Modern web applications are increasingly exposed to a surge of automated traffic driven by AI crawlers, LLM scrapers, and malicious bots. These automated requests can consume bandwidth, distort analytics, increase infrastructure costs, and degrade application performance. Traditional defense mechanisms are no longer sufficient to handle these evolving threats. This course provides a comprehensive, hands-on approach to building a robust, multi-layered defense system against AI-driven bot traffic using AWS services.

In this course, you will learn how to design and deploy a production-grade infrastructure using Terraform, AWS CloudFront, AWS WAF, Lambda@Edge, and other essential tools. Starting with a simple Flask application, you will progressively build a complete AWS environment, including networking, load balancing, auto-scaling, and edge delivery. You will then enhance this architecture with intelligent traffic routing, bot-aware caching strategies, and degraded content delivery techniques to efficiently manage bot traffic without impacting real users.

The course also emphasizes real-world problem-solving, such as handling sudden bot traffic spikes, preventing cache collisions, and resolving missing asset issues. Additionally, you will analyze traffic data using Amazon Athena to generate actionable insights and implement a strategic bot management policy based on real data.

By the end of this course, you will have the skills to design, deploy, and manage a scalable, secure, and cost-efficient AWS-based system that effectively defends against modern AI bot threats using a data-driven and infrastructure-as-code approach.