Understand
Start with your business, environment, and the decisions you need to make.
18 / MLOps & AI supply-chain security
Protect the path from data to deployment.
Strengthen the pipelines, artifacts, identities, and release controls used to build and operate AI models.
Discuss your requirementsSpecialist delivery and availability are confirmed during scoping.
Secure MLOps
A clear scope.
A useful outcome.
Follow one model through data preparation, experimentation, training, evaluation, registration, and serving. Identify who can change each component and how those changes reach production. Establish practical integrity checks, release decisions, and rollback responsibilities around the tools your team already uses. Start with a representative pipeline before applying the pattern across your AI estate.
What we cover
- Dataset provenance, access, and integrity controls
- Training infrastructure, dependencies, and workload credentials
- Model registries, artifact integrity, and promotion permissions
- Evaluation gates, serving configuration, and rollback workflows
What you take away
- AI pipeline architecture and ownership map
- Prioritized supply-chain and access-control findings
- Pilot pipeline hardening and validation plan
- Model-release checklist and operational handover guidance
How to engage
Pipeline review followed by a scoped engineering pilot and staged rollout.
What progress looks like
Model releases with traceable artifacts, controlled promotion rights, and exercised rollback steps.
The final scope, deliverables, and timing are agreed for your engagement.
Assess
Agree the scope and examine the controls, configurations, and exposures that matter.
Prioritize
Translate findings into clear actions, accountable owners, and realistic next steps.
Improve
Support remediation, review the evidence, and keep the programme moving forward.
START A CONVERSATION
Get in touch Let’s talk about mlops & ai supply-chain security.
Tell us what you need to protect. We’ll help turn the question into a clear scope of work.