Why Data Ownership Questions Spike With Sustainability and Process Data
Carbon and compliance tools often need batch sheets, sensor streams, and energy logs. That is the same data that encodes your yield improvements and unique raw-material blends. Treat access requests like a licensing deal, not a simple integration.
Regulators are sharpening expectations. The EU AI Act becomes largely applicable on August 2, 2026, with transparency and data governance duties that affect buyers and providers of AI systems. See the European Commission’s official application timeline for key dates.
Define The Three Buckets Before You Share Anything
Write definitions in plain English and put them in the contract. Your Data means everything you upload or stream, including content, prompts, embeddings created from your content, and labels you or your vendor apply. Derived Data means metrics computed from Your Data, such as defect rates by SKU or kiln energy per ton.
Model Artifacts means weights, prompts, rules, fine-tunes, and evaluation sets created during delivery of your project. State that Model Artifacts trained or tuned with Your Data are for your exclusive use, are not shared with other customers, and are deleted or transferred at project end.
Contract Red Lines That Protect The Plant
Make five protections non negotiable and visible in the order form. First, vendor will not train foundation or cross customer models on Your Data without your written consent. The FTC has warned that companies that promise not to reuse customer data for model training must keep that promise, and that quiet policy changes can be deceptive. Share their post on privacy and model training commitments during negotiations.
Second, require single tenant data isolation with separate encryption keys. Third, specify residency for production and backups. Fourth, insist on the right to export all inputs, outputs, prompts, and logs in a machine readable format. Fifth, require deletion certificates when you leave.
Keep Learning Inside Your Walls
A great manufacturing AI vendor makes the system learn only from your organization’s content and queries. They document that retrieval augmented generation uses your private index, that fine tuning occurs inside your tenant, and that cross customer training is disabled by default.
Ask for a design that separates compute, storage, and indexing per customer. Confirm that test environments do not use real formulations. Require a red team test where your team tries to extract another customer’s data and the vendor shows why it fails.
Evidence For Auditors Without Slowing Down
Auditors do not want a slide deck. They want controls and artifacts. Point them to a security report that maps to SOC 2 Trust Services Criteria. The AICPA’s criteria on security, confidentiality, and privacy set the baseline most auditors expect in 2026. You can cite the official Trust Services Criteria when drafting requirements.
AI governance evidence is maturing as well. ISO published ISO IEC 42001 for AI management systems that emphasizes traceability and responsible use. If your vendor maps controls to this standard, you get a common language for audits. See ISO’s page for ISO IEC 42001.
For risk framing, NIST’s AI Risk Management Framework remains the clearest reference. It highlights governance, measurement, and documentation practices that translate well to plant systems. Link your vendor’s controls to the NIST AI RMF so audit reviews go faster.
What Good Siloing Looks Like In Practice
Data paths are simple and verifiable. Your raw data lands in a storage account within your tenant, a transformation job produces a private index, and the model queries only that index. The model cannot write back to any corpus that another customer can access.
Prompts and outputs are logged with time, actor, source document IDs, and model version. No training datasets are created from ad hoc operator prompts. Temporary caches are purged on a fixed schedule and covered by deletion certificates.
Avoid Hidden Data Spillovers During Sustainability Work
The fastest growing risk is well meaning analysis that expands vendor scope. For example, a pilot to estimate Scope 1 emissions can morph into multi plant OEE benchmarking if you do not limit Derived Data rights. Cap cross customer benchmarking to opt in data, with irreversible aggregation that prevents re identification.
If you participate in consortium studies, route data through your company, not directly from your vendor. The legal context for data sharing is shifting. The safest option is customer controlled aggregation with counsel review before any external sharing.
An Audit Friendly Package You Can Ask Vendors To Provide
Ask for one zip that covers implementation, operations, and incident response. Include these documents:
- Data flow diagrams for ingestion, indexing, inference, and logging
- Access control matrix by role with least privilege mappings
- Model card or factsheet with training sources, fine tunes, and evaluations
- Log samples that tie prompts to outputs to source documents
- Deletion and backup schedules with residency locations
This single package lets your internal audit or external assessors finish work faster. It also forces vendors to maintain living documentation, which reduces surprises when staff changes.
Negotiation Moves That Save Time
Start with business outcomes, then add the smallest data scope that achieves them. If the use case only needs read only access to SDS sheets and process alarms, do not open batch formulations. Expand access later with a change order.
Make vendors prove isolation in your environment before go live. A one day tabletop with security and technical services can validate identities, keys, network boundaries, and logs. If they cannot demonstrate isolation and deletion quickly, they are not ready for production.
The Bottom Line For 2026
You can get AI value without risking trade secrets. Write contracts that define Your Data, Derived Data, and Model Artifacts. Require learning that stays inside your tenant and logs you can hand to an auditor. Anchor expectations to SOC 2, ISO IEC 42001, the NIST AI RMF, and the EU AI Act timeline. Add clear exit and deletion rights, then pilot with the smallest data footprint that proves ROI without regret.
