Overview
Pioneer.ai gives developers a set of AI models and APIs to build on. Trust is part of the product. This page explains the controls we run behind the API, what happens to the data you send us, and how we work to keep our models safe to deploy. It is written for the engineers integrating Pioneer.ai and the security teams reviewing us. If you need something not covered here, such as a security questionnaire, subprocessor list, or a signed agreement, reach out at security@pioneer.ai.Data security
Encryption
All data is encrypted in transit using TLS 1.2 or higher. Data at rest is encrypted using AES-256. API traffic is served exclusively over HTTPS, and requests over unencrypted channels are rejected.Infrastructure
Pioneer.ai runs on AWS, which maintains its own physical and environmental security controls. Our production environment is isolated from development and staging, with separate VPC CIDRs and restrictive security groups, and network access is restricted by default.Access control
Access to production systems and customer data is limited to the employees who need it, granted on a least-privilege basis, and reviewed regularly. We require single sign-on and multi-factor authentication for internal systems, and access is revoked promptly when someone changes roles or leaves.Secure development
Changes to production go through code review and automated testing before they ship. We use dependency scanning and static analysis in our pipeline, keep an audit trail of deployments, and patch known vulnerabilities on a defined timeline.Authentication for developers
API access is authenticated with per-team API keys. A key is bound to a team and can access every project in that team, so treat each key as team-wide. We recommend you store keys in a secrets manager, set an expiry where appropriate, and never commit them to source control. Keys can be revoked at any time from the dashboard.Privacy & data handling
What we collect
When you call the API we process the inputs you send (prompts, files, and parameters) and return outputs. We also collect operational metadata such as request timestamps, token counts, and error logs to run and secure the service.Do we train on your data?
Sometimes, by default. Unless you opt out, we may use inputs and outputs you send through the API to improve and train our models. Enterprise customers can opt out of platform training in Settings. Separately, your inference data is used for continuous adaptation and remediation training of your own task models. That task-model training applies regardless of the platform-training opt-out and is not affected by the toggle.Retention
By default, API inputs and outputs are retained indefinitely to support debugging, abuse detection, and service reliability and improvement. Stored payloads tier to colder storage over time but are not automatically deleted. For eligible use cases, you can setstore: false on a request for full zero-retention handling, in which case the input and output are never written to storage.
We use a small set of third-party providers to deliver the service (for example, cloud hosting and monitoring). A current list of subprocessors is available below. We update it before adding a new subprocessor that handles customer data.
Subprocessors (last updated July 30, 2026):