Security
Security information should be understandable and tied to real workflows rather than broad marketing claims.
Trust & Security
Trust & Security
Review how ScribeFlash approaches security communication, data handling, retention, and deletion requests.
Built for careful workflows
- Privacy-conscious support routing
- Visible policy links and trust notes
- Cautious language around unpublished details
- Clearer paths for deletion and follow-up
Security, privacy, data handling, and reliability
Privacy
Privacy-related questions, deletion requests, and data-handling concerns should have a clear support path.
Data handling
Uploaded files, transcript outputs, and exports should be discussed in terms of product use and operational handling.
Reliability
Trust also means making support routing, process boundaries, and product scope easier to understand.
Handling principles
User files, transcript text, and exported outputs are handled to support the product workflow and related operations.
When exact public retention or infrastructure details are not published, we treat them as pending rather than implying a formal public statement already exists.
Trust questions can be routed through the same support address used for product and billing issues so users do not need to guess the entry point.
Trust checklist
- Use only the details needed to identify the relevant upload, task, or account.
- Include task IDs, transcript references, or account email when asking for deletion or follow-up.
- Use the trust or privacy subject line if the request is security-sensitive or policy-related.
Trust FAQ
Do we claim certifications that are not published?
No. If a certification, audit outcome, or formal subprocessor list is not publicly posted, we do not present it as completed public documentation.
How should users request deletion or privacy follow-up?
Use the contact channel with the relevant account email and identifiers so the request can be matched to the correct workflow.
What does privacy-conscious mean here?
It means product and support flows should avoid unnecessary ambiguity and keep data-handling expectations readable for users.