CopyGuard AI: Code Detection on AWS

How I built an enterprise-grade serverless platform to detect AI-generated code using Amazon Bedrock, complete with monitoring, security, and DevOps best practices.

The Problem: Detecting AI-Generated Code in the Wild

With the rise of AI coding assistants like GitHub Copilot, ChatGPT, and Claude, distinguishing between human-written and AI-generated code has become increasingly important for educational institutions, code review processes, and intellectual property protection.

That's why I built CopyGuard - a sophisticated, production-ready platform that leverages Amazon Bedrock's Claude v2 model to intelligently analyze code snippets and determine their origin with remarkable accuracy.

What Makes CopyGuard Different?

Unlike simple rule-based detectors, CopyGuard is built with enterprise-grade architecture and production-ready practices:

🧠 AI-Powered Intelligence: Uses Amazon Bedrock's Claude v2 for nuanced code analysis
☁️ Serverless & Scalable: Auto-scaling infrastructure that handles traffic spikes
🔒 Enterprise Security: Proper IAM roles, API authentication, and access controls
📊 Production Monitoring: Real-time metrics, alarms, and Grafana dashboards
🌍 Global Performance: CloudFront CDN for worldwide low-latency access

The Architecture: Built for Scale

The Technology Stack

Infrastructure as Code

I chose Terraform for infrastructure management, ensuring:

Reproducible deployments
Version-controlled infrastructure
Modular, reusable components
Random suffixes for resource uniqueness

AI/ML Integration

Amazon Bedrock with Claude v2 provides:

High-accuracy code analysis
Natural language processing capabilities
Serverless AI model access
Cost-effective per-request pricing

Monitoring & Observability

CloudWatch and Grafana deliver:

Custom metrics for confidence scores
Real-time performance monitoring
Error threshold alerting
60-day log retention for compliance

Deep Dive: The Lambda Function

The heart of CopyGuard is a sophisticated Lambda function that handles:

Intelligent Response Parsing

# Advanced regex patterns for confidence extraction
confidence_patterns = [
    r'(\d+(?:\.\d+)?)%?\s*confidence',
    r'confidence.*?(\d+(?:\.\d+)?)',
    r'(\d+(?:\.\d+)?)\s*percent'
]

Custom CloudWatch Metrics

ConfidenceScore: AI detection confidence percentage
IsAIGenerated: Binary classification results
LatencyMs: Response time performance
Lambda Errors: Automated error alerting

S3 Integration

Every analysis result is automatically stored in S3 with:

Timestamp-based organization
JSON format for easy querying
Complete audit trail for compliance

Security: Built with Zero Trust in Mind

API Security

API key authentication for all requests
CORS configuration for browser security
Rate limiting capabilities (future enhancement)

AWS Security Best Practices

IAM roles with least privilege principle
S3 bucket policies for access control
Encrypted data in transit and at rest
No sensitive data in CloudWatch logs

Data Protection

Server-side encryption on S3
VPC endpoints for private communication (optional)
CloudTrail logging for audit compliance

Real-World Performance

Response Time Targets

Average latency: <2 seconds
P95 latency: <5 seconds
Timeout: 30 seconds maximum

Cost Analysis (Monthly)

For 1,000 requests:

Lambda: ~$0.20
API Gateway: ~$3.50
Bedrock: ~$15.00
S3: ~$0.05
CloudWatch: ~$2.00
CloudFront: ~$1.00
Total: ~$22/month

Cost per request: ~$0.016 - incredibly cost-effective for AI-powered analysis!

[Screenshot: AWS Cost Explorer showing actual usage costs]

The User Experience

Simple API Integration

curl -X POST https://your-api-endpoint/detect \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-secret-key" \
  -d '{
    "code": "def fibonacci(n): return n if n <= 1 else fibonacci(n-1) + fibonacci(n-2)"
  }'

Rich Response Format

{
  "result": {
    "label": "Human-written",
    "confidence": 85,
    "raw": "This code appears to be human-written with 85% confidence..."
  },
  "s3_key": "results/2024-01-15T10:30:00.000Z_abc123.json"
}

Deployment: From Zero to Production

One-Command Deployment

# Configure your environment
cp terraform.tfvars.example terraform.tfvars

# Deploy everything
terraform init
terraform plan  
terraform apply

What Gets Created

15+ AWS resources provisioned automatically
Complete monitoring stack configured
Security policies applied
Frontend deployed and accessible globally

Monitoring in Action

CloudWatch Dashboards

Real-time visibility into:

Request volume and patterns
Error rates and types
Performance metrics
Cost optimization opportunities

Grafana Integration

Advanced visualizations for:

Confidence score distributions
Geographic usage patterns
Performance trends over time
Custom business metrics

Lessons Learned & Best Practices

DevOps Excellence

Infrastructure as Code: Every resource version-controlled
Monitoring First: Observability built in from day one
Security by Design: Least privilege throughout the stack
Cost Optimization: Serverless architecture minimizes waste

Technical Insights

Regex Optimization: Performance matters for real-time analysis
Error Handling: Robust exception management prevents failures
Connection Pooling: Reduced cold start impact
Modular Design: Terraform modules enable reusability

Production Readiness

60-day log retention: Compliance and debugging capability
Automated alerting: Proactive issue detection
Complete audit trail: Every analysis tracked in S3
Performance monitoring: Sub-2-second response times

The Road Ahead: Future Enhancements

Technical Roadmap

Multi-model Support: GPT-4, Llama 2, Claude 3 integration
Batch Processing: Analyze entire repositories
CI/CD Pipeline: GitHub Actions for automated deployment
Advanced Analytics: ML-powered usage insights

Business Features

User Authentication: AWS Cognito integration
Usage Analytics: Detailed reporting dashboard
API Versioning: Backward compatibility
Webhook Support: Real-time notifications

Key Takeaways

Building CopyGuard taught me valuable lessons about creating production-ready AI applications:

Start with Architecture: Proper planning prevents poor performance
Security First: Build security in, don't bolt it on
Monitor Everything: You can't improve what you don't measure
Cost Awareness: Serverless doesn't mean cost-free
User Experience: Great APIs need great documentation

Try CopyGuard Today

The complete source code, infrastructure definitions, and deployment instructions are available on GitHub. Whether you're building similar AI-powered tools or learning about AWS serverless architecture, CopyGuard demonstrates production-ready patterns you can apply to your own projects.

🔗 Project Repository: github.com/Yashmaini30/CopyGuard

Getting Started

Clone the repository
Configure your AWS credentials
Run terraform apply
Start analyzing code!

Note: All AWS resources used in this project were terminated after testing to avoid unnecessary costs and ensure account security.

About the Author

Yash Maini is an aspiring cloud and MLOps engineer with a passion for building scalable AI applications. This project showcases my work in serverless architecture and AWS Bedrock. I’m actively seeking roles in AI/ML engineering, MLOps, or cloud development — let’s connect!

📧 Contact: mainiyash2@gmail.com
🔗 GitHub: @Yashmaini30

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Comments & Discussion

What challenges have you faced building AI-powered applications? Share your experiences and questions about serverless architecture, AWS Bedrock, or production monitoring in the comments.

Tags: #AWS #Serverless #AI #MachineLearning #DevOps #Terraform #CloudArchitecture #AmazonBedrock #Production #Monitoring

Building CopyGuard: A Production-Ready AI Code Detection Platform on AWS