Definition
No-train guarantee
A no-train guarantee is a vendor's contractual commitment that your data — your issues, documents, and prompts — will never be used to train its or a third party's AI models. It is the baseline trust requirement for putting proprietary work into an AI platform: your content powers your features and nothing else, with no leakage into a shared model that competitors could query.
Key takeaways
- A no-train guarantee is a contractual commitment that your data won't be used to train any AI models.
- It removes the core fear of AI tools — that confidential prompts and documents could resurface in someone else's answer.
- Credible guarantees are backed by contract terms, provider agreements, and tenant access controls, not just a claim.
- Planoda is no-train by design, reinforced by tenant isolation that keeps AI context within a single workspace.
The fear that keeps proprietary data out of AI tools is simple: if my prompts and documents train the model, my confidential work could resurface in someone else's answer. A no-train guarantee removes that fear by drawing a hard line — customer data is used to serve the customer's own requests and is never fed back into model training, neither the vendor's models nor any provider's behind the scenes. It's the difference between renting intelligence and donating your data to it.
A credible guarantee is more than a sentence in a marketing page; it's backed by contractual terms, the right provider agreements (so the model API you call doesn't train on the data either), and access controls that keep one tenant's data from ever reaching another's context. Buyers in 2026 increasingly treat it as table stakes alongside encryption and SOC 2.
Planoda's posture is no-train by design: customer data drives a workspace's own AI features and is not used to train models, reinforced by tenant isolation so retrieval and AI context stay strictly within a single workspace.
Related terms
- Agent governanceAgent governance is the set of controls that make an AI agent's actions safe, attributable, and reviewable: human approval gates on consequential actions, an immutable audit trail of who approved what, role-based capability limits, and spend controls. It is the difference between an agent that suggests and one you can trust to act.
- Multi-TenancyMulti-tenancy is an architecture where one running application and database serve many independent customers (tenants), with each tenant's data strictly isolated from the others. It lets a SaaS product share infrastructure for efficiency while guaranteeing that one workspace can never see another's data — a guarantee enforced in the data layer, not left to hope.
- Row-Level Security (RLS)Row-level security (RLS) is a database feature that restricts which rows a query can read or modify based on the current user or context. Instead of relying solely on application code to filter data, the database itself enforces access policies on every query — a strong defense for multi-tenant systems where one workspace's data must never leak to another.
- GDPRThe General Data Protection Regulation is the European Union's comprehensive data-protection law, in force since 2018. It governs how organizations collect, process, and store the personal data of people in the EU, granting individuals rights over their data — access, correction, deletion, portability — and imposing strict obligations on data handlers, backed by fines of up to 4% of global annual revenue.
- SOC 2SOC 2 is an auditing standard from the AICPA that assesses how a service organization handles customer data against five trust service criteria: security, availability, processing integrity, confidentiality, and privacy. A SOC 2 report, produced by an independent auditor, is the common way SaaS vendors demonstrate to customers that their controls are designed and operating effectively.