# PPRE: Predictive Risk Engine for Blockchain Security Analysis ## Technical Implementation Whitepaper **Version:** 1.0 **Date:** July 2025 **Authors:** PACT Protocol Technical Team **Contact:** [technical@pactprotocol.io](mailto:technical@pactprotocol.io) --- ## Abstract The Predictive Risk Engine (PPRE) is an advanced command-line interface tool that leverages artificial intelligence and machine learning to provide enterprise-grade predictive risk analysis for blockchain addresses, transactions, and smart contract interactions. Built with TypeScript and Node.js, PPRE integrates multiple AI model providers, vector databases, and real-time streaming capabilities to deliver comprehensive risk assessment and monitoring solutions. This technical whitepaper provides detailed documentation of PPRE's architecture, implementation, and capabilities within the PACT Protocol ecosystem. ## Table of Contents 1. [Introduction](#introduction) 2. [Technical Architecture](#technical-architecture) 3. [Core Commands and Features](#core-commands-and-features) 4. [AI-Powered Risk Analysis Engine](#ai-powered-risk-analysis-engine) 5. [Real-time Streaming and Monitoring](#real-time-streaming-and-monitoring) 6. [Machine Learning and Fine-tuning](#machine-learning-and-fine-tuning) 7. [Memory Systems and Vector Databases](#memory-systems-and-vector-databases) 8. [SLA Policy Enforcement](#sla-policy-enforcement) 9. [Configuration Management](#configuration-management) 10. [Signal Processing and Data Flow](#signal-processing-and-data-flow) 11. [Performance and Scalability](#performance-and-scalability) 12. [Security Considerations](#security-considerations) 13. [Integration and Deployment](#integration-and-deployment) 14. [Future Development](#future-development) 15. [Conclusion](#conclusion) --- ## 1. Introduction PPRE represents a paradigm shift in blockchain security analysis, moving from reactive security measures to predictive risk assessment. Unlike traditional security tools that analyze known vulnerabilities after they occur, PPRE uses machine learning and AI to predict potential security risks before they materialize. ### 1.1 Key Design Principles **Predictive Intelligence:** Uses historical data and behavioral patterns to forecast security risks with quantifiable confidence scores. **Real-time Monitoring:** Continuous surveillance of blockchain activities with immediate risk assessment and alerting. **Adaptive Learning:** Local machine learning capabilities that improve prediction accuracy through fine-tuning with domain-specific data. **Multi-Modal Analysis:** Combines on-chain data, off-chain signals, and external intelligence sources for comprehensive risk assessment. **Enterprise Scalability:** Designed for high-volume processing with configurable SLA policies and compliance frameworks. ### 1.2 Technology Stack - **Language:** TypeScript with strict typing and ES2022 target - **Runtime:** Node.js 18+ with native ESM modules - **CLI Framework:** Commander.js v12+ for professional command structure - **AI Integration:** OpenRouter API with GPT-4 and multiple model providers - **Vector Databases:** Weaviate and Supabase integration for similarity search - **Machine Learning:** Local logistic regression and naive Bayes implementation - **Streaming:** Chokidar-based file watching with debounced processing - **Data Processing:** CSV parsing, JSON handling, and real-time data transformation ## 2. Technical Architecture ### 2.1 System Overview PPRE follows a modular, event-driven architecture with clear separation of concerns: ``` ┌─────────────────────────────────────────────────────────────┐ │ CLI Interface Layer │ ├─────────────────────────────────────────────────────────────┤ │ predict │ stream │ fine-tune │ simulate │ timeline │ sla │ ├─────────────────────────────────────────────────────────────┤ │ Command Processing Layer │ ├─────────────────────────────────────────────────────────────┤ │ AI Models │ Vector DB │ ML Training │ SLA │ │ (OpenRouter) │ (Weaviate) │ (Local) │ Engine │ ├─────────────────────────────────────────────────────────────┤ │ Signal Processing Layer │ ├─────────────────────────────────────────────────────────────┤ │ Event Stream │ Memory │ Timeline │ Config │ │ Processing │ Management │ Analytics │ Mgmt │ ├─────────────────────────────────────────────────────────────┤ │ Storage Layer │ └─────────────────────────────────────────────────────────────┘ ``` ### 2.2 Core Components **CLI Application (`src/cli.ts`):** ```typescript #!/usr/bin/env node import { Command } from 'commander'; import { printBanner } from './utils/banner.js'; import { loadConfig } from './utils/config.js'; const program = new Command(); program .name('ppre') .description('PACT Protocol Predictive Risk Engine CLI') .version('1.0.0'); // Load configuration and initialize commands const config = loadConfig(); await initializeCommands(program, config); ``` **Command Structure:** - **predict**: AI-powered risk score analysis for entities - **stream**: Real-time monitoring of signal files with automatic prediction - **fine-tune**: Local machine learning model training and optimization - **simulate**: Risk scenario simulation and testing - **timeline**: Historical risk analysis and trend visualization - **sla-check**: SLA policy compliance validation - **stream-memory**: Memory-enhanced streaming with vector similarity - **trigger**: Manual risk event triggering for testing - **status**: System status and configuration validation ### 2.3 Module Organization ``` src/ ├── commands/ # CLI command implementations │ ├── predict.ts # Risk prediction analysis │ ├── stream.ts # Real-time monitoring │ ├── fine-tune.ts # ML model training │ ├── simulate.ts # Risk simulation │ ├── timeline.ts # Historical analysis │ ├── sla-check.ts # SLA compliance │ ├── stream-memory.ts # Memory-enhanced streaming │ ├── trigger.ts # Manual triggering │ └── status.ts # System status ├── utils/ # Core utilities │ ├── prediction.ts # Risk prediction engine │ ├── ai.ts # AI model integration │ ├── memory.ts # Vector memory systems │ ├── ml-training.ts # Local ML training │ ├── sla.ts # SLA policy engine │ ├── timeline.ts # Timeline analytics │ ├── config.ts # Configuration management │ └── types.ts # TypeScript definitions ├── stream/ # Streaming components │ ├── memory-stream.ts # Memory streaming │ ├── memory-watcher.ts # File watching │ └── backup-manager.ts # Backup management └── models/ # AI model providers ├── openrouter.ts # OpenRouter integration ├── local-trained.ts # Local ML models └── stubbed.ts # Development/testing ``` ## 3. Core Commands and Features ### 3.1 Predict Command The predict command is the primary risk analysis feature that processes signal data and generates risk scores: **Basic Usage:** ```bash ppre predict --input signals.json --entity wallet123 ppre predict --input batch-signals/ --log 10 ppre predict --entity user456 --noMemory --overlay ``` **Implementation Overview:** ```typescript export interface PredictCommandOptions extends CommandOptions { input?: string; entity?: string; print?: boolean; log?: number | string; noMemory?: boolean; overlay?: boolean; } export async function createPredictCommand(): Promise { const predictCommand = new Command('predict'); predictCommand .description('Run AI-powered risk prediction analysis on signal data') .option('-i, --input ', 'Input signal file or directory') .option('-e, --entity ', 'Entity identifier for prediction') .option('-p, --print', 'Print results to console') .option('-l, --log ', 'Log recent prediction results') .option('--no-memory', 'Disable memory-enhanced predictions') .option('--overlay', 'Overlay memory context in results') .action(async (options: PredictCommandOptions) => { await handlePredictCommand(options); }); } ``` **Prediction Process Flow:** 1. **Signal Validation**: Verify input signal structure and data types 2. **Memory Context**: Retrieve similar historical patterns (unless disabled) 3. **AI Analysis**: Process signals through configured AI model provider 4. **Risk Scoring**: Generate risk score with confidence assessment 5. **SLA Validation**: Check against configured SLA policies 6. **Result Output**: Format and display prediction results ### 3.2 Stream Command Real-time monitoring system that watches for new signal files and automatically processes them: **Stream Configuration:** ```typescript export interface StreamCommandOptions extends CommandOptions { watch?: string; debounce?: number; autoTrigger?: boolean; entityField?: string; dryRun?: boolean; } export interface StreamEvent { timestamp: string; type: 'file_added' | 'prediction' | 'trigger' | 'error'; file?: string; entity?: string; riskScore?: number; action: string; duration?: number; error?: string; metadata?: Record; } ``` **Stream Processing Pipeline:** ```typescript export class StreamWatcher { private watcher: FSWatcher | null = null; private debouncedProcessor: ((...args: any[]) => void) | null = null; async start(watchPath: string, options: StreamOptions): Promise { this.watcher = chokidar.watch(watchPath, { ignored: /^\./, // Ignore hidden files persistent: true, ignoreInitial: false }); this.debouncedProcessor = debounce( this.processFile.bind(this), options.debounce || 2000 ); this.watcher .on('add', this.debouncedProcessor) .on('change', this.debouncedProcessor) .on('error', this.handleError.bind(this)); } } ``` ### 3.3 Fine-tune Command Local machine learning model training for domain-specific risk prediction: **Training Data Format:** ```json {"signals": {"repo_pushes": 5, "audit_flags": 2, "login_failures": 3}, "label": "compliant"} {"signals": {"repo_pushes": 25, "audit_flags": 8, "login_failures": 12}, "label": "violation"} ``` **Training Implementation:** ```typescript export interface FineTuneCommandOptions { input: string; output?: string; algorithm?: 'logistic' | 'naive-bayes'; preview?: boolean; validationSplit?: number; epochs?: number; learningRate?: number; testRun?: boolean; verbose?: boolean; } export class MLTrainer { async train(data: TrainingData[], options: FineTuneOptions): Promise { const { features, labels, scalingParams } = this.preprocessData(data); if (options.algorithm === 'logistic') { return this.trainLogisticRegression(features, labels, options); } else { return this.trainNaiveBayes(features, labels, options); } } private trainLogisticRegression( features: number[][], labels: number[], options: FineTuneOptions ): TrainedModel { // Implement logistic regression with gradient descent const weights = new Array(features[0].length).fill(0); let bias = 0; for (let epoch = 0; epoch < options.epochs; epoch++) { // Training loop implementation const { newWeights, newBias } = this.gradientDescentStep( features, labels, weights, bias, options.learningRate ); weights.splice(0, weights.length, ...newWeights); bias = newBias; } return this.createTrainedModel(weights, bias, features, labels); } } ``` ### 3.4 Memory-Enhanced Predictions Vector similarity search for historical context integration: **Memory Streaming:** ```typescript export interface MemoryStreamOptions extends CommandOptions { watch?: string; debounce?: number; debug?: boolean; once?: boolean; } export class MemoryStream { private vectorDB: VectorDBProvider; private backupManager: BackupManager; async processSignalFile(filePath: string): Promise { const signals = await this.loadSignals(filePath); const entityId = this.extractEntityId(filePath); // Store in vector database await this.vectorDB.storeVector(entityId, signals); // Create backup await this.backupManager.createBackup(entityId, signals); // Update statistics this.updateStats('filesProcessed'); } async getSimilarHistoricalData( entityId: string, signals: Record, topK: number = 5 ): Promise { return this.vectorDB.querySimilarVectors(entityId, signals, topK); } } ``` ## 4. AI-Powered Risk Analysis Engine ### 4.1 Multi-Provider Architecture PPRE supports multiple AI model providers with automatic fallback capabilities: **Provider Interface:** ```typescript export interface AIModelProvider { name: string; predict(signals: RiskSignals, config: PPREConfig): Promise; } export class PredictionEngine { private providers: Map = new Map(); constructor() { this.providers.set('openrouter', new OpenRouterProvider()); this.providers.set('local-trained', new LocalTrainedProvider()); this.providers.set('stubbed', new StubbedProvider()); } async runPrediction( inputData: RiskSignals, config: PPREConfig, entity?: string ): Promise { const provider = this.getProvider(config.modelProvider); const result = await provider.predict(inputData, config); if (entity) { result.entity = entity; } return result; } } ``` ### 4.2 OpenRouter Integration **API Configuration:** ```typescript export class OpenRouterProvider implements AIModelProvider { name = 'openrouter'; async predict( signals: RiskSignals, config: PPREConfig ): Promise { const prompt = this.createRiskAnalysisPrompt(signals, config); const response = await fetch('https://openrouter.ai/api/v1/chat/completions', { method: 'POST', headers: { 'Authorization': `Bearer ${config.apiKey}`, 'Content-Type': 'application/json', }, body: JSON.stringify({ model: 'openai/gpt-4', messages: [ { role: 'system', content: this.getSystemPrompt() }, { role: 'user', content: prompt } ], temperature: 0.1, max_tokens: 1000 }) }); return this.parseResponse(await response.json(), signals); } private createRiskAnalysisPrompt( signals: RiskSignals, config: PPREConfig ): string { return `Analyze these risk signals and provide a detailed risk assessment: SIGNALS: ${JSON.stringify(signals.signals, null, 2)} CONFIGURATION: - Threshold: ${config.threshold} - Signal Sources: ${config.signalSources.join(', ')} Provide response in this JSON format: { "riskScore": 0.75, "confidence": 0.92, "isHighRisk": true, "analysis": { "signalAnalysis": {...}, "riskFactors": [...], "modelUsed": "gpt-4" } }`; } } ``` ### 4.3 Risk Signal Processing **Signal Types and Validation:** ```typescript export interface RiskSignals { signals: { repo_pushes?: number; audit_flags?: number; login_failures?: number; geo_drift?: boolean | number; contract_interaction_volume?: number; transaction_frequency?: number; gas_usage_pattern?: number; wallet_age?: number; [key: string]: any; }; } export async function validateSignals(signals: RiskSignals): Promise { // Check structure if (!signals.signals || typeof signals.signals !== 'object') { throw new Error('Invalid signals structure'); } // Validate signal types for (const [key, value] of Object.entries(signals.signals)) { if (value === null || value === undefined) { throw new Error(`Signal '${key}' has invalid value: ${value}`); } // Type-specific validation if (key.includes('_count') || key.includes('_volume')) { if (typeof value !== 'number' || value < 0) { throw new Error(`Signal '${key}' must be a non-negative number`); } } } return true; } ``` ### 4.4 Confidence Scoring and Risk Thresholds **Risk Assessment Logic:** ```typescript export interface RiskPredictionResult { entity: string; riskScore: number; // 0-1 confidence: number; // 0-1 threshold: number; isHighRisk: boolean; analysis: { signalAnalysis: Record; modelUsed: string; signalSources: string[]; riskFactors: string[]; }; timestamp: string; } export function calculateRiskLevel( riskScore: number, threshold: number, confidence: number ): 'low' | 'medium' | 'high' | 'critical' { if (riskScore >= 0.9 && confidence >= 0.8) return 'critical'; if (riskScore >= threshold && confidence >= 0.7) return 'high'; if (riskScore >= threshold * 0.7) return 'medium'; return 'low'; } ``` ## 5. Real-time Streaming and Monitoring ### 5.1 File Watching System **Chokidar Integration:** ```typescript export class StreamWatcher { private watcher: FSWatcher | null = null; private stats: StreamStats = { filesProcessed: 0, predictionsRun: 0, triggersExecuted: 0, errors: 0, uptime: 0, startTime: new Date().toISOString() }; async start(watchPath: string, options: StreamOptions): Promise { const startTime = Date.now(); this.watcher = chokidar.watch(watchPath, { ignored: /^\./, // Ignore hidden files persistent: true, ignoreInitial: options.ignoreInitial !== false, awaitWriteFinish: { stabilityThreshold: 1000, pollInterval: 100 } }); const debouncedProcessor = debounce( this.processFile.bind(this, options), options.debounce || 2000 ); this.watcher .on('add', (path) => { logger.debug(`File added: ${path}`); debouncedProcessor(path); }) .on('change', (path) => { logger.debug(`File changed: ${path}`); debouncedProcessor(path); }) .on('error', (error) => { logger.error(`Watcher error: ${error.message}`); this.stats.errors++; }); this.stats.startTime = new Date().toISOString(); logger.info(`Stream watcher started, monitoring: ${watchPath}`); } } ``` ### 5.2 Debounced Processing **Event Debouncing:** ```typescript export function debounce any>( func: T, delay: number ): (...args: Parameters) => void { let timeoutId: NodeJS.Timeout; return (...args: Parameters) => { clearTimeout(timeoutId); timeoutId = setTimeout(() => func(...args), delay); }; } ``` ### 5.3 Stream Event Processing **Event Pipeline:** ```typescript export async function processStreamFile( filePath: string, options: StreamOptions ): Promise { const startTime = Date.now(); const event: StreamEvent = { timestamp: new Date().toISOString(), type: 'file_added', file: filePath, action: 'processing' }; try { // Load and validate signals const signals = await loadSignalsFromFile(filePath); const entity = extractEntityFromPath(filePath, options.entityField); if (!entity) { throw new Error('Could not extract entity ID from file path'); } // Run prediction const config = loadConfig(); const prediction = await runPrediction(signals, config, entity); event.type = 'prediction'; event.entity = entity; event.riskScore = prediction.riskScore; event.duration = Date.now() - startTime; // Auto-trigger if enabled and high risk if (options.autoTrigger && prediction.isHighRisk) { await triggerRiskEvent(entity, prediction.riskScore); event.type = 'trigger'; event.action = 'triggered'; } // Log result await logger.info( `Stream processed: ${entity} - Risk: ${prediction.riskScore.toFixed(3)}`, 'STREAM' ); return event; } catch (error: any) { event.type = 'error'; event.error = error.message; event.duration = Date.now() - startTime; await logger.error(`Stream processing failed: ${error.message}`, 'STREAM'); return event; } } ``` ## 6. Machine Learning and Fine-tuning ### 6.1 Local Model Training **Training Data Processing:** ```typescript export interface TrainingData { signals: Record; label: 'compliant' | 'violation'; } export class DataLoader { async loadTrainingData(filePath: string): Promise { const ext = path.extname(filePath).toLowerCase(); if (ext === '.jsonl') { return this.loadJSONL(filePath); } else if (ext === '.csv') { return this.loadCSV(filePath); } else { throw new Error(`Unsupported file format: ${ext}`); } } private async loadJSONL(filePath: string): Promise { const content = await fs.readFile(filePath, 'utf8'); const lines = content.trim().split('\n'); return lines.map(line => { try { const data = JSON.parse(line); this.validateTrainingRecord(data); return data as TrainingData; } catch (error) { throw new Error(`Invalid JSONL format in line: ${line}`); } }); } } ``` ### 6.2 Feature Engineering and Scaling **Data Preprocessing:** ```typescript export class FeatureProcessor { preprocessData(data: TrainingData[]): { features: number[][]; labels: number[]; featureNames: string[]; scalingParams: Record; } { // Extract feature names const featureNames = this.extractFeatureNames(data); // Convert to numeric features const features = data.map(record => featureNames.map(name => this.normalizeFeature(record.signals[name])) ); // Convert labels to binary const labels = data.map(record => record.label === 'violation' ? 1 : 0); // Calculate scaling parameters const scalingParams = this.calculateScalingParams(features, featureNames); // Apply scaling const scaledFeatures = this.applyScaling(features, scalingParams); return { features: scaledFeatures, labels, featureNames, scalingParams }; } private calculateScalingParams( features: number[][], featureNames: string[] ): Record { const params: Record = {}; for (let i = 0; i < featureNames.length; i++) { const values = features.map(row => row[i]); params[featureNames[i]] = { min: Math.min(...values), max: Math.max(...values) }; } return params; } } ``` ### 6.3 Logistic Regression Implementation **Training Algorithm:** ```typescript export class LogisticRegression { train( features: number[][], labels: number[], options: TrainingOptions ): TrainingResult { const numFeatures = features[0].length; let weights = new Array(numFeatures).fill(0); let bias = 0; const learningRate = options.learningRate || 0.01; const epochs = options.epochs || 100; for (let epoch = 0; epoch < epochs; epoch++) { const { weightGradients, biasGradient } = this.computeGradients( features, labels, weights, bias ); // Update weights for (let i = 0; i < weights.length; i++) { weights[i] -= learningRate * weightGradients[i]; } bias -= learningRate * biasGradient; // Log progress if (options.verbose && epoch % 10 === 0) { const loss = this.computeLoss(features, labels, weights, bias); console.log(`Epoch ${epoch}: Loss = ${loss.toFixed(4)}`); } } return { weights, bias }; } private sigmoid(z: number): number { return 1 / (1 + Math.exp(-Math.max(-250, Math.min(250, z)))); } private computeGradients( features: number[][], labels: number[], weights: number[], bias: number ): { weightGradients: number[]; biasGradient: number } { const m = features.length; const weightGradients = new Array(weights.length).fill(0); let biasGradient = 0; for (let i = 0; i < m; i++) { const prediction = this.predict(features[i], weights, bias); const error = prediction - labels[i]; biasGradient += error; for (let j = 0; j < weights.length; j++) { weightGradients[j] += error * features[i][j]; } } // Average gradients for (let j = 0; j < weights.length; j++) { weightGradients[j] /= m; } biasGradient /= m; return { weightGradients, biasGradient }; } } ``` ### 6.4 Model Persistence and Loading **Trained Model Format:** ```typescript export interface TrainedModel { version: string; algorithm: string; trained_at: string; features: string[]; weights: Record; bias: number; scaling: Record; metrics: { training_accuracy: number; validation_accuracy: number; precision: number; recall: number; f1_score: number; }; training_samples: number; validation_samples: number; } export class LocalTrainedProvider implements AIModelProvider { name = 'local-trained'; private model: TrainedModel | null = null; async loadModel(modelPath: string): Promise { try { const modelData = await fs.readFile(modelPath, 'utf8'); this.model = JSON.parse(modelData); // Validate model format this.validateModel(this.model); } catch (error) { throw new Error(`Failed to load model: ${error.message}`); } } async predict( signals: RiskSignals, config: PPREConfig ): Promise { if (!this.model) { throw new Error('No trained model loaded'); } // Extract and scale features const features = this.extractFeatures(signals.signals, this.model); const scaledFeatures = this.applyScaling(features, this.model.scaling); // Compute prediction const riskScore = this.computeRiskScore(scaledFeatures, this.model); return { entity: '', riskScore, confidence: this.calculateConfidence(riskScore, features), threshold: config.threshold, isHighRisk: riskScore >= config.threshold, analysis: { signalAnalysis: this.analyzeSignals(signals.signals, this.model), modelUsed: `local-${this.model.algorithm}`, signalSources: config.signalSources, riskFactors: this.identifyRiskFactors(signals.signals, this.model) }, timestamp: new Date().toISOString() }; } } ``` ## 7. Memory Systems and Vector Databases ### 7.1 Vector Database Integration **Provider Interface:** ```typescript export interface VectorDBProvider { initialize(): Promise; storeVector(entityId: string, signals: Record): Promise; querySimilarVectors( entityId: string, signals: Record, topK: number ): Promise; } export interface VectorSearchResult { entityId: string; signals: Record; timestamp: string; similarity: number; riskScore?: number; } ``` ### 7.2 Weaviate Integration **Weaviate Provider Implementation:** ```typescript export class WeaviateProvider implements VectorDBProvider { private client: any; private className: string; constructor(config: WeaviateConfig) { this.client = weaviate.client({ scheme: 'https', host: config.url || 'localhost:8080', apiKey: new weaviate.ApiKey(config.apiKey || ''), }); this.className = config.className || 'PPRESignals'; } async initialize(): Promise { // Create schema if it doesn't exist const schema = { class: this.className, description: 'PPRE risk signals with vector embeddings', properties: [ { name: 'entityId', dataType: ['string'], description: 'Entity identifier' }, { name: 'signals', dataType: ['object'], description: 'Risk signals data' }, { name: 'timestamp', dataType: ['date'], description: 'Signal timestamp' }, { name: 'riskScore', dataType: ['number'], description: 'Calculated risk score' } ], vectorizer: 'text2vec-openai' }; try { await this.client.schema.classCreator().withClass(schema).do(); } catch (error) { // Class might already exist console.log(`Schema creation note: ${error.message}`); } } async storeVector( entityId: string, signals: Record ): Promise { const signalText = this.signalsToText(signals); await this.client.data .creator() .withClassName(this.className) .withProperties({ entityId, signals, timestamp: new Date().toISOString(), signalText }) .do(); } async querySimilarVectors( entityId: string, signals: Record, topK: number ): Promise { const queryText = this.signalsToText(signals); const result = await this.client.graphql .get() .withClassName(this.className) .withFields('entityId signals timestamp riskScore _additional { certainty }') .withNearText({ concepts: [queryText] }) .withLimit(topK) .do(); return result.data.Get[this.className].map((item: any) => ({ entityId: item.entityId, signals: item.signals, timestamp: item.timestamp, similarity: item._additional.certainty, riskScore: item.riskScore })); } private signalsToText(signals: Record): string { return Object.entries(signals) .map(([key, value]) => `${key}: ${value}`) .join(', '); } } ``` ### 7.3 Memory-Enhanced Predictions **Vector Similarity Integration:** ```typescript export class MemoryEnhancedPredictor { constructor( private vectorDB: VectorDBProvider, private predictionEngine: PredictionEngine ) {} async predictWithMemory( signals: RiskSignals, entityId: string, config: PPREConfig ): Promise { // Get base prediction const basePrediction = await this.predictionEngine.runPrediction( signals, config, entityId ); // Retrieve similar historical patterns const similarPatterns = await this.vectorDB.querySimilarVectors( entityId, signals.signals, 5 ); if (similarPatterns.length === 0) { return basePrediction; } // Calculate memory influence const memoryInfluence = this.calculateMemoryInfluence( signals.signals, similarPatterns ); // Adjust prediction based on memory const adjustedScore = this.adjustRiskScore( basePrediction.riskScore, memoryInfluence ); return { ...basePrediction, riskScore: adjustedScore, memoryContext: memoryInfluence, analysis: { ...basePrediction.analysis, riskFactors: [ ...basePrediction.analysis.riskFactors, `Memory influence: ${memoryInfluence.length} similar patterns` ] } }; } private calculateMemoryInfluence( currentSignals: Record, patterns: VectorSearchResult[] ): MemoryInfluence[] { return patterns.map(pattern => ({ matchScore: pattern.similarity, signals: pattern.signals, timestamp: pattern.timestamp, riskScore: pattern.riskScore })); } private adjustRiskScore( baseScore: number, memoryInfluence: MemoryInfluence[] ): number { if (memoryInfluence.length === 0) return baseScore; // Weight memory influence by similarity const weightedSum = memoryInfluence.reduce((sum, influence) => { const weight = influence.matchScore; const memoryScore = influence.riskScore || 0.5; return sum + (weight * memoryScore); }, 0); const totalWeight = memoryInfluence.reduce((sum, influence) => sum + influence.matchScore, 0 ); const memoryAverage = weightedSum / totalWeight; // Blend base prediction with memory (70% base, 30% memory) return (baseScore * 0.7) + (memoryAverage * 0.3); } } ``` ### 7.4 Backup and Recovery **Backup Management:** ```typescript export class BackupManager { private backupDir: string; constructor(backupDir: string = './backups') { this.backupDir = backupDir; } async createBackup( entityId: string, signals: Record ): Promise { const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); const filename = `${entityId}_${timestamp}.json`; const filepath = path.join(this.backupDir, filename); // Ensure backup directory exists await fs.ensureDir(this.backupDir); const backupData = { entityId, signals, timestamp: new Date().toISOString(), version: '1.0.0' }; await fs.writeJSON(filepath, backupData, { spaces: 2 }); return filepath; } async restoreFromBackup(backupPath: string): Promise { const backupData = await fs.readJSON(backupPath); return { entityId: backupData.entityId, signals: backupData.signals, timestamp: backupData.timestamp, embedding: [] // Will be recalculated }; } async listBackups(entityId?: string): Promise { const files = await fs.readdir(this.backupDir); if (entityId) { return files.filter(file => file.startsWith(`${entityId}_`)); } return files.filter(file => file.endsWith('.json')); } } ``` ## 8. SLA Policy Enforcement ### 8.1 Policy Definition and Management **SLA Policy Structure:** ```typescript export interface SLAPolicy { entityId: string; entityPattern?: string; // For wildcard matching like "*-enterprise-*" maxRiskScore: number; violationWindow: number; // Number of recent predictions to check action: 'notify' | 'autoPause' | 'webhook'; description: string; enabled: boolean; notifyWebhook?: string; createdAt?: string; } export interface SLASettings { strictMode: boolean; // Exit CLI with code 1 on violation logViolations: boolean; enableSmartContracts: boolean; defaultAction: 'notify' | 'autoPause'; } export interface SLAResult { compliant: boolean; policy: SLAPolicy; currentScore: number; violationCount: number; recentScores: number[]; averageScore: number; actionRequired: boolean; actionTaken?: string; violationRate: number; trend: 'increasing' | 'decreasing' | 'stable'; } ``` ### 8.2 SLA Engine Implementation **Policy Evaluation:** ```typescript export class SLAEngine { private policies: SLAPolicy[] = []; private violations: SLAViolation[] = []; async loadPolicies(configPath?: string): Promise { const config = loadConfig(); this.policies = config.slaPolicies || []; if (configPath) { const additionalPolicies = await fs.readJSON(configPath); this.policies.push(...additionalPolicies); } } async checkCompliance( entityId: string, currentScore: number, recentScores: number[] ): Promise { const applicablePolicies = this.findApplicablePolicies(entityId); const results: SLAResult[] = []; for (const policy of applicablePolicies) { const result = await this.evaluatePolicy( policy, entityId, currentScore, recentScores ); results.push(result); if (!result.compliant && result.actionRequired) { await this.executeAction(policy, result); } } return results; } private findApplicablePolicies(entityId: string): SLAPolicy[] { return this.policies.filter(policy => { if (!policy.enabled) return false; // Exact match if (policy.entityId === entityId) return true; // Pattern match if (policy.entityPattern) { const regex = new RegExp( policy.entityPattern.replace(/\*/g, '.*') ); return regex.test(entityId); } return false; }); } private async evaluatePolicy( policy: SLAPolicy, entityId: string, currentScore: number, recentScores: number[] ): Promise { const relevantScores = recentScores.slice(-policy.violationWindow); const violationCount = relevantScores.filter( score => score > policy.maxRiskScore ).length; const averageScore = relevantScores.reduce((a, b) => a + b, 0) / relevantScores.length; const violationRate = violationCount / relevantScores.length; const trend = this.calculateTrend(relevantScores); const compliant = currentScore <= policy.maxRiskScore; return { compliant, policy, currentScore, violationCount, recentScores: relevantScores, averageScore, actionRequired: !compliant && violationCount > 0, violationRate, trend }; } private async executeAction( policy: SLAPolicy, result: SLAResult ): Promise { const violation: SLAViolation = { entityId: result.policy.entityId, policy, currentScore: result.currentScore, violationCount: result.violationCount, timestamp: new Date().toISOString() }; switch (policy.action) { case 'notify': await this.sendNotification(violation); break; case 'webhook': if (policy.notifyWebhook) { await this.callWebhook(policy.notifyWebhook, violation); } break; case 'autoPause': await this.pauseEntity(violation.entityId); break; } this.violations.push(violation); violation.actionTaken = policy.action; } } ``` ### 8.3 SLA Reporting and Analytics **Compliance Reporting:** ```typescript export class SLAReporter { async generateComplianceReport( results: SLAResult[], format: 'json' | 'table' | 'summary' = 'table' ): Promise { if (format === 'json') { return JSON.stringify(results, null, 2); } if (format === 'summary') { return this.generateSummaryReport(results); } return this.generateTableReport(results); } private generateTableReport(results: SLAResult[]): string { const table = new Table({ head: ['Entity', 'Policy', 'Score', 'Threshold', 'Status', 'Trend', 'Action'], colWidths: [20, 25, 12, 12, 15, 12, 15] }); results.forEach(result => { const status = result.compliant ? chalk.green('✓ Compliant') : chalk.red('✗ Violation'); const trendIcon = { 'increasing': '📈', 'decreasing': '📉', 'stable': '➡️' }[result.trend]; table.push([ result.policy.entityId, result.policy.description.substring(0, 22) + '...', result.currentScore.toFixed(3), result.policy.maxRiskScore.toFixed(3), status, `${trendIcon} ${result.trend}`, result.actionTaken || 'none' ]); }); return table.toString(); } private generateSummaryReport(results: SLAResult[]): string { const total = results.length; const compliant = results.filter(r => r.compliant).length; const violations = total - compliant; const complianceRate = (compliant / total) * 100; return ` 📊 SLA Compliance Summary ═══════════════════════════════════════ Total Policies Evaluated: ${total} Compliant: ${compliant} (${complianceRate.toFixed(1)}%) Violations: ${violations} ${violations > 0 ? '⚠️ Immediate attention required for violations' : '✅ All policies compliant'} `; } } ``` ## 9. Configuration Management ### 9.1 Configuration Schema **Main Configuration Interface:** ```typescript export interface PPREConfig { version: string; modelProvider: ModelProvider; threshold: number; signalSources: SignalSource[]; smartContractEndpoint: string; apiKey: string; logLevel: 'debug' | 'info' | 'warn' | 'error'; logFile?: string; outputFormat: 'json' | 'table' | 'csv'; timeout: number; vectorDB?: VectorDBConfig; slaPolicies?: SLAPolicy[]; slaSettings?: SLASettings; } export interface VectorDBConfig { provider: VectorDBProviderType; url?: string; apiKey?: string; embeddingModel?: string; dimensions?: number; } ``` ### 9.2 Configuration Loading and Validation **Configuration Management:** ```typescript export class ConfigManager { private static instance: ConfigManager; private config: PPREConfig | null = null; static getInstance(): ConfigManager { if (!ConfigManager.instance) { ConfigManager.instance = new ConfigManager(); } return ConfigManager.instance; } loadConfig(configPath?: string): PPREConfig { if (this.config) { return this.config; } // Load from various sources in priority order const configs = [ this.loadFromEnvironment(), this.loadFromFile(configPath), this.loadFromDefaults() ]; // Merge configurations this.config = this.mergeConfigs(configs); // Validate this.validateConfig(this.config); return this.config; } private loadFromEnvironment(): Partial { return { modelProvider: (process.env.PPRE_MODEL_PROVIDER as ModelProvider) || undefined, threshold: process.env.PPRE_THRESHOLD ? parseFloat(process.env.PPRE_THRESHOLD) : undefined, apiKey: process.env.PPRE_API_KEY || undefined, logLevel: (process.env.PPRE_LOG_LEVEL as any) || undefined, timeout: process.env.PPRE_TIMEOUT ? parseInt(process.env.PPRE_TIMEOUT) : undefined }; } private loadFromFile(configPath?: string): Partial { const defaultPaths = [ configPath, './ppre.config.json', './config/ppre.config.json', path.join(os.homedir(), '.ppre', 'config.json') ].filter(Boolean); for (const configFile of defaultPaths) { try { if (fs.existsSync(configFile!)) { const content = fs.readFileSync(configFile!, 'utf8'); return JSON.parse(content); } } catch (error) { console.warn(`Failed to load config from ${configFile}: ${error.message}`); } } return {}; } private loadFromDefaults(): PPREConfig { return { version: '1.0.0', modelProvider: 'openrouter', threshold: 0.7, signalSources: ['onchain', 'offchain'], smartContractEndpoint: 'http://localhost:3000', apiKey: '', logLevel: 'info', outputFormat: 'table', timeout: 30000 }; } private validateConfig(config: PPREConfig): void { if (!config.version) { throw new Error('Configuration version is required'); } if (!['openrouter', 'local-trained', 'stubbed'].includes(config.modelProvider)) { throw new Error(`Invalid model provider: ${config.modelProvider}`); } if (config.threshold < 0 || config.threshold > 1) { throw new Error('Threshold must be between 0 and 1'); } if (config.timeout < 1000) { throw new Error('Timeout must be at least 1000ms'); } } } ``` ### 9.3 Environment-Specific Configuration **Environment Variables:** ```bash # Core Configuration PPRE_MODEL_PROVIDER=openrouter PPRE_THRESHOLD=0.75 PPRE_API_KEY=your_api_key_here PPRE_LOG_LEVEL=info PPRE_TIMEOUT=45000 # Vector Database PPRE_VECTOR_DB_PROVIDER=weaviate PPRE_VECTOR_DB_URL=https://your-cluster.weaviate.network PPRE_VECTOR_DB_API_KEY=your_weaviate_key # Smart Contract Integration PPRE_SMART_CONTRACT_ENDPOINT=https://your-contract-api.com PPRE_ENABLE_SMART_CONTRACTS=true # Advanced Features PPRE_ENABLE_MEMORY_ENHANCED=true PPRE_SLA_STRICT_MODE=false PPRE_BACKUP_ENABLED=true ``` ### 9.4 Configuration Templates **Enterprise Configuration Template:** ```json { "version": "1.0.0", "modelProvider": "openrouter", "threshold": 0.8, "signalSources": ["onchain", "offchain", "external"], "smartContractEndpoint": "https://enterprise-api.company.com", "apiKey": "${PPRE_API_KEY}", "logLevel": "info", "outputFormat": "json", "timeout": 60000, "vectorDB": { "provider": "weaviate", "url": "${WEAVIATE_URL}", "apiKey": "${WEAVIATE_API_KEY}", "embeddingModel": "text2vec-openai", "dimensions": 1536 }, "slaSettings": { "strictMode": true, "logViolations": true, "enableSmartContracts": true, "defaultAction": "webhook" }, "slaPolicies": [ { "entityId": "*-enterprise-*", "maxRiskScore": 0.6, "violationWindow": 10, "action": "webhook", "description": "Enterprise user high-risk threshold", "enabled": true, "notifyWebhook": "https://alerts.company.com/ppre-violation" } ] } ``` ## 10. Signal Processing and Data Flow ### 10.1 Signal Ingestion Pipeline **Signal File Processing:** ```typescript export class SignalProcessor { async processSignalFile(filePath: string): Promise { try { // Validate file exists and is readable await this.validateFile(filePath); // Load and parse JSON const content = await fs.readFile(filePath, 'utf8'); const rawData = JSON.parse(content); // Validate structure const signals = this.validateSignalStructure(rawData); // Normalize and clean signals const normalizedSignals = this.normalizeSignals(signals); // Apply signal transformations const transformedSignals = this.applyTransformations(normalizedSignals); return { signals: transformedSignals }; } catch (error: any) { throw new Error(`Signal processing failed: ${error.message}`); } } private validateSignalStructure(data: any): Record { if (!data || typeof data !== 'object') { throw new Error('Invalid signal file: must be a JSON object'); } if (!data.signals || typeof data.signals !== 'object') { throw new Error('Invalid signal file: missing "signals" property'); } return data.signals; } private normalizeSignals(signals: Record): Record { const normalized: Record = {}; for (const [key, value] of Object.entries(signals)) { // Handle different data types if (typeof value === 'boolean') { normalized[key] = value ? 1 : 0; } else if (typeof value === 'string') { // Try to convert string numbers const numValue = parseFloat(value); normalized[key] = isNaN(numValue) ? this.encodeString(value) : numValue; } else if (typeof value === 'number') { normalized[key] = value; } else { // Convert complex objects to numeric representation normalized[key] = this.encodeComplexValue(value); } } return normalized; } private applyTransformations(signals: Record): Record { const transformed = { ...signals }; // Apply domain-specific transformations if ('login_failures' in transformed && 'login_attempts' in transformed) { transformed.login_failure_rate = transformed.login_failures / Math.max(transformed.login_attempts, 1); } if ('gas_used' in transformed && 'transaction_count' in transformed) { transformed.avg_gas_per_transaction = transformed.gas_used / Math.max(transformed.transaction_count, 1); } // Time-based features if ('last_activity' in transformed) { const lastActivity = new Date(transformed.last_activity); const now = new Date(); transformed.hours_since_activity = (now.getTime() - lastActivity.getTime()) / (1000 * 60 * 60); } return transformed; } } ``` ### 10.2 Real-time Data Flow **Event-Driven Processing:** ```typescript export class DataFlowManager { private eventEmitter: EventEmitter = new EventEmitter(); private processingQueue: Queue = new Queue(); constructor( private signalProcessor: SignalProcessor, private predictionEngine: PredictionEngine, private memorySystem: MemoryStream ) { this.setupEventHandlers(); this.startProcessingWorker(); } private setupEventHandlers(): void { this.eventEmitter.on('signal_received', async (data: SignalEvent) => { await this.handleSignalEvent(data); }); this.eventEmitter.on('prediction_complete', async (result: PredictionResult) => { await this.handlePredictionResult(result); }); this.eventEmitter.on('memory_updated', async (update: MemoryUpdate) => { await this.handleMemoryUpdate(update); }); } async processSignalEvent(filePath: string): Promise { // Queue for asynchronous processing this.processingQueue.enqueue({ type: 'signal_processing', filePath, timestamp: new Date().toISOString() }); this.eventEmitter.emit('signal_received', { filePath }); } private async handleSignalEvent(data: SignalEvent): Promise { try { // Process signals const signals = await this.signalProcessor.processSignalFile(data.filePath); // Extract entity ID const entityId = this.extractEntityId(data.filePath); // Update memory system await this.memorySystem.processSignalFile(data.filePath); // Run prediction const config = loadConfig(); const prediction = await this.predictionEngine.runPrediction( signals, config, entityId ); this.eventEmitter.emit('prediction_complete', { entityId, prediction, signals }); } catch (error: any) { console.error(`Signal processing error: ${error.message}`); this.eventEmitter.emit('processing_error', { filePath: data.filePath, error: error.message }); } } } ``` ### 10.3 Batch Processing **Batch Signal Processing:** ```typescript export class BatchProcessor { async processBatch( inputPath: string, options: BatchProcessingOptions ): Promise { const files = await this.discoverSignalFiles(inputPath); const results: ProcessingResult[] = []; const batchSize = options.batchSize || 10; console.log(`Processing ${files.length} files in batches of ${batchSize}`); for (let i = 0; i < files.length; i += batchSize) { const batch = files.slice(i, i + batchSize); const batchResults = await this.processBatchChunk(batch, options); results.push(...batchResults); // Progress reporting const progress = Math.min((i + batchSize) / files.length * 100, 100); console.log(`Progress: ${progress.toFixed(1)}% (${i + batchSize}/${files.length})`); // Rate limiting if (options.delayBetweenBatches) { await this.sleep(options.delayBetweenBatches); } } return { totalFiles: files.length, successfullyProcessed: results.filter(r => r.success).length, errors: results.filter(r => !r.success).length, results, summary: this.generateBatchSummary(results) }; } private async processBatchChunk( files: string[], options: BatchProcessingOptions ): Promise { // Process files concurrently within batch const promises = files.map(async file => { try { const signals = await this.signalProcessor.processSignalFile(file); const entityId = this.extractEntityId(file); const config = loadConfig(); const prediction = await this.predictionEngine.runPrediction( signals, config, entityId ); return { file, entityId, success: true, prediction, processingTime: Date.now() }; } catch (error: any) { return { file, success: false, error: error.message, processingTime: Date.now() }; } }); return Promise.all(promises); } } ``` ## 11. Performance and Scalability ### 11.1 Concurrent Processing **Async Processing with Semaphores:** ```typescript export class Semaphore { private permits: number; private waiting: Array<() => void> = []; constructor(permits: number) { this.permits = permits; } async acquire(): Promise { if (this.permits > 0) { this.permits--; return; } return new Promise((resolve) => { this.waiting.push(resolve); }); } release(): void { this.permits++; if (this.waiting.length > 0) { const resolve = this.waiting.shift()!; this.permits--; resolve(); } } } export async function processFilesWithConcurrency( files: string[], processor: (file: string) => Promise, maxConcurrency: number = 5 ): Promise { const semaphore = new Semaphore(maxConcurrency); const promises = files.map(async (file) => { await semaphore.acquire(); try { return await processor(file); } finally { semaphore.release(); } }); return Promise.all(promises); } ``` ### 11.2 Caching Strategy **Multi-Level Caching:** ```typescript export class PredictionCache { private memoryCache = new Map(); private diskCache: string; private maxMemorySize: number = 1000; private ttl: number = 24 * 60 * 60 * 1000; // 24 hours constructor(cacheDir: string = './cache') { this.diskCache = cacheDir; this.ensureCacheDir(); } async get(key: string): Promise { // Check memory cache first const memoryCached = this.memoryCache.get(key); if (memoryCached && this.isValid(memoryCached)) { return memoryCached.result; } // Check disk cache const diskCached = await this.getDiskCached(key); if (diskCached && this.isValid(diskCached)) { // Promote to memory cache this.memoryCache.set(key, diskCached); return diskCached.result; } return null; } async set(key: string, result: RiskPredictionResult): Promise { const cached: CachedPrediction = { result, timestamp: Date.now(), key }; // Store in memory this.memoryCache.set(key, cached); // Store on disk await this.setDiskCached(key, cached); // Cleanup if needed if (this.memoryCache.size > this.maxMemorySize) { this.evictOldest(); } } private generateCacheKey( signals: RiskSignals, config: PPREConfig ): string { const hashInput = JSON.stringify({ signals: signals.signals, modelProvider: config.modelProvider, threshold: config.threshold }); return crypto.createHash('sha256').update(hashInput).digest('hex').substring(0, 16); } private isValid(cached: CachedPrediction): boolean { return (Date.now() - cached.timestamp) < this.ttl; } } ``` ### 11.3 Memory Management **Resource Monitoring:** ```typescript export class ResourceMonitor { private memoryThreshold: number = 0.8; // 80% of available memory private cpuThreshold: number = 0.9; // 90% CPU usage async checkResources(): Promise { const memoryUsage = process.memoryUsage(); const totalMemory = os.totalmem(); const freeMemory = os.freemem(); const usedMemory = totalMemory - freeMemory; const memoryUtilization = usedMemory / totalMemory; const heapUtilization = memoryUsage.heapUsed / memoryUsage.heapTotal; return { memory: { utilization: memoryUtilization, heapUtilization, available: freeMemory, total: totalMemory, withinThreshold: memoryUtilization < this.memoryThreshold }, heap: { used: memoryUsage.heapUsed, total: memoryUsage.heapTotal, external: memoryUsage.external }, recommendations: this.generateRecommendations(memoryUtilization, heapUtilization) }; } private generateRecommendations( memoryUtil: number, heapUtil: number ): string[] { const recommendations: string[] = []; if (memoryUtil > this.memoryThreshold) { recommendations.push('Consider reducing batch size or concurrent operations'); recommendations.push('Enable disk caching to reduce memory usage'); } if (heapUtil > 0.9) { recommendations.push('Force garbage collection or restart process'); recommendations.push('Reduce cache size or TTL values'); } return recommendations; } async enforceMemoryLimits(): Promise { const status = await this.checkResources(); if (!status.memory.withinThreshold) { // Force garbage collection if (global.gc) { global.gc(); } // Clear caches await this.clearCaches(); // Reduce concurrent operations await this.reduceConcurrency(); } } } ``` ### 11.4 Performance Optimization **Algorithm Optimization:** ```typescript export class PerformanceOptimizer { async optimizePredictionPipeline( config: PPREConfig ): Promise { const optimizations: Optimization[] = []; // Model provider optimization if (config.modelProvider === 'openrouter') { optimizations.push({ type: 'model_caching', description: 'Enable response caching for OpenRouter API', expectedImprovement: '30-50% reduction in API calls' }); } // Vector database optimization if (config.vectorDB?.provider === 'weaviate') { optimizations.push({ type: 'vector_indexing', description: 'Optimize vector index configuration', expectedImprovement: '20-40% faster similarity search' }); } // Batch processing optimization optimizations.push({ type: 'batch_tuning', description: 'Tune batch sizes based on available resources', expectedImprovement: '15-25% better throughput' }); return { optimizations, estimatedPerformanceGain: this.calculatePerformanceGain(optimizations), implementationPriority: this.prioritizeOptimizations(optimizations) }; } async benchmarkPredictionPerformance( testCases: number = 100 ): Promise { const startTime = process.hrtime.bigint(); const memoryBefore = process.memoryUsage(); const testSignals = this.generateTestSignals(testCases); const config = loadConfig(); const results: number[] = []; for (const signals of testSignals) { const taskStart = process.hrtime.bigint(); await this.predictionEngine.runPrediction(signals, config); const taskEnd = process.hrtime.bigint(); results.push(Number(taskEnd - taskStart) / 1_000_000); // Convert to milliseconds } const endTime = process.hrtime.bigint(); const memoryAfter = process.memoryUsage(); return { totalTime: Number(endTime - startTime) / 1_000_000, averageTime: results.reduce((a, b) => a + b, 0) / results.length, medianTime: this.calculateMedian(results), p95Time: this.calculatePercentile(results, 0.95), p99Time: this.calculatePercentile(results, 0.99), memoryDelta: { heap: memoryAfter.heapUsed - memoryBefore.heapUsed, external: memoryAfter.external - memoryBefore.external }, throughput: testCases / (Number(endTime - startTime) / 1_000_000_000) // per second }; } } ``` ## 12. Security Considerations ### 12.1 API Key Management **Secure Credential Storage:** ```typescript export class SecureCredentialManager { private keytar: typeof import('keytar'); constructor() { // Lazy load keytar for cross-platform compatibility this.keytar = require('keytar'); } async storeAPIKey(service: string, apiKey: string): Promise { // Validate API key format if (!this.isValidAPIKey(apiKey)) { throw new Error('Invalid API key format'); } // Store in system keychain await this.keytar.setPassword('ppre-cli', service, apiKey); // Clear from memory apiKey = ''; } async retrieveAPIKey(service: string): Promise { try { return await this.keytar.getPassword('ppre-cli', service); } catch (error) { console.warn(`Failed to retrieve API key for ${service}: ${error.message}`); return null; } } async deleteAPIKey(service: string): Promise { try { await this.keytar.deletePassword('ppre-cli', service); } catch (error) { console.warn(`Failed to delete API key for ${service}: ${error.message}`); } } private isValidAPIKey(apiKey: string): boolean { // Validate format (example: starts with 'ppre_' followed by alphanumeric) return /^ppre_[a-zA-Z0-9]{32,64}$/.test(apiKey); } } ``` ### 12.2 Input Validation and Sanitization **Signal Data Validation:** ```typescript export class SecurityValidator { validateSignalInput(signals: any): RiskSignals { // Check for injection attempts if (this.containsSuspiciousContent(signals)) { throw new Error('Potentially malicious content detected in signals'); } // Validate structure if (!signals || typeof signals !== 'object') { throw new Error('Invalid signal structure'); } // Sanitize values const sanitizedSignals = this.sanitizeSignals(signals.signals || signals); return { signals: sanitizedSignals }; } private containsSuspiciousContent(data: any): boolean { const dataString = JSON.stringify(data); // Check for common injection patterns const suspiciousPatterns = [ /.*?<\/script>/gi, /javascript:/gi, /eval\(/gi, /Function\(/gi, /require\(/gi, /import\(/gi, /__proto__/gi, /constructor/gi ]; return suspiciousPatterns.some(pattern => pattern.test(dataString)); } private sanitizeSignals(signals: Record): Record { const sanitized: Record = {}; for (const [key, value] of Object.entries(signals)) { // Sanitize key const sanitizedKey = key.replace(/[^a-zA-Z0-9_]/g, ''); // Sanitize value based on type if (typeof value === 'string') { // Remove potentially dangerous characters sanitized[sanitizedKey] = value.replace(/[<>'"]/g, ''); } else if (typeof value === 'number') { // Validate numeric ranges if (isNaN(value) || !isFinite(value)) { sanitized[sanitizedKey] = 0; } else { sanitized[sanitizedKey] = Math.max(-1e10, Math.min(1e10, value)); } } else if (typeof value === 'boolean') { sanitized[sanitizedKey] = Boolean(value); } else { // Convert complex types to safe string representation sanitized[sanitizedKey] = String(value).substring(0, 100); } } return sanitized; } } ``` ### 12.3 Network Security **Secure API Communication:** ```typescript export class SecureAPIClient { private readonly timeout: number = 30000; private readonly maxRetries: number = 3; private readonly userAgent: string = 'PPRE-CLI/1.0.0'; async makeSecureRequest( url: string, options: RequestOptions ): Promise { // Validate URL this.validateURL(url); // Configure secure request options const secureOptions: RequestInit = { ...options, headers: { 'User-Agent': this.userAgent, 'Content-Type': 'application/json', ...options.headers }, timeout: this.timeout, // Enforce HTTPS agent: url.startsWith('https://') ? undefined : new Error('Only HTTPS URLs are allowed') }; // Add rate limiting await this.rateLimiter.waitForAvailability(); // Make request with retries return this.makeRequestWithRetries(url, secureOptions); } private validateURL(url: string): void { try { const parsedURL = new URL(url); // Only allow HTTPS if (parsedURL.protocol !== 'https:') { throw new Error('Only HTTPS URLs are allowed'); } // Validate hostname if (parsedURL.hostname === 'localhost' || parsedURL.hostname.startsWith('127.') || parsedURL.hostname.startsWith('10.') || parsedURL.hostname.startsWith('192.168.')) { throw new Error('Private IP addresses not allowed'); } } catch (error) { throw new Error(`Invalid URL: ${error.message}`); } } private async makeRequestWithRetries( url: string, options: RequestInit ): Promise { let lastError: Error; for (let attempt = 0; attempt < this.maxRetries; attempt++) { try { const response = await fetch(url, options); if (!response.ok) { throw new Error(`HTTP ${response.status}: ${response.statusText}`); } return { success: true, data: await response.json(), status: response.status }; } catch (error: any) { lastError = error; if (attempt < this.maxRetries - 1) { const delay = Math.min(1000 * Math.pow(2, attempt), 10000); await this.sleep(delay); } } } return { success: false, error: lastError!.message, status: 0 }; } } ``` ### 12.4 Data Privacy and Protection **Privacy-Preserving Analytics:** ```typescript export class PrivacyManager { async anonymizeSignals(signals: RiskSignals): Promise { const anonymized = { ...signals }; // Remove or hash potentially identifying information Object.keys(anonymized.signals).forEach(key => { if (this.isPotentiallyIdentifying(key)) { // Hash the value instead of storing raw if (typeof anonymized.signals[key] === 'string') { anonymized.signals[key] = this.hashValue(anonymized.signals[key]); } } }); return anonymized; } private isPotentiallyIdentifying(key: string): boolean { const identifyingPatterns = [ /id$/i, /address$/i, /email$/i, /name$/i, /identifier$/i ]; return identifyingPatterns.some(pattern => pattern.test(key)); } private hashValue(value: string): string { return crypto.createHash('sha256') .update(value + process.env.PPRE_SALT || 'default-salt') .digest('hex') .substring(0, 16); } async createDataRetentionPolicy(): Promise { return { signalData: { retentionPeriod: 30 * 24 * 60 * 60 * 1000, // 30 days anonymizeAfter: 7 * 24 * 60 * 60 * 1000, // 7 days deleteAfter: 90 * 24 * 60 * 60 * 1000 // 90 days }, predictionResults: { retentionPeriod: 60 * 24 * 60 * 60 * 1000, // 60 days aggregateOnly: true }, logs: { retentionPeriod: 14 * 24 * 60 * 60 * 1000 // 14 days } }; } } ``` ## 13. Integration and Deployment ### 13.1 Docker Containerization **Dockerfile:** ```dockerfile FROM node:18-alpine AS builder # Install system dependencies RUN apk add --no-cache python3 make g++ git # Set working directory WORKDIR /app # Copy package files COPY package*.json ./ COPY tsconfig.json ./ # Install dependencies RUN npm ci --only=production # Copy source code COPY src/ ./src/ COPY config/ ./config/ # Build application RUN npm run build # Production image FROM node:18-alpine AS production # Install runtime dependencies RUN apk add --no-cache dumb-init # Create non-root user RUN addgroup -g 1001 -S ppre && \ adduser -S ppre -u 1001 # Set working directory WORKDIR /app # Copy built application COPY --from=builder /app/dist ./dist/ COPY --from=builder /app/node_modules ./node_modules/ COPY --from=builder /app/package.json ./ # Create config directory RUN mkdir -p /app/config && \ chown -R ppre:ppre /app # Switch to non-root user USER ppre # Expose health check HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ CMD node dist/cli.js status || exit 1 # Set entrypoint ENTRYPOINT ["dumb-init", "--"] CMD ["node", "dist/cli.js"] ``` ### 13.2 CI/CD Pipeline Integration **GitHub Actions Workflow:** ```yaml name: PPRE Security Analysis on: pull_request: branches: [main, develop] push: branches: [main] jobs: ppre-analysis: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v3 - name: Setup Node.js uses: actions/setup-node@v3 with: node-version: '18' cache: 'npm' - name: Install PPRE run: | npm install -g @pact/ppre - name: Configure PPRE env: PPRE_API_KEY: ${{ secrets.PPRE_API_KEY }} run: | ppre init --template enterprise --non-interactive - name: Run Risk Analysis run: | ppre predict --input ./signals/ --log 50 > analysis-results.json - name: Check SLA Compliance run: | ppre sla-check --entity "${{ github.repository }}" --history 100 - name: Upload Results uses: actions/upload-artifact@v3 with: name: ppre-analysis path: | analysis-results.json ppre-logs.txt - name: Comment PR if: github.event_name == 'pull_request' uses: actions/github-script@v6 with: script: | const fs = require('fs'); const results = JSON.parse(fs.readFileSync('analysis-results.json', 'utf8')); const comment = `## 🔍 PPRE Risk Analysis Results **Risk Score:** ${results.riskScore.toFixed(3)} **Confidence:** ${results.confidence.toFixed(3)} **Status:** ${results.isHighRisk ? '⚠️ High Risk' : '✅ Low Risk'} ### Risk Factors ${results.analysis.riskFactors.map(factor => `- ${factor}`).join('\n')} `; github.rest.issues.createComment({ issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, body: comment }); ``` ### 13.3 Kubernetes Deployment **Kubernetes Manifests:** ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: ppre-processor labels: app: ppre-processor spec: replicas: 3 selector: matchLabels: app: ppre-processor template: metadata: labels: app: ppre-processor spec: serviceAccountName: ppre-service-account containers: - name: ppre image: pact/ppre:1.0.0 env: - name: PPRE_API_KEY valueFrom: secretKeyRef: name: ppre-secrets key: api-key - name: PPRE_MODEL_PROVIDER value: "openrouter" - name: PPRE_THRESHOLD value: "0.75" resources: requests: memory: "256Mi" cpu: "100m" limits: memory: "512Mi" cpu: "500m" livenessProbe: exec: command: - node - dist/cli.js - status initialDelaySeconds: 30 periodSeconds: 30 readinessProbe: exec: command: - node - dist/cli.js - status initialDelaySeconds: 5 periodSeconds: 10 volumeMounts: - name: config-volume mountPath: /app/config - name: signal-storage mountPath: /app/signals volumes: - name: config-volume configMap: name: ppre-config - name: signal-storage persistentVolumeClaim: claimName: ppre-signals-pvc --- apiVersion: v1 kind: Service metadata: name: ppre-service spec: selector: app: ppre-processor ports: - port: 3000 targetPort: 3000 protocol: TCP type: ClusterIP ``` ### 13.4 Monitoring and Observability **Prometheus Metrics:** ```typescript export class MetricsCollector { private registry: prometheus.Registry; private predictionsTotal: prometheus.Counter; private predictionDuration: prometheus.Histogram; private riskScoreGauge: prometheus.Gauge; private errorCounter: prometheus.Counter; constructor() { this.registry = new prometheus.Registry(); this.predictionsTotal = new prometheus.Counter({ name: 'ppre_predictions_total', help: 'Total number of risk predictions made', labelNames: ['model_provider', 'entity_type', 'risk_level'] }); this.predictionDuration = new prometheus.Histogram({ name: 'ppre_prediction_duration_seconds', help: 'Time spent running predictions', buckets: [0.1, 0.5, 1, 2, 5, 10] }); this.riskScoreGauge = new prometheus.Gauge({ name: 'ppre_current_risk_score', help: 'Current risk score for entities', labelNames: ['entity_id'] }); this.errorCounter = new prometheus.Counter({ name: 'ppre_errors_total', help: 'Total number of errors by type', labelNames: ['error_type', 'component'] }); // Register metrics this.registry.registerMetric(this.predictionsTotal); this.registry.registerMetric(this.predictionDuration); this.registry.registerMetric(this.riskScoreGauge); this.registry.registerMetric(this.errorCounter); } recordPrediction( result: RiskPredictionResult, duration: number, modelProvider: string ): void { const riskLevel = result.isHighRisk ? 'high' : 'low'; this.predictionsTotal.inc({ model_provider: modelProvider, entity_type: 'unknown', // Could be derived from entity ID pattern risk_level: riskLevel }); this.predictionDuration.observe(duration / 1000); // Convert to seconds this.riskScoreGauge.set( { entity_id: result.entity }, result.riskScore ); } recordError(errorType: string, component: string): void { this.errorCounter.inc({ error_type: errorType, component: component }); } async getMetrics(): Promise { return this.registry.metrics(); } } // Grafana Dashboard Configuration export const grafanaDashboard = { dashboard: { title: "PPRE - Predictive Risk Engine", panels: [ { title: "Predictions per Minute", type: "graph", targets: [ { expr: "rate(ppre_predictions_total[1m])", legendFormat: "{{model_provider}} - {{risk_level}}" } ] }, { title: "Average Risk Score", type: "stat", targets: [ { expr: "avg(ppre_current_risk_score)", legendFormat: "Average Risk Score" } ] }, { title: "Prediction Latency", type: "graph", targets: [ { expr: "histogram_quantile(0.95, ppre_prediction_duration_seconds_bucket)", legendFormat: "95th percentile" }, { expr: "histogram_quantile(0.50, ppre_prediction_duration_seconds_bucket)", legendFormat: "50th percentile" } ] }, { title: "Error Rate", type: "graph", targets: [ { expr: "rate(ppre_errors_total[5m])", legendFormat: "{{error_type}} - {{component}}" } ] } ] } }; ``` ## 14. Future Development ### 14.1 Advanced AI Capabilities **Multi-Model Ensemble:** ```typescript export class EnsemblePredictor { private models: Map = new Map(); private weights: Map = new Map(); async addModel(name: string, provider: AIModelProvider, weight: number = 1.0): Promise { this.models.set(name, provider); this.weights.set(name, weight); } async ensemblePredict( signals: RiskSignals, config: PPREConfig ): Promise { const predictions: ModelPrediction[] = []; // Get predictions from all models for (const [name, provider] of this.models) { try { const result = await provider.predict(signals, config); predictions.push({ modelName: name, result, weight: this.weights.get(name) || 1.0 }); } catch (error) { console.warn(`Model ${name} failed: ${error.message}`); } } // Calculate weighted ensemble score const ensembleScore = this.calculateEnsembleScore(predictions); const confidence = this.calculateEnsembleConfidence(predictions); return { ensembleScore, confidence, isHighRisk: ensembleScore >= config.threshold, individualPredictions: predictions, modelAgreement: this.calculateAgreement(predictions), timestamp: new Date().toISOString() }; } private calculateEnsembleScore(predictions: ModelPrediction[]): number { const totalWeight = predictions.reduce((sum, pred) => sum + pred.weight, 0); const weightedSum = predictions.reduce( (sum, pred) => sum + (pred.result.riskScore * pred.weight), 0 ); return weightedSum / totalWeight; } } ``` ### 14.2 Federated Learning **Distributed Model Training:** ```typescript export class FederatedLearningClient { private localModel: TrainedModel | null = null; private federationEndpoint: string; async participateInFederation( localTrainingData: TrainingData[], federationRound: number ): Promise { // Train local model const localUpdate = await this.trainLocalModel(localTrainingData); // Submit local updates to federation const globalUpdate = await this.submitLocalUpdate(localUpdate, federationRound); // Apply global model updates await this.applyGlobalUpdate(globalUpdate); return { localSamples: localTrainingData.length, globalRound: federationRound, modelAccuracy: await this.evaluateModel(), contributionWeight: this.calculateContributionWeight(localTrainingData) }; } private async trainLocalModel(data: TrainingData[]): Promise { const trainer = new MLTrainer(); const model = await trainer.train(data, { algorithm: 'logistic', epochs: 10, learningRate: 0.01 }); return { weights: model.weights, bias: model.bias, sampleCount: data.length, accuracy: model.metrics.training_accuracy }; } private async submitLocalUpdate( update: ModelUpdate, round: number ): Promise { const response = await fetch(`${this.federationEndpoint}/submit-update`, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ round, clientId: this.getClientId(), update: this.encryptUpdate(update) }) }); return response.json(); } } ``` ### 14.3 Quantum-Resistant Security **Post-Quantum Cryptography:** ```typescript export class QuantumResistantSecurity { private latticeKey: LatticeKey; async generateQuantumSafeKeys(): Promise { // Implement lattice-based cryptography const keyPair = await this.generateLatticeKeyPair(); return { publicKey: keyPair.publicKey, privateKey: this.secureStore(keyPair.privateKey), algorithm: 'lattice-dilithium' }; } async encryptSignals( signals: RiskSignals, publicKey: PublicKey ): Promise { // Use quantum-resistant encryption const encryptedData = await this.latticeEncrypt( JSON.stringify(signals), publicKey ); return { encryptedSignals: encryptedData, algorithm: 'lattice-kyber', keyVersion: publicKey.version }; } async signPrediction( prediction: RiskPredictionResult, privateKey: PrivateKey ): Promise { const signature = await this.latticeSign( JSON.stringify(prediction), privateKey ); return { prediction, signature, algorithm: 'lattice-dilithium', timestamp: new Date().toISOString() }; } } ``` ### 14.4 Autonomous Risk Management **Self-Healing Systems:** ```typescript export class AutonomousRiskManager { private decisionEngine: DecisionEngine; private actionExecutor: ActionExecutor; async enableAutonomousMode(config: AutonomousConfig): Promise { // Set up autonomous decision making this.decisionEngine = new DecisionEngine(config.rules); this.actionExecutor = new ActionExecutor(config.actions); // Start monitoring loop setInterval(async () => { await this.autonomousMonitoringCycle(); }, config.monitoringInterval); } private async autonomousMonitoringCycle(): Promise { try { // Collect current risk metrics const metrics = await this.collectRiskMetrics(); // Analyze trends and patterns const analysis = await this.analyzeTrends(metrics); // Make autonomous decisions const decisions = await this.decisionEngine.makeDecisions(analysis); // Execute approved actions for (const decision of decisions) { if (decision.confidence >= 0.9 && decision.risk <= 0.1) { await this.actionExecutor.execute(decision.action); } } } catch (error) { console.error('Autonomous monitoring error:', error); } } async predictSystemBehavior( timeHorizon: number = 24 * 60 * 60 * 1000 // 24 hours ): Promise { const currentState = await this.getCurrentSystemState(); const historicalData = await this.getHistoricalData(timeHorizon * 30); // 30x the prediction window // Use time series analysis to predict future state const prediction = await this.timeSeriesPredict( currentState, historicalData, timeHorizon ); return { predictedRiskLevel: prediction.averageRisk, expectedEvents: prediction.events, recommendedActions: prediction.actions, confidence: prediction.confidence, timeHorizon }; } } ``` ## 15. Conclusion PPRE represents a significant advancement in predictive risk analysis for blockchain and enterprise applications. The comprehensive architecture, advanced AI integration, and extensive feature set make it a powerful tool for proactive risk management. ### 15.1 Key Technical Achievements **Architectural Excellence:** - Modular, extensible design with clear separation of concerns - Professional TypeScript implementation with comprehensive error handling - Cross-platform compatibility with multiple deployment options - Robust configuration management and security features **AI Innovation:** - Multi-provider AI architecture with automatic fallback capabilities - Local machine learning with fine-tuning capabilities - Memory-enhanced predictions using vector similarity search - Confidence scoring and risk threshold management **Enterprise Features:** - Real-time streaming and monitoring capabilities - SLA policy enforcement with automated compliance checking - Comprehensive reporting and analytics - Professional CLI interface with extensive command structure **Performance and Scalability:** - Concurrent processing with resource management - Multi-level caching strategies - Batch processing for high-volume scenarios - Performance monitoring and optimization tools ### 15.2 Impact and Value Proposition **Predictive Capabilities:** - Proactive risk identification before security incidents occur - Historical pattern analysis for improved decision making - Real-time monitoring with immediate alerting - Continuous learning through local model fine-tuning **Operational Efficiency:** - Automated compliance checking reduces manual oversight - Batch processing enables large-scale analysis - Comprehensive reporting streamlines audit processes - CI/CD integration enables continuous security validation **Security Enhancement:** - Multi-layered security with encrypted communications - Secure credential management using system keychains - Input validation and sanitization prevents injection attacks - Privacy-preserving analytics protect sensitive data ### 15.3 Future Outlook PPRE is positioned to evolve with emerging technologies and security challenges: **Advanced AI Integration:** - Ensemble learning for improved accuracy - Federated learning for privacy-preserving collaboration - Quantum machine learning for enhanced pattern recognition - Autonomous decision making with self-healing capabilities **Blockchain Evolution:** - Multi-chain support for diverse ecosystems - Layer 2 integration for scaling solutions - Cross-chain risk assessment capabilities - DeFi protocol-specific analysis modules **Enterprise Expansion:** - Advanced compliance frameworks - Custom rule engine development - White-label deployment options - API service offerings for integration The modular architecture and comprehensive feature set position PPRE as an essential tool in the modern security professional's toolkit, providing the predictive capabilities necessary to stay ahead of emerging threats in the rapidly evolving blockchain landscape. --- ## Appendices ### Appendix A: Command Reference **Complete CLI Command Documentation:** ```bash # Prediction Commands ppre predict --input --entity [options] ppre predict --log --overlay --noMemory # Streaming Commands ppre stream --watch --debounce --autoTrigger ppre stream-memory --watch --debug --once # Training Commands ppre fine-tune --input --output [options] ppre fine-tune --preview --validation-split # Timeline and Analytics ppre timeline --entity --limit --graph ppre simulate --scenario --iterations # SLA and Compliance ppre sla-check --entity --history ppre trigger --entity --score # System Commands ppre status --verbose --check-dependencies ppre init --interactive --template ``` ### Appendix B: Configuration Examples **Development Configuration:** ```json { "version": "1.0.0", "modelProvider": "stubbed", "threshold": 0.5, "signalSources": ["onchain"], "smartContractEndpoint": "http://localhost:3000", "apiKey": "dev-key", "logLevel": "debug", "outputFormat": "json", "timeout": 10000 } ``` **Production Configuration:** ```json { "version": "1.0.0", "modelProvider": "openrouter", "threshold": 0.8, "signalSources": ["onchain", "offchain", "external"], "smartContractEndpoint": "https://api.company.com", "apiKey": "${PPRE_API_KEY}", "logLevel": "info", "outputFormat": "json", "timeout": 30000, "vectorDB": { "provider": "weaviate", "url": "${WEAVIATE_URL}", "apiKey": "${WEAVIATE_API_KEY}" }, "slaSettings": { "strictMode": true, "logViolations": true, "enableSmartContracts": true } } ``` ### Appendix C: API Integration Examples **Signal Processing Example:** ```typescript import { PredictionEngine, loadConfig } from '@pact/ppre'; const config = loadConfig(); const engine = new PredictionEngine(); const signals = { signals: { repo_pushes: 15, audit_flags: 3, login_failures: 2, geo_drift: 1.2, contract_interaction_volume: 250 } }; const result = await engine.runPrediction(signals, config, 'user-123'); console.log(`Risk Score: ${result.riskScore.toFixed(3)}`); console.log(`High Risk: ${result.isHighRisk}`); ``` ### Appendix D: Troubleshooting Guide **Common Issues and Solutions:** 1. **API Key Configuration:** ```bash # Set API key securely ppre init --interactive # Or use environment variable export PPRE_API_KEY=your_key_here ``` 2. **Model Loading Errors:** ```bash # Check model file exists ppre status --verbose # Retrain model if corrupted ppre fine-tune --input data.jsonl --output model.json ``` 3. **Memory Issues:** ```bash # Check resource usage ppre status --check-dependencies # Reduce batch size ppre predict --input data/ --batch-size 5 ``` 4. **Network Connectivity:** ```bash # Test API connectivity curl -H "Authorization: Bearer $PPRE_API_KEY" https://openrouter.ai/api/v1/models # Use local model as fallback ppre init --template local ``` --- **© 2025 PACT Protocol Technical Team. All rights reserved.** For technical support and contributions, visit: [https://github.com/pact-protocol/ppre](https://github.com/pact-protocol/ppre) **Predictive Security, Powered by AI.**