Enterprise AI Governance: Why We Don't Train on Your Data
For enterprises in regulated industries, AI governance isn't optional—it's a requirement. Here's how we meet that bar.
When enterprises evaluate AI solutions for their supply chains, one question comes up repeatedly: “What happens to our data?” For many organizations—especially those in regulated industries—this isn’t just a checkbox. It’s a deal-breaker.
We’ve seen procurement teams walk away from otherwise promising AI vendors because they couldn’t get clear answers on data governance. Legal teams are increasingly treating AI data practices with the same scrutiny they apply to cloud security assessments. And they should: the consequences of getting this wrong—competitive exposure, regulatory violations, reputational damage—are significant.
At Authentica, we’ve built our platform with a clear answer: your data stays yours, and we never use it to train AI models.
The Default That Matters
Many AI platforms bury their data usage policies deep in terms of service, making it unclear whether customer data is being used to improve their models. We take the opposite approach: no training by default.
This isn’t just a policy checkbox. It’s a fundamental architectural decision. Your supply chain data—invoices, contracts, shipping documents, supplier communications—never enters any model training pipeline unless you explicitly opt in through a separate signed amendment.
Why This Matters for Supply Chain
Supply chain data is uniquely sensitive. It contains:
- Pricing information that reveals your negotiating position with suppliers
- Volume data that exposes business performance
- Supplier relationships that competitors would value
- Logistics patterns that reveal operational strategies
This information in the wrong hands—or inadvertently surfacing through a trained model—could cause real competitive harm. That’s why our governance model treats all customer data as confidential by default.
What We Do Instead
Our AI agents are configured using your business rules, document templates, and workflow definitions—but this configuration happens in isolation. Each customer’s environment is separate. The agents understand your processes through explicit configuration, not through learning from your historical data.
This approach means:
- Your competitive information never influences other customers’ experiences
- You maintain complete control over what the AI knows about your operations
- There’s no risk of your data “leaking” through model outputs
The Contractual Commitment
Our Master Subscription Agreement makes this commitment explicit and enforceable:
“Provider shall not use Customer Data to train, develop, or improve machine learning or artificial intelligence models, algorithms, or systems without Customer’s prior written consent via a separate amendment.”
This isn’t marketing language—it’s a contractual obligation with real consequences if violated.
When You Might Want to Opt In
There are scenarios where contributing to model improvement makes sense. Some customers choose to participate because:
- They want to help improve document recognition for unusual formats
- They’re part of an industry consortium working on shared standards
- They’ve anonymized specific data sets for research purposes
But that’s always your choice, made through an explicit process with full transparency about what data would be used and how.
Beyond Training: Complete Data Governance
No-training-by-default is just one element of our data governance framework. We also provide:
- 30-day data deletion upon termination, with written certification
- 72-hour breach notification if any security incident occurs
- Full audit trails of all agent decisions and data access
- Customer-owned outputs—you retain all rights to AI-generated content
Why Enterprises Are Making This a Hard Requirement
We’re seeing a clear shift in how enterprise buyers evaluate AI vendors. What used to be “nice to have” governance features are increasingly becoming deal-breakers:
Regulatory pressure is real. From GDPR to industry-specific regulations in pharmaceuticals, food, and financial services, the obligations around data handling are explicit and carry real penalties.
Legal teams are paying attention. AI-specific clauses are showing up in security questionnaires and MSA negotiations. “Do you train on our data?” is now a standard question, and “it depends” is no longer an acceptable answer.
Competitive concerns are top of mind. Supply chain data reveals pricing strategies, supplier relationships, and operational patterns. Executives are rightly concerned about where that information goes—and who might benefit from it.
Audit requirements are expanding. SOC 2 and similar frameworks now expect clear documentation of how AI systems handle customer data. If you can’t explain your data flows, you can’t pass the audit.
The vendors who will win enterprise AI deals are the ones who treat governance as a first-class feature, not an afterthought.
The Bottom Line
AI should make your supply chain more efficient, not create new risks. Our governance model ensures you get the benefits of AI automation while maintaining complete control over your data.
If you have questions about our data governance practices or want to see our full legal documentation, get in touch. We’re happy to walk through the details with your legal and security teams.