
Module 22 Lesson 1: The AI Security Professional
Defining the role. A deep dive into the day-to-day responsibilities, toolsets, and team dynamics of a professional AI Security Engineer.
13 articles

Defining the role. A deep dive into the day-to-day responsibilities, toolsets, and team dynamics of a professional AI Security Engineer.
Reclaim your disk space. Master the art of Docker housekeeping, from pruning dangling images to managing large log files that eat your server's storage.
Go beyond the basic 'run'. Master the advanced flags for port mapping, resource limits, and auto-restart policies to manage production containers effectively.
Master the commands for managing multi-container stacks. Learn how to view aggregate logs, stop services safely, and rebuild images on the fly.
More power. Learn how to use the --scale flag to run multiple instances of a service and how Docker Compose manages the load balancing internally.
Master the environment split. Learn how to use multiple Compose files to handle development, staging, and production differences without repeating code.
The right tool for the right job. Learn how to maintain separate Docker configurations for development (speed and debugging) and production (security and size).

Cross the finish line. Learn the fundamentals of push-button and fully automated deployments to servers, clouds, and clusters.

The emergency exits. Learn how to perform instant rollbacks when a deployment goes wrong and how to automate recovery using GitLab's built-in tools.

The triage process. Learn the professional workflow for identifying, dismissing, or resolving security vulnerabilities within the GitLab ecosystem.
Safe Deployments. How to use Bedrock Agent Versions and Aliases to update your AI without breaking your production app.
Handling the Peak. Advanced strategies for dealing with Bedrock's rate limits using exponential backoff and request queuing.
Hands-on: Deploying to a remote server. Final operational checks before going live.