9 Deployment and Security

 

This chapter covers

  • Unique security threats including prompt injection, model manipulation, and API abuse
  • Secure API key management and rate-limiting strategies for AI endpoints
  • Data privacy compliance (GDPR, CCPA) and sensitive data handling techniques
  • Monitoring and observability practices tailored for AI workflows
  • Deployment strategies for hosted platforms and self-hosted environments
  • Detecting prompt injections and implementing privacy controls on real-world cases

Deploying AI applications introduces risks that traditional software also faces. A single misconfigured API endpoint could expose sensitive user data to adversarial prompts, while unmonitored LLM usage might lead to astronomical costs or regulatory violations. This is no different than using any other paid service provider where you have to abide by their code of conduct.

Consider again some of their unique challenges of the tools we’ve been utilizing:

9.1 Building a secure foundation with input validation, rate limits, and middleware

9.1.1 Input validation

9.1.2 Security middleware layer

9.1.3 Example security control: Rate limiting

9.2 Building a core security & data protection pipeline

9.3 Setting up Authentication and Authorization

9.3.1 Simple authentication with Clerk.js and Next.js

9.4 API Key and secrets management

9.4.1 Understanding Next.js environment variables

9.5 Data protection and compliance

9.6 Deployment Considerations for AI Web Applications

9.6.1 Deployment options

9.6.2 Production deployment checklist

9.6.3 Example Deployment to Vercel

9.6.4 Next steps

9.7 Summary