Does AutoRFP Train on or Store Our RFP Data?
When a security questionnaire owner or proposal manager asks whether an RFP AI vendor trains on customer data, the vendor usually answers with a single reassuring sentence and moves on. That single sentence rarely covers what the reader needs to know. Training, storage, retention, and access are four separate questions, and vendor marketing often collapses them into one line so the reader stops asking.
Training refers to whether your content shapes a shared model that other customers eventually benefit from, while storage is a different matter: how long your raw content sits on a vendor's servers, and in what form. Retention covers what happens to that content after your contract ends or after a single response is generated, and access covers who inside the vendor's organization, human or automated, can open your files and under what logging.
A vendor can answer "we don't train on your data" honestly while still storing that data indefinitely, retaining it after offboarding, or giving a wide set of internal staff unlogged access to it. Before you put security questionnaire answers, pricing, or client-specific proposal language into any platform, get answers to all four questions instead of the one the vendor chose to highlight.
What AutoRFP says about training and storing your RFP data
The trust center claim
AutoRFP's trust center states that it ensures none of the data provided by customers is used to train public machine-learning models, and that all data is used only at runtime and is not retained by the model once a task completes. That is a specific, useful claim, and it addresses the training question directly. It does not, on its own, describe how long raw files sit in AutoRFP's infrastructure before or after that runtime use, which is a separate storage question worth asking in writing.
The legal FAQ language
AutoRFP's legal page quotes its own Terms and Conditions, stating that AutoRFP.ai will not use your data for training of artificial intelligence models, while also noting that it may generate aggregated and de-identified data for improving its services. That second clause matters: "aggregated and de-identified" is a common and legitimate practice, but it is also a broad category, and the contract language does not spell out exactly what counts as aggregation or how de-identification is verified. A careful reader should ask AutoRFP to define both terms in the data processing agreement itself rather than accepting the FAQ summary as the full picture.
A tension worth flagging
AutoRFP's feature page for its RFP Response Engine describes an AI "trained on your winning responses" that "learns your voice over time." That phrasing sits uneasily next to the trust center's "zero external AI training" claim and the legal page's "will not use Your Data for training" language. AutoRFP may mean something narrower by "trained" on the feature page, such as retrieval over a customer's own approved content rather than fine-tuning a shared model, but the reader should not have to guess which meaning applies. If you are evaluating AutoRFP, ask the sales team to reconcile the feature page's training language with the trust center and legal page in writing, and get that reconciliation added to your data processing agreement rather than left as a marketing distinction.
AutoRFP's privacy policy also describes broad categories of personal information it collects, including contact details, device and connection data, and survey responses, along with potential disclosure to trusted third parties. That is a normal SaaS privacy posture, but it is a separate question from AI training, and it deserves its own line of questioning during procurement review.
How Responsive approaches customer data and AI training
Responsive drafts AI-generated answers from a curated content library through retrieval-augmented generation, instead of fine-tuning a shared model on customer RFP content. The same source notes that the AI Assistant may also use publicly available sources when appropriate, while always prioritizing the organization's verified internal knowledge. In practice, the Responsive AI agents search your organization's own approved, verified content each time they draft an answer, then compose a response from what they find there. This is consistent with why generic AI alone can't be trusted with RFP data: generic tools cannot reliably distinguish between approved, current, and customer-ready content, and they often pull from mixed sources that make it hard to tell whether material is current or outdated.
A content library still stores your approved answers in your tenant, so storage, retention, and access remain fair questions to put in a DPA review with us, the same way they are with AutoRFP. What we document publicly is how answers are generated: retrieval from that library, with a TRACE Score and citation, not shared-model training. That public claim is not a substitute for reading the contract.
Every answer our AI produces also carries a TRACE Score, an objective rating across trustworthiness, relevance, accuracy, completeness, and explainability, along with a citation back to the source document it drew from. That gives proposal and security teams a way to check, answer by answer, exactly where a claim originated before it goes into a client-facing response, instead of relying on a vendor's general assurance after the fact.
We back this approach with third-party certifications that customers can verify independently. Our security page documents current compliance certifications including SOC 2, ISO 27001, ISO 27701, ISO 42001, GDPR, CCPA, and CSA STAR Level 1, audited standards with defined scopes that give a security questionnaire owner something more concrete to check than a single trust center sentence.
A short checklist for evaluating any RFP AI vendor's data claims
Whichever platform you are evaluating, including AutoRFP, ask for the specific data processing agreement and master services agreement clause that governs AI training, beyond the trust center summary. Ask the vendor to define "training" explicitly against fine-tuning, embeddings, and any "product improvement" data use, since a narrow definition of training can still leave room for your content to shape outputs in other ways. Ask who inside the vendor's organization, and which automated systems, can access raw customer content, and how that access is logged and audited. If you are weighing whether to build internal tooling, adopt a vendor, or integrate both, this checklist maps closely to the questions covered in evaluating build, buy, or integrate for AI response management, which walks through governance and traceability criteria in more depth.
Where security questionnaire owners go next
If you own security questionnaire response at your organization, the clearest way to judge a vendor's data handling disclosure is to compare it against a disclosure you can fully verify. Responsive's Trust Center documents its own security posture and is built to withstand that kind of scrutiny, and our approach to security questionnaire automation relies on the same grounded, cited answer generation described above. For teams evaluating a broader move away from spreadsheets or a legacy RFP tool, our RFP management software built around a verified content library applies the same content-library and citation approach across every stage of a response, beyond the security questionnaire portion of it.