options: StreamOptions): Promise { try { // 1. Initialize file system watcher const watcher = await initializeWatcher(options.watch); // 2. Set up event processing const eventProcessor = new EventProcessor(options); // 3. Configure auto-analysis if enabled if (options.autoAnalyze) { const analyzer = new RealTimeAnalyzer(); eventProcessor.addAnalyzer(analyzer); } // 4. Set up webhook notifications if (options.webhook) { const notifier = new WebhookNotifier(options.webhook); eventProcessor.addNotifier(notifier); } // 5. Start monitoring await watcher.start(); // 6. Handle graceful shutdown process.on('SIGINT', async () => { await watcher.stop(); await eventProcessor.shutdown(); }); } catch (error) { throw new MonitoringError(`Stream command failed: ${error.message}`); } } }; ``` ### 3. AI & Analytics Layer #### Anomaly Detection Engine Advanced statistical and machine learning anomaly detection: ```typescript class AnomalyDetector { private detectors: Map = new Map(); constructor() { this.detectors.set('spike', new SpikeDetector()); this.detectors.set('flatline', new FlatlineDetector()); this.detectors.set('vector-jump', new VectorJumpDetector()); this.detectors.set('pattern', new PatternDetector()); this.detectors.set('stealth', new StealthUsageDetector()); } async detectAnomalies(data: TimeSeries[]): Promise { const anomalies: Anomaly[] = []; for (const [type, detector] of this.detectors) { const detected = await detector.detect(data); anomalies.push(...detected.map(a => ({ ...a, type }))); } return this.consolidateAnomalies(anomalies); } private consolidateAnomalies(anomalies: Anomaly[]): Anomaly[] { // Remove duplicates and merge related anomalies const consolidated = new Map(); for (const anomaly of anomalies) { const key = `${anomaly.entity}-${anomaly.timestamp}`; if (consolidated.has(key)) { const existing = consolidated.get(key)!; existing.severity = Math.max(existing.severity, anomaly.severity); existing.evidence = { ...existing.evidence, ...anomaly.evidence }; } else { consolidated.set(key, anomaly); } } return Array.from(consolidated.values()); } } class SpikeDetector implements DetectionAlgorithm { async detect(data: TimeSeries[]): Promise { const anomalies: Anomaly[] = []; for (let i = 1; i < data.length; i++) { const current = data[i]; const previous = data.slice(Math.max(0, i - 10), i); if (previous.length < 3) continue; const mean = previous.reduce((sum, d) => sum + d.value, 0) / previous.length; const variance = previous.reduce((sum, d) => sum + Math.pow(d.value - mean, 2), 0) / previous.length; const stdDev = Math.sqrt(variance); const zScore = Math.abs((current.value - mean) / stdDev); if (zScore > 1.5) { // Configurable threshold anomalies.push({ entity: current.entity, timestamp: current.timestamp, severity: zScore > 3 ? 'high' : zScore > 2 ? 'medium' : 'low', evidence: { zScore, mean, stdDev, currentValue: current.value } }); } } return anomalies; } } ``` #### Fraud Ring Detection AI Network analysis and coordination pattern detection: ```typescript class FraudRingAI { async analyzeNetwork(entities: Entity[]): Promise { // 1. Build entity relationship graph const graph = await this.buildEntityGraph(entities); // 2. Detect clusters using community detection algorithms const clusters = await this.detectCommunities(graph); // 3. Analyze each cluster for fraud indicators const fraudRings: FraudRing[] = []; for (const cluster of clusters) { const analysis = await this.analyzeCluster(cluster); if (analysis.isFraudulent) { fraudRings.push(this.createFraudRing(cluster, analysis)); } } return fraudRings; } private async analyzeCluster(cluster: EntityCluster): Promise { const coordination = await this.calculateCoordinationScore(cluster); const infrastructure = await this.analyzeSharedInfrastructure(cluster); const temporal = await this.analyzeTemporalPatterns(cluster); return { coordinationScore: coordination.score, sharedInfrastructure: infrastructure, temporalPatterns: temporal, isFraudulent: coordination.score > 0.7 && infrastructure.overlap > 0.5 }; } private async calculateCoordinationScore(cluster: EntityCluster): Promise { const entities = cluster.entities; let totalSimilarity = 0; let comparisons = 0; for (let i = 0; i < entities.length; i++) { for (let j = i + 1; j < entities.length; j++) { const similarity = await this.calculateEntitySimilarity(entities[i], entities[j]); totalSimilarity += similarity; comparisons++; } } const avgSimilarity = comparisons > 0 ? totalSimilarity / comparisons : 0; return { score: avgSimilarity, evidence: { comparisons, avgSimilarity, highSimilarityPairs: comparisons > 0 ? Math.round(comparisons * 0.3) : 0 } }; } } ``` ### 4. Vector Analysis System Mathematical foundation for compliance analysis: ```typescript class VectorAnalysisSystem { calculateCosineSimilarity(vectorA: number[], vectorB: number[]): number { if (vectorA.length !== vectorB.length) { throw new Error('Vector dimensions must match'); } const dotProduct = vectorA.reduce((sum, val, i) => sum + val * vectorB[i], 0); const magnitudeA = Math.sqrt(vectorA.reduce((sum, val) => sum + val * val, 0)); const magnitudeB = Math.sqrt(vectorB.reduce((sum, val) => sum + val * val, 0)); if (magnitudeA === 0 || magnitudeB === 0) { return 0; } return dotProduct / (magnitudeA * magnitudeB); } calculateEuclideanDistance(vectorA: number[], vectorB: number[]): number { if (vectorA.length !== vectorB.length) { throw new Error('Vector dimensions must match'); } return Math.sqrt( vectorA.reduce((sum, val, i) => sum + Math.pow(val - vectorB[i], 2), 0) ); } calculateZScore(value: number, mean: number, stdDev: number): number { if (stdDev === 0) return 0; return (value - mean) / stdDev; } detectVectorJump(currentVector: number[], previousVector: number[], threshold: number = 0.3): boolean { const similarity = this.calculateCosineSimilarity(currentVector, previousVector); const shift = 1 - similarity; return shift > threshold; } analyzeVectorTrend(vectors: number[][]): VectorTrend { if (vectors.length < 2) { return { direction: 'stable', magnitude: 0, confidence: 0 }; } const similarities: number[] = []; for (let i = 1; i < vectors.length; i++) { const similarity = this.calculateCosineSimilarity(vectors[i-1], vectors[i]); similarities.push(similarity); } const avgSimilarity = similarities.reduce((sum, s) => sum + s, 0) / similarities.length; const trendMagnitude = Math.abs(1 - avgSimilarity); return { direction: avgSimilarity > 0.95 ? 'stable' : avgSimilarity > 0.85 ? 'gradual' : 'significant', magnitude: trendMagnitude, confidence: this.calculateTrendConfidence(similarities) }; } } ``` ## AI Integration ### 1. OpenAI Integration AI-powered threat analysis and report generation: ```typescript class AIReportGenerator { private client: OpenAI; constructor() { this.client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY }); } async generateThreatReport(threats: Threat[], entities: Entity[]): Promise { const prompt = this.buildAnalysisPrompt(threats, entities); const response = await this.client.chat.completions.create({ model: 'gpt-4', messages: [ { role: 'system', content: this.getSystemPrompt() }, { role: 'user', content: prompt } ], temperature: 0.3, max_tokens: 3000 }); return this.parseAIResponse(response.choices[0].message.content); } private getSystemPrompt(): string { return ` You are an expert compliance analyst specializing in blockchain and financial crime detection. Your task is to analyze threat data and provide actionable intelligence reports. Focus on: - Risk assessment and severity classification - Pattern identification and clustering - Temporal trend analysis - Actionable recommendations - Regulatory compliance guidance Provide structured analysis with clear sections: 1. Executive Summary 2. High-Risk Entities 3. Pattern Clusters 4. Temporal Shifts 5. Recommended Actions `; } private buildAnalysisPrompt(threats: Threat[], entities: Entity[]): string { return ` Analyze the following threat detection results: THREATS DETECTED: ${threats.length} ${threats.map(t => ` - Type: ${t.type} - Severity: ${t.severity} - Risk Score: ${t.riskScore} - Entities: ${t.entities.join(', ')} - Pattern: ${t.pattern} - Confidence: ${t.confidence} `).join('\n')} ENTITY DETAILS: ${entities.map(e => ` - ID: ${e.id} - Risk Score: ${e.riskScore} - Compliance Status: ${e.complianceStatus} - Recent Activity: ${e.recentActivity} `).join('\n')} Please provide a comprehensive threat analysis report with actionable recommendations. `; } } ``` ### 2. Machine Learning Pipeline Pattern recognition and behavioral analysis: ```typescript class MLPipeline { private featureExtractor: FeatureExtractor; private patternClassifier: PatternClassifier; private riskScorer: RiskScorer; constructor() { this.featureExtractor = new FeatureExtractor(); this.patternClassifier = new PatternClassifier(); this.riskScorer = new RiskScorer(); } async analyzeBehavioralPatterns(entityData: EntityData[]): Promise { // 1. Extract features from raw data const features = await this.featureExtractor.extract(entityData); // 2. Classify patterns const patterns = await this.patternClassifier.classify(features); // 3. Calculate risk scores const riskScores = await this.riskScorer.score(features, patterns); // 4. Generate insights const insights = await this.generateInsights(patterns, riskScores); return { patterns, riskScores, insights, confidence: this.calculateConfidence(features, patterns) }; } } class FeatureExtractor { async extract(entityData: EntityData[]): Promise { const features: Feature[] = []; for (const data of entityData) { // Transaction patterns const txFeatures = this.extractTransactionFeatures(data.transactions); // Temporal patterns const temporalFeatures = this.extractTemporalFeatures(data.timeline); // Network features const networkFeatures = this.extractNetworkFeatures(data.connections); features.push({ entityId: data.entityId, transaction: txFeatures, temporal: temporalFeatures, network: networkFeatures }); } return features; } private extractTransactionFeatures(transactions: Transaction[]): TransactionFeatures { return { volume: transactions.reduce((sum, tx) => sum + tx.amount, 0), frequency: transactions.length, avgAmount: transactions.length > 0 ? transactions.reduce((sum, tx) => sum + tx.amount, 0) / transactions.length : 0, uniqueCounterparties: new Set(transactions.map(tx => tx.counterparty)).size, timeSpread: this.calculateTimeSpread(transactions), velocityScore: this.calculateVelocityScore(transactions) }; } } ``` ## Real-time Monitoring ### 1. File System Monitoring Cross-platform file watching with chokidar: ```typescript class FileSystemMonitor { private watcher: chokidar.FSWatcher | null = null; private eventQueue: MonitoringEvent[] = []; private processors: EventProcessor[] = []; async startMonitoring(path: string, options: MonitoringOptions): Promise { this.watcher = chokidar.watch(path, { ignored: /(^|[\/\\])\../, // ignore dotfiles persistent: true, ignoreInitial: false, followSymlinks: true, cwd: '.', disableGlobbing: false, usePolling: false, interval: 100, binaryInterval: 300, alwaysStat: false, depth: 99, awaitWriteFinish: { stabilityThreshold: 2000, pollInterval: 100 } }); // Set up event handlers this.watcher .on('add', (path, stats) => this.handleFileEvent('ADD', path, stats)) .on('change', (path, stats) => this.handleFileEvent('CHANGE', path, stats)) .on('unlink', (path) => this.handleFileEvent('DELETE', path)) .on('error', (error) => this.handleError(error)); // Start event processing this.startEventProcessing(); } private handleFileEvent(type: EventType, path: string, stats?: fs.Stats): void { const event: MonitoringEvent = { id: generateEventId(), type, path, timestamp: new Date().toISOString(), size: stats?.size, metadata: { stats: stats ? this.extractFileStats(stats) : undefined } }; this.eventQueue.push(event); } private startEventProcessing(): void { setInterval(() => { if (this.eventQueue.length > 0) { const events = this.eventQueue.splice(0); this.processEvents(events); } }, 1000); // Process events every second } private async processEvents(events: MonitoringEvent[]): Promise { for (const processor of this.processors) { await processor.process(events); } } } ``` ### 2. Pattern-Based Watching Glob pattern matching with configurable actions: ```typescript class PatternWatcher { private patterns: Map = new Map(); private debounceTimers: Map = new Map(); addPattern(pattern: string, config: PatternConfig): void { this.patterns.set(pattern, config); } async watchPattern(pattern: string): Promise { const config = this.patterns.get(pattern); if (!config) { throw new Error(`Pattern not configured: ${pattern}`); } const watcher = chokidar.watch(pattern, config.watchOptions); watcher.on('all', (event, path) => { this.handlePatternEvent(event, path, config); }); } private handlePatternEvent(event: string, path: string, config: PatternConfig): void { // Implement debouncing const debounceKey = `${config.pattern}-${path}`; if (this.debounceTimers.has(debounceKey)) { clearTimeout(this.debounceTimers.get(debounceKey)!); } const timer = setTimeout(() => { this.executeAction(event, path, config); this.debounceTimers.delete(debounceKey); }, config.debounce || 1000); this.debounceTimers.set(debounceKey, timer); } private async executeAction(event: string, path: string, config: PatternConfig): Promise { switch (config.action) { case 'validate': await this.validateFile(path); break; case 'analyze': await this.analyzeFile(path); break; case 'alert': await this.triggerAlert(path, event); break; } } } ``` ## Threat Detection Engine ### 1. Multi-Stage Detection Pipeline Comprehensive threat detection with multiple analysis stages: ```typescript class ThreatDetectionEngine { private pipeline: DetectionPipeline; constructor() { this.pipeline = new DetectionPipeline() .addStage(new DataNormalizationStage()) .addStage(new AnomalyDetectionStage()) .addStage(new PatternAnalysisStage()) .addStage(new RiskScoringStage()) .addStage(new ThreatClassificationStage()) .addStage(new ConfidenceCalculationStage()); } async detectThreats(data: ThreatDetectionData): Promise { try { const result = await this.pipeline.execute(data); return this.formatThreatResults(result); } catch (error) { throw new ThreatDetectionError(`Detection failed: ${error.message}`); } } } class AnomalyDetectionStage implements ProcessingStage { private detector: AnomalyDetector; constructor() { this.detector = new AnomalyDetector(); } async process(input: NormalizedData): Promise { const anomalies = await this.detector.detectAnomalies(input.timeSeries); return { anomalies, statistics: { totalDataPoints: input.timeSeries.length, anomaliesDetected: anomalies.length, anomalyRate: anomalies.length / input.timeSeries.length } }; } } class ThreatClassificationStage implements ProcessingStage { private classifiers: Map = new Map(); constructor() { this.classifiers.set('sanctions-evasion', new SanctionsEvasionClassifier()); this.classifiers.set('fraud-patterns', new FraudPatternClassifier()); this.classifiers.set('mixer-detection', new MixerDetectionClassifier()); this.classifiers.set('compliance-drift', new ComplianceDriftClassifier()); } async process(input: RiskScoredData): Promise { const threats: ClassifiedThreat[] = []; for (const [type, classifier] of this.classifiers) { const classified = await classifier.classify(input); if (classified.isPositive) { threats.push({ type, confidence: classified.confidence, evidence: classified.evidence, riskScore: classified.riskScore }); } } return { threats, overallRiskScore: this.calculateOverallRisk(threats), classification: this.determineClassification(threats) }; } } ``` ### 2. Temporal Analysis Engine Time-based pattern analysis for threat evolution: ```typescript class TemporalAnalysisEngine { async analyzeTemporalPatterns(data: TemporalData[]): Promise { const patterns = await this.detectTemporalPatterns(data); const trends = await this.analyzeTrends(data); const shifts = await this.detectRegimeShifts(data); return { patterns, trends, shifts, insights: this.generateTemporalInsights(patterns, trends, shifts) }; } private async detectTemporalPatterns(data: TemporalData[]): Promise { const patterns: TemporalPattern[] = []; // Detect cyclical patterns const cyclical = await this.detectCyclicalPatterns(data); patterns.push(...cyclical); // Detect burst patterns const bursts = await this.detectBurstPatterns(data); patterns.push(...bursts); // Detect coordination patterns const coordination = await this.detectCoordinationPatterns(data); patterns.push(...coordination); return patterns; } private async detectCoordinationPatterns(data: TemporalData[]): Promise { const patterns: TemporalPattern[] = []; // Group data by entity const entityGroups = this.groupByEntity(data); // Analyze cross-entity temporal correlations for (const [entity1, data1] of entityGroups) { for (const [entity2, data2] of entityGroups) { if (entity1 >= entity2) continue; const correlation = this.calculateTemporalCorrelation(data1, data2); if (correlation.coefficient > 0.7) { patterns.push({ type: 'coordination', entities: [entity1, entity2], correlation: correlation.coefficient, timeWindow: correlation.timeWindow, confidence: correlation.significance }); } } } return patterns; } } ``` ## Data Processing Pipeline ### 1. Event Processing Architecture Scalable event processing with queue management: ```typescript class EventProcessingSystem { private queues: Map = new Map(); private processors: Map = new Map(); constructor() { this.initializeQueues(); this.initializeProcessors(); } private initializeQueues(): void { this.queues.set('high-priority', new PriorityEventQueue(1000)); this.queues.set('normal', new StandardEventQueue(5000)); this.queues.set('low-priority', new StandardEventQueue(10000)); } async processEvent(event: MonitoringEvent): Promise { const priority = this.determinePriority(event); const queue = this.queues.get(priority); if (!queue) { throw new Error(`Queue not found for priority: ${priority}`); } await queue.enqueue(event); } private determinePriority(event: MonitoringEvent): string { if (event.riskScore && event.riskScore > 0.8) { return 'high-priority'; } else if (event.riskScore && event.riskScore > 0.5) { return 'normal'; } else { return 'low-priority'; } } startProcessing(): void { for (const [priority, queue] of this.queues) { const processor = this.processors.get(priority); if (processor) { this.startQueueProcessor(queue, processor); } } } private startQueueProcessor(queue: EventQueue, processor: EventProcessor): void { setInterval(async () => { const events = await queue.dequeueMultiple(10); // Process in batches if (events.length > 0) { await processor.processBatch(events); } }, 1000); } } ``` ### 2. Data Transformation Pipeline Multi-stage data transformation and enrichment: ```typescript class DataTransformationPipeline { private transformers: DataTransformer[] = []; addTransformer(transformer: DataTransformer): void { this.transformers.push(transformer); } async transform(data: RawEventData): Promise { let result = data; for (const transformer of this.transformers) { result = await transformer.transform(result); } return result as ProcessedEventData; } } class DataEnrichmentTransformer implements DataTransformer { async transform(data: RawEventData): Promise { const enriched = { ...data }; // Add entity metadata if (data.entityId) { enriched.entityMetadata = await this.getEntityMetadata(data.entityId); } // Add risk context enriched.riskContext = await this.getRiskContext(data); // Add historical context enriched.historicalContext = await this.getHistoricalContext(data); return enriched; } private async getEntityMetadata(entityId: string): Promise { // Fetch entity metadata from database return this.entityService.getMetadata(entityId); } private async getRiskContext(data: RawEventData): Promise { // Calculate risk indicators return { baselineRisk: await this.calculateBaselineRisk(data), contextualRisk: await this.calculateContextualRisk(data), temporalRisk: await this.calculateTemporalRisk(data) }; } } ``` ## Security Architecture ### 1. Cryptographic Proof System Tamper-proof audit trails with cryptographic verification: ```typescript class CryptographicProofSystem { async generateProof(action: string, data: any): Promise { const timestamp = new Date().toISOString(); const nonce = crypto.randomBytes(16).toString('hex'); const payload = { action, data: this.sanitizeData(data), timestamp, nonce, version: '1.0' }; const dataHash = this.createHash(JSON.stringify(payload.data)); const metadataHash = this.createHash(`${action}:${timestamp}:${nonce}`); const combinedHash = this.createHash(dataHash + metadataHash); return { id: `pact-proof-${combinedHash.substring(0, 16)}`, payload, hashes: { data: dataHash, metadata: metadataHash, combined: combinedHash }, algorithm: 'SHA-256', generated: timestamp }; } async verifyProof(proof: CryptographicProof, originalData: any): Promise { try { // Recreate data hash const recreatedDataHash = this.createHash(JSON.stringify(originalData)); // Verify data integrity if (recreatedDataHash !== proof.hashes.data) { return false; } // Recreate metadata hash const recreatedMetadataHash = this.createHash( `${proof.payload.action}:${proof.payload.timestamp}:${proof.payload.nonce}` ); // Verify metadata integrity if (recreatedMetadataHash !== proof.hashes.metadata) { return false; } // Verify combined hash const recreatedCombinedHash = this.createHash( proof.hashes.data + proof.hashes.metadata ); return recreatedCombinedHash === proof.hashes.combined; } catch (error) { return false; } } private createHash(data: string): string { return crypto.createHash('sha256').update(data).digest('hex'); } private sanitizeData(data: any): any { // Remove sensitive information while preserving structure const sanitized = JSON.parse(JSON.stringify(data)); // Remove common sensitive fields this.removeSensitiveFields(sanitized, ['password', 'token', 'key', 'secret']); return sanitized; } } ``` ### 2. Access Control and Authorization Role-based access control for monitoring operations: ```typescript class AccessControlManager { private permissions: Map = new Map(); private roles: Map = new Map(); async authorize( userId: string, operation: string, resource: string ): Promise { const userRoles = await this.getUserRoles(userId); for (const role of userRoles) { const permissions = this.permissions.get(role.id) || []; for (const permission of permissions) { if (this.permissionMatches(permission, operation, resource)) { return true; } } } return false; } private permissionMatches( permission: Permission, operation: string, resource: string ): boolean { return ( (permission.operation === '*' || permission.operation === operation) && (permission.resource === '*' || permission.resource === resource) && this.isPermissionActive(permission) ); } private isPermissionActive(permission: Permission): boolean { const now = new Date(); return ( (!permission.expiresAt || permission.expiresAt > now) && permission.status === 'active' ); } } ``` ## Performance Optimization ### 1. Caching Strategy Multi-level caching for improved performance: ```typescript class CacheManager { private l1Cache: Map = new Map(); // In-memory private l2Cache: RedisCache; // Redis cache private l3Cache: DatabaseCache; // Persistent cache constructor() { this.l2Cache = new RedisCache(); this.l3Cache = new DatabaseCache(); } async get(key: string): Promise { // Try L1 cache first const l1Result = this.l1Cache.get(key); if (l1Result && !this.isExpired(l1Result)) { return l1Result.data; } // Try L2 cache const l2Result = await this.l2Cache.get(key); if (l2Result) { // Populate L1 cache this.l1Cache.set(key, { data: l2Result, expiresAt: Date.now() + (5 * 60 * 1000) // 5 minutes }); return l2Result; } // Try L3 cache const l3Result = await this.l3Cache.get(key); if (l3Result) { // Populate L2 and L1 caches await this.l2Cache.set(key, l3Result, 15 * 60); // 15 minutes this.l1Cache.set(key, { data: l3Result, expiresAt: Date.now() + (5 * 60 * 1000) }); return l3Result; } return null; } async set(key: string, data: T, ttl?: number): Promise { // Set in all cache levels const defaultTTL = ttl || 15 * 60; // 15 minutes default // L1 cache this.l1Cache.set(key, { data, expiresAt: Date.now() + (defaultTTL * 1000) }); // L2 cache await this.l2Cache.set(key, data, defaultTTL); // L3 cache (longer TTL) await this.l3Cache.set(key, data, defaultTTL * 4); } } ``` ### 2. Batch Processing Optimization Efficient batch processing for high-volume data: ```typescript class BatchProcessor { private batchSize: number; private maxWaitTime: number; private currentBatch: ProcessingItem[] = []; private batchTimer: NodeJS.Timeout | null = null; constructor(batchSize: number = 100, maxWaitTime: number = 5000) { this.batchSize = batchSize; this.maxWaitTime = maxWaitTime; } async addItem(item: ProcessingItem): Promise { this.currentBatch.push(item); // Process batch if size threshold reached if (this.currentBatch.length >= this.batchSize) { await this.processBatch(); } else if (!this.batchTimer) { // Set timer for maximum wait time this.batchTimer = setTimeout(() => { this.processBatch(); }, this.maxWaitTime); } } private async processBatch(): Promise { if (this.currentBatch.length === 0) return; const batch = this.currentBatch.splice(0); if (this.batchTimer) { clearTimeout(this.batchTimer); this.batchTimer = null; } try { await this.processItemsBatch(batch); } catch (error) { await this.handleBatchError(batch, error); } } private async processItemsBatch(items: ProcessingItem[]): Promise { // Group items by type for optimized processing const grouped = this.groupItemsByType(items); await Promise.all( Object.entries(grouped).map(([type, typeItems]) => this.processTypeGroup(type, typeItems) ) ); } } ``` ## Extensibility Framework ### 1. Plugin Architecture Extensible plugin system for custom monitoring capabilities: ```typescript interface MonitoringPlugin { name: string; version: string; initialize(context: PluginContext): Promise; commands?: CustomCommand[]; monitors?: CustomMonitor[]; analyzers?: CustomAnalyzer[]; cleanup?(): Promise; } class PluginManager { private plugins: Map = new Map(); private context: PluginContext; constructor(context: PluginContext) { this.context = context; } async loadPlugin(pluginPath: string): Promise { try { const plugin = await import(pluginPath); await this.registerPlugin(plugin.default); } catch (error) { throw new PluginLoadError(`Failed to load plugin: ${error.message}`); } } async registerPlugin(plugin: MonitoringPlugin): Promise { // Initialize plugin await plugin.initialize(this.context); // Register custom commands if (plugin.commands) { plugin.commands.forEach(cmd => this.context.commandRegistry.register(cmd) ); } // Register custom monitors if (plugin.monitors) { plugin.monitors.forEach(monitor => this.context.monitorRegistry.register(monitor) ); } // Register custom analyzers if (plugin.analyzers) { plugin.analyzers.forEach(analyzer => this.context.analyzerRegistry.register(analyzer) ); } this.plugins.set(plugin.name, plugin); } async unloadPlugin(name: string): Promise { const plugin = this.plugins.get(name); if (plugin && plugin.cleanup) { await plugin.cleanup(); } this.plugins.delete(name); } } ``` ### 2. Custom Analyzer Framework Framework for implementing custom analysis algorithms: ```typescript interface CustomAnalyzer { name: string; analyzerType: 'anomaly' | 'pattern' | 'risk' | 'temporal'; analyze(data: AnalysisInput): Promise; configure(config: AnalyzerConfig): void; } class AnalyzerFramework { private analyzers: Map = new Map(); registerAnalyzer(analyzer: CustomAnalyzer): void { this.analyzers.set(analyzer.name, analyzer); } async runAnalysis( analyzerName: string, data: AnalysisInput ): Promise { const analyzer = this.analyzers.get(analyzerName); if (!analyzer) { throw new Error(`Analyzer not found: ${analyzerName}`); } return analyzer.analyze(data); } async runAnalysisPipeline( analyzerNames: string[], data: AnalysisInput ): Promise { const results: AnalysisOutput[] = []; for (const name of analyzerNames) { const result = await this.runAnalysis(name, data); results.push(result); } return results; } } ``` --- This architecture documentation provides a comprehensive overview of the PACT Monitor CLI system design, enabling developers to understand, maintain, and extend the sophisticated monitoring and threat detection capabilities effectively.