Change Management Technology

[All Actors]
1. Identify and document potential risks associated with each proposed change, including operational, security, performance, and compliance-related risks.

2. Define the likelihood and impact of each risk, and specify mitigation steps or fallback procedures (e.g., rollback plans, alternative configurations).

3. Include risk evaluation as part of the formal change review and approval workflow.

4. Implement monitoring and logging mechanisms to track the real-time impact of deployed changes and detect early signs of risk materialization.

5. Assign clear ownership and escalation paths for each identified risk, ensuring that designated stakeholders are prepared to intervene if risk thresholds are crossed.

6. Regularly review and update the risk registry to capture lessons learned from previous changes and incorporate them into future change planning.

1. Inquiring with Control Owners

1.1 Interview change management leadership to understand how changes to AI-specific infrastructure assets are managed. This includes GPU/TPU/accelerator updates, AI-optimized server and storage configurations, high-bandwidth network fabric modifications, and distributed compute environment updates. Discuss the workflows for hardware introductions, driver and firmware updates, resource allocation changes, and infrastructure optimizations. Examine how risks are assessed, particularly regarding multi-tenant isolation, AI workload performance impacts, hardware-software compatibility, and capacity planning.

2. Inspecting Records and Documents

2.1 Confirm the use of enterprise change management systems such as ServiceNow, Jira, or BMC Remedy for tracking infrastructure changes. Validate that these systems are integrated into the broader governance and approval framework.

2.2 Review configuration management databases (CMDBs) to ensure accurate inventories of infrastructure assets, well-documented baseline configurations for AI environments, relationship mapping between components, and comprehensive change histories.

2.3 Assess the use of automated testing frameworks to validate infrastructure changes. Confirm that performance validation tests are consistently run, benchmark results are evaluated against thresholds, test coverage is documented, and testing is integrated into approval workflows.

2.4 Evaluate cloud infrastructure management practices by verifying enforcement of infrastructure-as-code templates, proper versioning, detection and remediation of configuration drift, and controlled deployment procedures.

2.5 Review API management practices related to infrastructure. Confirm that access to management APIs is controlled, versions are tracked, administrative activity is monitored, and authentication/authorization is enforced.

2.6 Verify oversight of infrastructure components managed by external providers. Check that contracts specify change management obligations, vendor change notifications are integrated into internal workflows, impact assessments are conducted, testing protocols are in place, and post-change SLA monitoring is active.

2.7 Inspect change management for AI-specific infrastructure. Confirm that driver and firmware updates are validated with ML frameworks, performance benchmarking is conducted with relevant AI workloads, capacity planning is aligned with distributed training needs, and hardware optimization configurations are properly tested.

2.8 Review infrastructure-as-code (IaC) practices. Ensure code reviews are conducted, access controls are applied to repositories, templates are tested in staging before production deployment, and syntax and security validations are automated. Confirm version control and change history are documented.