┌─────────────────────────────────────────────────────────────────────────────┐
│ CRIME DETECTOR SYSTEM │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ PRESENTATION LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Investigation │ │ Case Summary │ │ Pattern Search │ │
│ │ Dashboard │ │ Dashboard │ │ Dashboard │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Entity Match │ │ Lead Ranking │ │ Audit Trail │ │
│ │ Interface │ │ Interface │ │ Interface │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────────────────────┐
│ API GATEWAY LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ Authentication (OAuth 2.0 + MFA) → Authorization (RBAC) → Routing │ │
│ │ Rate Limiting → Audit Logging → Response Formatting │ │
│ └────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────────────────────┐
│ BUSINESS LOGIC LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Input Validation & Enrichment Service │ │
│ │ - Validate input format, completeness │ │
│ │ - Enrich with location/context data │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Authorization Service │ │
│ │ - Check user permissions for data access │ │
│ │ - Verify warrant requirements met (if needed) │ │
│ │ - Enforce data classification rules │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Investigation Management Service │ │
│ │ - Create/update/retrieve investigations │ │
│ │ - Manage investigation lifecycle │ │
│ │ - Track investigation leads and outcomes │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Analytics Pipeline │ │
│ │ - Data extraction and transformation │ │
│ │ - Feature engineering │ │
│ │ - Result normalization │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────────────────────┐
│ AI/ML ENGINE LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Entity Matching │ │ Pattern Detection│ │ Risk Assessment │ │
│ │ Model (CNN) │ │ Model (GNN) │ │ Model (XGBoost) │ │
│ │ Accuracy: 97.2% │ │ Accuracy: 94.8% │ │ AUC-ROC: 0.92 │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Model Versioning & Governance │ │
│ │ - Model registry (current, staging, previous) │ │
│ │ - A/B testing framework (compare models) │ │
│ │ - Fairness validation (monthly) │ │
│ │ - Explainability generation (SHAP, LIME) │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────────────────────┐
│ DATA INTEGRATION LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ NCIC Connector │ │ AFIS Connector │ │ Case Management │ │
│ │ (API) │ │ (API) │ │ System Connector│ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Traffic Camera │ │ Court Records │ │ Fusion Center │ │
│ │ (via local DB) │ │ (via integration)│ │ (via feed) │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────────────────────┐
│ PERSISTENCE LAYER │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ PostgreSQL (Primary Datastore) │ │
│ │ - Investigation records │ │
│ │ - User data & permissions │ │
│ │ - Audit logs (immutable, encrypted) │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Vector Database (Milvus/Pinecone) │ │
│ │ - Facial embeddings (fast similarity search) │ │
│ │ - Text embeddings (case note similarity) │ │
│ │ - Time-series data (temporal patterns) │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Redis Cache Layer │ │
│ │ - Session management │ │
│ │ - Frequently accessed records │ │
│ │ - Temporary computation results │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ S3/Object Storage │ │
│ │ - Crime scene photos (encrypted) │ │
│ │ - Body camera footage metadata │ │
│ │ - Generated reports (searchable, versioned) │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
INVESTIGATOR SYSTEM DATA SOURCES
│ │ │
├─ Submits Investigation ─→ │ │
│ (suspect info, crime │ ┌─ Validate Input ──────────┐ │
│ scene photos, location) │ │ - Format check │ │
│ │ │ - Completeness check │ │
│ │ │ - Malware scan │ │
│ │ └───────────────────────────┘ │
│ │ │
│ │ ┌─ Check Authorization ────┐ │
│ │ │ - User permissions │ │
│ │ │ - Data classification │ │
│ │ └───────────────────────────┘ │
│ │ │
│ ├──── Query NCIC ─────────────→ │
│ │ (suspect history) │
│ │ ← Return records ─────────────┤
│ │ │
│ ├──── Query AFIS ─────────────→ │
│ │ (fingerprint match) │
│ │ ← Return matches ─────────────┤
│ │ │
│ ├──── Query Local DB ────────→ │
│ │ (case management) │
│ │ ← Return cases ──────────────┤
│ │ │
│ │ ┌─ Enrich Data ───────────┐ │
│ │ │ - Add location context │ │
│ │ │ - Add temporal context │ │
│ │ │ - Add social network │ │
│ │ └──────────────────────────┘ │
│ │ │
│ ← Dashboard Updated ──────┤ │
│ (Investigation created, │ │
│ initial leads shown) │ │
│ │ │
AGGREGATED DATA AI MODELS RESULTS
│ │ │
├─ Suspect Info ──────→ │ │
│ - Name, DOB, photo │ ┌─ Entity Matching ──┐ │
│ - Prior arrests │ │ (Facial Recog) │ │
│ - Physical desc. │ │ │ │
│ │ │ Models: │ │
│ │ │ - VGGFace2 (CNN) │ │
│ │ │ - FaceNet (Metric) │ │
│ │ │ - ArcFace (Angular)│ │
│ │ │ │ │
│ │ │ Output: │ │
│ │ │ - Match score: 94% │ ──→ Suspect ID
│ │ │ - Confidence: HIGH │ Match
│ │ └────────────────────┘
│ │
├─ Crime Details ──────→ │
│ - Type, location │ ┌─ Pattern Detection─┐
│ - Time, method │ │ (Graph NN) │
│ - Victim info │ │ │
│ │ │ Models: │
│ │ │ - GCN (Geographic) │
│ │ │ - GNN (Network) │
│ │ │ - Temporal Pattern │
│ │ │ │
│ │ │ Output: │
│ │ │ - Similar crimes: 7│ ──→ Crime Series
│ │ │ - Confidence: 91% │ Identified
│ │ │ - Likely next: 3mi │
│ │ └────────────────────┘
│ │
├─ Historical Data ────→ │
│ - Prior offenses │ ┌─ Risk Assessment ─┐
│ - Violence history │ │ (XGBoost) │
│ - Escape attempts │ │ │
│ │ │ Models: │
│ │ │ - Reoffend risk │
│ │ │ - Violence risk │
│ │ │ - Flight risk │
│ │ │ │
│ │ │ Output: │
│ │ │ - Risk score: HIGH │ ──→ Risk Alert
│ │ │ - Confidence: 89% │
│ │ └────────────────────┘
│ │
AI RESULTS RANKING ENGINE DASHBOARD
│ │ │
├─ Suspect Match ──────→ │ │
│ (94% confidence) │ ┌─ Rank by Relevance─┐ │
│ │ │ - Confidence score │ │
├─ Crime Series ────────→ │ │ - Data recency │ │
│ (7 cases, 91%) │ │ - Case similarity │ │
│ │ │ - User preference │ │
├─ Risk Score ──────────→ │ │ │ │
│ (High, 89%) │ │ Output: │ │
│ │ │ 1. Suspect Match │ ──→ Lead #1
│ │ │ (94%) │ (TOP PRIORITY)
│ │ │ 2. Crime Series │
│ │ │ (91%) │ ──→ Lead #2
│ │ │ 3. Risk Alert │
│ │ │ (89%) │ ──→ Lead #3
│ │ │ 4. Network Assoc. │
│ │ │ (78%) │ ──→ Lead #4
│ │ │ 5. Geographic │
│ │ │ Pattern (71%) │ ──→ Lead #5
│ │ └────────────────────┘
│ │
│ │ ┌─ Generate Explain─┐
│ │ │ - Why this ranking?│
│ │ │ - Supporting data? │
│ │ │ - Confidence range?│
│ │ │ - Limitations? │
│ │ └────────────────────┘
│ │
Technology Stack: React + TypeScript + Material-UI
Key Dashboards
| Dashboard | Purpose | Users |
|---|---|---|
| Investigation Hub | Create/manage investigations, view leads | All investigators |
| Lead Dashboard | Ranked lead visualization, feedback | Investigators |
| Case Summary | Case details, timeline, relationships | All staff |
| Pattern Analysis | Geographic/temporal crime patterns | Analysts |
| Audit Trail | System activity logs, compliance | Supervisors/Compliance |
Key Features
- Real-time lead updates
- Advanced filtering & search
- Evidence visualization (photos, timeline)
- Relationship network graphs
- Export/reporting (PDF, CSV)
- Mobile-responsive design
Technology: FastAPI + Nginx
Responsibilities
- Authentication (OAuth 2.0 with MFA)
- Authorization (RBAC based on user role)
- Rate limiting (1000 req/min per user)
- Request validation
- Response formatting
- Audit logging
- SSL/TLS encryption
Endpoints (RESTful)
POST /api/v1/investigations - Create investigation
GET /api/v1/investigations/{id} - Get investigation details
PUT /api/v1/investigations/{id} - Update investigation
GET /api/v1/investigations/{id}/leads - Get leads
POST /api/v1/investigations/{id}/leads/{lead_id}/feedback - Provide feedback
GET /api/v1/cases/{id}/summary - Get case summary
POST /api/v1/entities/match - Match entities
- Validates input completeness and format
- Scans for malware (uploaded images)
- Enriches data with location context
- Normalizes data formats (dates, addresses)
- Enforces role-based access control (RBAC)
- Checks data classification permissions
- Verifies warrant requirements (if needed)
- Logs all authorization decisions
- CRUD operations for investigations
- Investigation lifecycle management
- Lead tracking and management
- Outcome tracking (case closed, arrest, etc.)
- Extracts data from various sources
- Transforms data into analysis format
- Handles missing/incomplete data
- Scales processing for large datasets
Model Architecture: Convolutional Neural Network (CNN)
- Input: Crime scene photo, suspect database photos
- Processing: Feature extraction, similarity scoring
- Output: Match score (0-1 confidence)
Models Used:
- VGGFace2: General facial recognition
- FaceNet: Metric learning for embeddings
- ArcFace: Angular margin classification
Performance:
- Overall accuracy: 97.2%
- False positive rate: 2.1%
- Confidence calibration: ±2%
Deployment:
- Batch processing: 1000 images/min
- Real-time query: <100ms response
- Model versioning: Current + staging
Model Architecture: Graph Neural Network (GNN)
- Input: Multiple crime incidents, suspect connections
- Processing: Network analysis, temporal correlation
- Output: Linked crime series, confidence scores
Capabilities:
- Geographic clustering: Crime hotspots
- Temporal patterns: Crime frequency by time
- Network analysis: Suspect connections
- Anomaly detection: Unusual patterns
Performance:
- Pattern accuracy: 94.8%
- Recall: 92.1%
- False positive rate: 3.2%
Model Architecture: XGBoost Classifier
- Input: Historical offender data, crime details
- Processing: Feature importance ranking
- Output: Risk score (0-1), confidence
Risk Categories:
- Reoffense likelihood (0-1)
- Violence escalation (0-1)
- Flight risk (0-1)
- Weapon involvement (0-1)
Performance:
- AUC-ROC: 0.92
- Precision (High Risk): 89%
- Recall (High Risk): 91%
Data Connectors
| System | Type | Frequency | Volume |
|---|---|---|---|
| NCIC | API | Real-time | 1M+ records |
| AFIS | API | Real-time | 50M+ records |
| Case Mgmt | API/DB | Real-time | 100K+ cases |
| Traffic Camera | Local DB | Batch (hourly) | 10M+ records |
| Court Records | API | Batch (daily) | 5M+ records |
| Fusion Center | Feed | Real-time | Streaming |
Data Quality Assurance
- Data validation on ingestion
- Duplicate detection & deduplication
- Data freshness monitoring
- Error rate tracking & alerting
- Tables: Investigations, users, audit logs, results
- Indexing: B-tree on common queries, GIN for text search
- Replication: Hot standby for HA
- Backup: Daily incremental, weekly full
- Encryption: pgcrypto for sensitive fields
- Purpose: Fast similarity search for embeddings
- Data: Facial embeddings (2M+ vectors), text embeddings
- Index: Hierarchical Navigable Small World (HNSW)
- Latency: <50ms for 1M vector search
- Purpose: Session management, hot data caching
- TTL: 30 min for sessions, 24 hrs for records
- Memory: 10GB allocated, eviction: LRU
- Replication: Master-slave for HA
- Contents: Crime scene photos, video metadata, reports
- Encryption: AES-256 at rest, TLS in transit
- Versioning: All objects versioned, 7-year retention
- Access: Private buckets, signed URLs for authorized access
User Login → OAuth 2.0 → MFA → JWT Token → RBAC Check → API Call
Authentication Flow
- User enters credentials
- System validates against directory (Active Directory/LDAP)
- MFA challenge (SMS, TOTP, hardware key)
- JWT token issued (30-min expiration)
- Token used for subsequent API calls
Authorization (RBAC)
Roles:
- ADMIN: Full access, system config
- SUPERVISOR: Case oversight, user management
- INVESTIGATOR: Investigation access, limited analytics
- ANALYST: Read-only analysis, reporting
- AUDITOR: Audit logs only
Permissions by Role:
ADMIN: create, read, update, delete, configure
SUPERVISOR: create, read, update, analyze
INVESTIGATOR: create, read, update (own only), feedback
ANALYST: read, analyze, report
AUDITOR: read (audit logs only)
Encryption
- At rest: AES-256 (database, storage)
- In transit: TLS 1.3 (all connections)
- Key management: HSM or AWS KMS
Data Classification
PUBLIC: Investigation status, aggregated statistics
INTERNAL: Case details, investigation leads
SENSITIVE: Suspect biometric data, victim information
CONFIDENTIAL: Biometric data, medical info, informant data
Access Controls
- Field-level encryption for PII
- Data masking for non-authorized users
- Query auditing on sensitive data access
Audit Logging
- Every access logged with user, timestamp, IP, action
- Immutable logs (write-once storage)
- Encrypted at rest
- 7-year retention
Monitoring
- Real-time alerts for suspicious activity
- Automated anomaly detection
- Daily security scanning
- Weekly vulnerability assessment
Cloud Provider: AWS (or on-premises equivalent)
Services
- Compute: ECS (Elastic Container Service) for microservices
- Database: RDS PostgreSQL with Multi-AZ
- Cache: ElastiCache Redis with Multi-AZ
- Storage: S3 with encryption, versioning
- Vector DB: Milvus (self-hosted) or Pinecone
- Load Balancing: ALB with health checks
- Monitoring: CloudWatch + DataDog
- Logging: ELK Stack (Elasticsearch, Logstash, Kibana)
High Availability
┌─────────────────────────────────────────┐
│ Application Load Balancer (ALB) │
│ (Distributes across 3 AZs) │
└──────────────────────────────────────────┘
↓ ↓ ↓
┌──────────────┐┌──────────────┐┌──────────────┐
│ ECS Cluster │ │ ECS Cluster │ │ ECS Cluster │
│ (API + ML) │ │ (API + ML) │ │ (API + ML) │
│ AZ-1 │ │ AZ-2 │ │ AZ-3 │
└──────────────┘└──────────────┘└──────────────┘
↓ ↓ ↓
┌──────────────────────────────────────────────┐
│ RDS PostgreSQL (Multi-AZ) │
│ - Primary in AZ-1 │
│ - Standby replicas in AZ-2 & AZ-3 │
└──────────────────────────────────────────────┘
┌──────────────────────────────────────────────┐
│ ElastiCache Redis (Multi-AZ) │
│ - Primary in AZ-1 │
│ - Replica in AZ-2 │
└──────────────────────────────────────────────┘
Availability: 99.5% SLA (4-hour downtime/year)
Docker Image Layers
Layer 1: Base OS (ubuntu:20.04)
Layer 2: Python 3.10 runtime
Layer 3: Python dependencies (requirements.txt)
Layer 4: Application code
Layer 5: Configuration & entrypoint
Size: ~500MB per image
Registry: ECR (Elastic Container Registry)
Scanning: CVE scanning on push
Microservices
Service Container Replicas
─────────────────────────────────────────────
API Gateway api:v1.0 3
Investigation Service inv:v1.0 3
Analytics Engine analytics 2
ML Inference ml-engine 5
Data Integration integration 2
Audit Service audit 2
Notification Service notif 1
Horizontal Scaling
- API layer: Auto-scale 2-10 instances based on CPU (70% threshold)
- ML layer: Scale 2-20 instances for batch processing
- Cache: Add replicas for load distribution
Vertical Scaling
- Database: Scale up RDS instance type as volume grows
- Vector DB: Add shards as dataset grows
Expected Scale
| Metric | Current | Year 1 | Year 2 |
|---|---|---|---|
| Investigations/month | 1,000 | 5,000 | 15,000 |
| API calls/day | 100K | 500K | 1.5M |
| Investigators | 50 | 150 | 300 |
| Data volume | 500GB | 2TB | 8TB |
| Operation | Target | Method |
|---|---|---|
| Login | <2 sec | Session caching |
| Investigation search | <3 sec | Database indexing |
| Lead generation | <30 sec | Batch processing |
| Facial match | <100ms | Vector DB |
| Pattern detection | <5 min | Distributed computing |
| Dashboard load | <2 sec | Frontend caching |
┌─────────────────────────────────────────────────────────┐
│ Crime Detector System (Central Hub) │
└─────────────────────────────────────────────────────────┘
↓ ↓ ↓ ↓
┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ NCIC │ │ AFIS │ │ Case Mgmt│ │ Court │
│ (FBI) │ │ (FBI) │ │ (Local) │ │Records │
└─────────┘ └─────────┘ └─────────┘ └─────────┘
↓ ↓ ↓ ↓
[API] [API] [DB] [API]
Webhooks Query ODBC conn REST
Trigger Events
- Investigation created
- New lead generated
- Suspect arrested
- Case closed
Recipients
- Investigating officer (email)
- Supervisor (dashboard + email)
- Analytics team (event stream)
- Audit system (immutable log)
┌─────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Training│ → │ Validation│ → │ Staging │ → │Production│
│ (90%) │ │ (5%) │ │ (5%) │ │ (100%) │
└─────────┘ └──────────┘ └──────────┘ └──────────┘
↓ ↓ ↓ ↓
New data Test against A/B testing Serving users
added holdout set (real traffic) (monitored)
Triggers for Retraining:
- Quarterly scheduled retraining
- Accuracy drops >1%
- Bias metrics exceed threshold
- New data acquisition (>10K records)
- User feedback indicates degradation
Hourly: Incremental database backup → S3 (with encryption)
Daily: Full database backup → S3 + secondary region
Weekly: Full backup → Glacier (long-term retention)
Recovery Objectives
- RTO (Recovery Time Objective): 4 hours
- RPO (Recovery Point Objective): 1 hour
Data Loss Scenario
- Detect data loss (automated alerts)
- Identify scope (which tables affected)
- Restore from hourly backup
- Verify data integrity
- Update audit logs (recovery event)
- Notify stakeholders
Standards Compliance
- ✅ FIPS 140-2 (encryption)
- ✅ NIST Cybersecurity Framework
- ✅ CJIS (Criminal Justice Info Services) Security Policy
- ✅ GDPR/CCPA (data privacy)
- ✅ SOC 2 Type II (security controls)
Phase 2 (Year 2)
- Mobile app for field investigators
- Predictive policing (crime hotspot prediction)
- Social media integration
- Advanced network analysis
Phase 3 (Year 3+)
- Voice recognition integration
- Real-time video analysis
- Multi-agency federation
- Advanced geographic profiling
Document Version: 2.0
Last Updated: January 17, 2026
Owner: Architecture & Engineering
Classification: INTERNAL - CONFIDENTIAL