MLOps & AI supply-chain security

Protect the path from data to deployment.

Specialist delivery and availability are confirmed during scoping.

THE RIGHT STARTING POINT

For engineering teams training, fine-tuning, or self-hosting models. Model robustness testing and corporate AI policy work remain separate scopes.

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.

The final scope, deliverables, and timing are agreed for your engagement.

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.

Before the engagement.

Agree the systems and business processes in scope, the authorisation required, the people involved and how the findings will be used. Timelines, deliverables, exclusions and any verification work should be confirmed directly.