AI System Deployment
Plain English Translation
Organizations must formally document an AI deployment plan before releasing an AI system into production environments. This involves defining and verifying that all pre-deployment requirements, such as performance testing, environmental checks, and management approvals, have been fully satisfied. By rigorously assessing release criteria, the organization ensures the system is safe, performs as expected, and accounts for impacts on all relevant stakeholders.
Technical Implementation
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Required Actions (startup)
- Define basic release criteria for AI models.
- Maintain a simple AI go-live checklist for production deployments.
- Ensure manual sign-off is recorded before launch.
Required Actions (scaleup)
- Formalize an AI deployment plan template for all AI systems.
- Implement MLOps deployment governance and controls.
- Document AI deployment approvals and sign-off in a centralized ticketing system.
Required Actions (enterprise)
- Integrate automated pre-deployment requirements checklists for AI systems into CI/CD pipelines.
- Enforce strict AI system release management documentation and automated rollback procedures.
- Ensure all AI deployment rollback and versioning checklist items are automatically verifiable prior to staging.
ISO 42001 A.6.2.5 AI system deployment requires that organizations document a deployment plan and ensure appropriate requirements are met prior to deploying an AI system.
ISO/IEC 42001:2023 AI system deployment plan requirements state the plan must account for differences between development and production environments, separate component deployments like software versus model, and the perspectives of relevant interested parties.
Organizations use a pre-deployment requirements checklist for AI systems to document evidence of passed verification and validation measures, completed user testing, and achieved performance metrics. Tools like WatchDog Security's Compliance Center can link the checklist to this control, attach evidence, and flag missing items before go-live.
Before deployment, organizations must ensure AI models pass predefined verification and validation measures, complete necessary user testing, and achieve specified performance metrics to prove they are ready for production.
Organizations must understand how to document AI deployment approvals and sign-off, which requires obtaining explicit management approvals as part of the formal release criteria before any AI model goes live. Tools like WatchDog Security's Secure File Sharing can help distribute approval packets securely and retain access-controlled audit logs showing who reviewed the materials and when.
To create an audit-ready AI model go-live checklist for production, list all release criteria, testing validations, environmental checks, and management sign-offs required by your AI system release management documentation.
An AI deployment rollback and versioning checklist should be included within the deployment plan to ensure safe transitions, even though detailed operational monitoring and post-deployment rollbacks are handled under subsequent lifecycle controls.
Deploying new AI systems or updating existing ones must follow MLOps deployment governance and controls, integrating seamlessly with organizational change management processes to prevent unintended operational disruptions.
Any privacy and security impacts identified during risk assessments must be reviewed, and mitigation controls verified as implemented, prior to executing the AI deployment plan. For technical readiness evidence, tools like WatchDog Security's Posture Management and Vulnerability Management can help surface configuration findings and remediation status to support pre-deployment checks.
Auditors looking for auditor evidence for ISO 42001 AI deployment expect to see a formal deployment plan, completed pre-deployment checklists, documented release criteria, and records of management approvals. Tools like WatchDog Security's Compliance Center can help organize these artifacts, maintain evidence-to-control traceability, and produce status views for audit preparation.
Deployment documentation often changes as environments, models, and release criteria evolve, so you need clear ownership, version history, and review traces. Tools like WatchDog Security's Policy Management can help maintain deployment plan templates, manage version control, and record acknowledgements or reviews so teams can demonstrate controlled updates over time.
External reviewers typically need limited, time-bound access to specific artifacts (deployment plan, checklist, approvals) without exposing unrelated internal materials. Tools like WatchDog Security's Trust Center can help publish selected evidence with access controls and evidence sync workflows to support structured, auditable sharing.
"The organization shall document a deployment plan and ensure that appropriate requirements are met prior to deployment."
"AI systems can be developed in various environments and deployed in others (such as developed on premises and deployed using cloud computing) and the organization should take these differences into account for the deployment plan. The organization should also consider whether components are deployed separately (e.g. software and model can be deployed independently)."
"Additionally, the organization should have a set of requirements to be met prior to release and deployment (sometimes referred to as “release criteria”). This can include verification and validation measures that are to be passed, performance metrics that are to be met, user testing to be completed, as well as management approvals and sign-offs to be obtained."
| Version | Date | Author | Description |
|---|---|---|---|
| 1.0.0 | 2026-02-23 | WatchDog Security GRC Team | Initial publication |
