AU245 – Configuration as Code for Enterprise Automation with Red Hat Ansible Automation Platform — Lesson 4: Drift Detection, Security, Governance, Audit Evidence, and Improvement

Learning Objectives

• Operate, diagnose, document, recover, and improve drift detection, security, and governance.
• Apply audit evidence and improvement while preserving reviewable technical evidence.
• Recognize and correct the condition where the technical result is restored but monitoring, rollback, evidence, ownership, or handover remains incomplete.

Detailed Topics

• drift detection: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• security: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• governance: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• audit evidence: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• improvement: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.

Instructor Demonstration

The instructor demonstrates drift detection, security, governance, audit evidence, and improvement by tracing architecture, commands, configuration, system messages, and evidence. The walkthrough tests how the technical result is restored but monitoring, rollback, evidence, ownership, or handover remains incomplete and explains how to isolate the cause without masking related issues.

Hands-On Exercise

Conduct an operational acceptance review for Drift Detection, Security, Governance, Audit Evidence, and Improvement and add the verified result to the configuration-as-code repository, promotion workflow, automated validation, and governance checklist.

1. Confirm scope, versions, access, dependencies, assumptions, and acceptance criteria.
2. Perform the workflow while recording commands, configuration, output, events, and decisions.
3. Test the normal path and one course-specific exception or boundary condition.
4. Retest the correction and document rollback, ownership, and follow-up actions.

Scenario Analysis

• Identify which dependency most strongly influences drift detection in the stated scenario.
• Compare two approaches for audit evidence and explain the security, reliability, performance, and support trade-offs.
• Decide what evidence must be retained in the configuration-as-code repository, promotion workflow, automated validation, and governance checklist before operational acceptance.

Troubleshooting Guidance

Begin with the first observable difference from the expected result. Check monitoring, troubleshooting, rollback, ownership, evidence, and handover separately, then correlate logs, events, commands, metrics, and configuration history. Treat the assumption that the technical result is restored but monitoring, rollback, evidence, ownership, or handover remains incomplete as a hypothesis until a reproducible retest confirms the cause.

Validation Checklist

• The required monitoring, troubleshooting, rollback, ownership, evidence, and handover were checked and recorded.
• The normal path and a course-specific exception were tested.
• The configuration-as-code repository, promotion workflow, automated validation, and governance checklist links conclusions to commands, configuration, logs, metrics, or other objective evidence.
• Corrective actions, rollback, ownership, and handover notes are complete.

Lesson Outcome

Learners can apply Drift Detection, Security, Governance, Audit Evidence, and Improvement, explain the observed platform behavior, and support the result with a reviewable configuration-as-code repository, promotion workflow, automated validation, and governance checklist.