Lab Overview

AWS CloudTrail is a service that enables you to log, monitor, and capture API-related events across your AWS infrastructure and most AWS services. Events that CloudTrail captures get delivered to an S3 bucket, and are also available for viewing from the CloudTrail console. CloudTrail captures, creates, modifies, and deletes API calls triggered from the console, API, command line tools, or even other AWS services. Optionally, CloudTrail can be configured to send events to CloudWatch as well (and this Lab does indeed tackle that, too). Typical use cases for CloudTrail, operating with CloudWatch, are monitoring, auditing, and security (governance, compliance, analysis).

It is important to know that CloudTrail is not a replacement for CloudWatch. It simply adds to the monitoring capabilities offered by AWS. Notice the focus for each service:

• CloudTrail focuses on API activity
• CloudWatch focuses on performance monitoring and overall system health

Prerequisites

Although this is a beginner level Lab, it is on the more challenging side for beginners (almost intermediate ;-). You should be familiar with AWS basics including:

• Using the AWS Console
• S3 (bucket and folder creation, uploading files to S3)
• EC2 (creating and launching a basic instance)
• Conceptual understanding of CloudWatch and Simple Notification Service (SNS)

Learning Objectives

Upon completion of this Lab, you will be able to:

• Turn on and configure CloudTrail to capture key events and deliver log files to a specific S3 bucket
• Navigate the S3 bucket structure where CloudTrail logs are stored (as compressed JSON files)
• Generate traffic in order to verify CloudTrail is working
• Use the CloudTrail console to learn more about the events CloudTrail captures
• Configure CloudTrail to send events to CloudWatch
• Create a metric filter and alarm so that you receive a notification when specific events occur in AWS

Lab Environment

After completing the lab instructions the environment will look similar to:

 

Updates 

June 28th, 2019 - Added more S3 permissions to suppress S3 error messages that appear during the lab and improved instructions related to opening log files

January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab