DevSecOps on AWS: Defend Against LLM Scrapers & Bot Traffic

What you'll learn

• How CloudFront caching works and how to build intelligent, bot-aware delivery flows

• How to implement degraded-content / traffic-splitting strategies using CloudFront, Lambda@Edge, and S3

• How to separate cache for bots vs humans using CloudFront Functions

• How to eliminate “missing assets” issues using immutable asset deployments

• How to deploy and tune CloudFront Origin Shield

• How to analyze CloudFront logs using Athena for bot and traffic insights

• How to configure AWS WAF to be defensive against bots DoS (IP sets, GEO rules, rate rules, managed rules)

• How to use JA4 fingerprinting for advanced rate-limit funnels

• How AWS WAF Bot Control really works (COMMON vs TARGETED modes)

• How to integrate the WAF client-side SDK to unlock advanced detections

• How to read and interpret Bot Control dashboards and labels

• How to build a real Bot Identification Report in Athena

• How to design, validate, and deploy a complete bot strategy (allow, block, degrade)

Requirements

• An active AWS account

• A domain name to use with CloudFront

• Understanding of HTTP, web apps, or APIs

• Good Terraform knowledge

• IT IS NOT THE COURSE FOR BEGINNERS

  
  

Short description

This course teaches you how to survive — and win — in the new era of AI bots, crawlers, scrapers, and automated traffic.

Today bots consume an enormous portion of API, web, and CDN traffic.
They cost money, distort analytics, break cache logic, and overload your application.
And traditional protections are no longer enough.

This course gives you a complete, practical, battle-tested system to handle AI bots with intelligence, not brute force.

Section 1 — Understanding the New Threat Landscape

We begin from the strategic level:

Why AI bots became a real business threat, what their objectives are, how bot traffic harms your infrastructure, and how to think about long-term defense.

You will also get a high-level architecture overview — the big picture of CloudFront, WAF, degraded content, and routing logic.

Section 2 — Flask Test Application & Terraform Preparations

Before we defend anything, we need something to protect.

You will create a tiny Flask API app, run it locally, understand its behavior, then prepare Terraform, AWS profiles, and ECR to deploy it later in the cloud.

Section 3 — Full Application Deployment Using Terraform

This is the heart of the infrastructure setup.

You will:

• Build networking components

• Delegate a domain

• Configure ACM

• Build ALB

• Deploy EC2 using AutoScaling

• Attach EC2 to ALB

• Configure CloudFront

• Integrate WAF

• Explore the AWS Console and learn to debug application behavior

This creates the full “lab environment” used for all bot routing experiments in the later sections.

Section 4 — Autoscaling & Real AI Bot Cost Surprises

We explore what happens when bots hit your infrastructure at scale.

You will see real examples of traffic spikes, CPU burns, cost explosions — and learn why AI bots require a different approach than traditional crawlers.

We also discuss AWS Fargate and show a real commercial example of bot impact.

Section 5 — Intelligent Traffic Routing with CloudFront

This is where the course becomes truly unique.

You will learn:

• How CloudFront actually works at request level

• How to build a degraded content strategy — lightweight static content for bots

• How to route bots with Lambda@Edge

• How to tag bots using CloudFront Functions

• How caching issues arise in real deployments and how to fix them

• How to handle static assets, versioning, origin shield, and inline assets

• How to make CloudFront fully bot-aware and resilient

By the end, your CloudFront distribution becomes a smart, bot-sensitive traffic router.

Section 6 — AWS WAF: Protecting Against AI Crawlers & Automated Bots

We go deep into WAF from both defensive and analytical perspectives:

• WAF basics and how it actually inspects traffic

• Custom black & white lists in the context of AI bots

• Geo-based filtering

• Athena quick start using WAF logs

• JA4 fingerprinting & statistical detection

• URL-scoped granular rate rules

• Reputation-based managed rules

• Intelligent Bot Mitigation theory

• Turning on Bot Control (COMMON + TARGETED)

• Integrating Bot Control SDK

• Reading Bot Control metrics and dashboards

• Understanding bot categories and deducing which real bots sit behind them

This section connects CloudFront & WAF into a unified defensive system.

Section 7 — Strategic Bot Policy & AI/Bot Traffic Analysis Using Athena

This is the analytical and strategic peak of the course.

You will learn how to extract real bot traffic from your logs, build a complete Bot Identification Report, and use it to craft a concrete bot defense strategy.

• Generating the Bot Identification Report using Athena + real production data samples

• Strategic bot policy implementation — part 1 (Terraform logic, CloudFront routing, WAF integration)

• Strategic bot policy implementation — part 2 (finalizing routing, degraded content, block rules)

• Final course summary + key takeaways + next steps

We finish with a clear framework that you can apply in any real-world environment — cloud or on-premise.

Who this course is for

Anyone responsible for web applications, API performance, cloud security, or cost optimization:

• Software Engineers

• DevOps & SRE

• Cloud Architects

• Security Engineers

• CTOs, Tech Leads, Startups

• Anyone curious about real-world bot defenses and traffic control

If you want a practical, battle-tested, deep-technical, and fully reproducible defense methodology against AI bots — this course is for you.