Data Ownership and Trust in AI

When enterprises consider AI, one concern rises above all others: what happens to our data?

Product development involves some of the most sensitive information a company holds from BOMs and material costs to design guides and supplier strategies. Without clear answers about storage, training, and control, AI adoption feels risky.

At Naia, we believe trust is the foundation of adoption. That’s why data ownership, separation, and governance are built into the platform from day one — even as we continue to expand our compliance and certification program.

The Challenge with Generic AI

Consumer AI tools like ChatGPT are powerful but opaque. Once data is uploaded, enterprises can’t easily tell:

• Where it is stored.

• Whether it is logged or retained.

• If it might be reused in model training.

These uncertainties make IT and compliance teams understandably cautious.

Naia’s Data Principles

Our approach is clear: you own your data, and Naia only works with it on your terms.

• No default training.
  Customer data is never used to train Naia models unless a company explicitly opts in.
  

• Scoped training
  When training does occur, we use a mix of anonymization, aggregation, and synthetic data to minimize risk. No system can ever claim “perfect anonymization” — but our design goal is to make re-identification practically unfeasible.
  

• Separation by design
  Each customer’s data is logically separated in our Azure environments. Your data is never co-mingled with other customers’.
  

• Opt-in improvement.
  Companies decide if and how to contribute insights back into the Naia Product LLM.

Training and Model Use

Training is often the biggest area of concern. At Naia, we treat it carefully:

• The Naia Product LLM
  is trained on domain-specific data: anonymized customer contributions, synthetic datasets, and external product-relevant sources.
  

• Opt-in only
  Your company’s proprietary data is never used unless you explicitly allow it.
  

• Dynamic scoping
  When teams run scenarios in Naia, context like BOMs, briefs, or sustainability metrics are applied at runtime. That scoped data improves the output for your team but is not added to model training.

What about our use of OpenAI GPT models?

Naia integrates GPTs in a controlled Azure environment, where enterprise data is processed under Microsoft’s compliance framework. This is not the same as using ChatGPT directly in a consumer context. Your data stays governed by enterprise-grade controls, with no risk of it being fed back into the public ChatGPT service.

Where We Are Today

Naia is still early, and we are transparent about that. We are not yet formally certified under ISO 27001, SOC 2, or other standards. But we are already:

• Operating fully within Microsoft Azure environments, with encryption, access control, and regional hosting options.

• Embedding separation of customer data at the company, team, and user level.

• Following a clear roadmap toward recognized certifications as we scale.

Why This Matters

AI will only become mission-critical if it is trusted. Without clarity on storage, training, and governance, enterprises hesitate to adopt at scale. Naia addresses those concerns directly:

• Ownership
  always stays with the customer.
  

• Training
  is only on anonymized, synthetic, and opt-in data.
  

• Separation
  ensures data from different customers never mixes.
  

• Governance
  comes from operating within Microsoft Azure, with compliance certifications to follow.

Final Takeaway

Product development relies on data you can’t afford to lose or expose. Naia gives enterprises the confidence to accelerate with AI — without compromising control.

Your data stays yours. Training is only opt-in. Storage and separation are secure. Compliance is built-in and strengthening over time.

That’s how AI becomes not just fast, but trustworthy.