Is It Safe to Put Customer Feedback Into ChatGPT? What IT Teams Check
It depends on which ChatGPT and what is in the file. Here is what OpenAI's own pages say, and the checklist IT teams use before approving any AI tool for customer feedback.
Putting customer feedback into ChatGPT can be safe, and whether it is depends mostly on which ChatGPT you mean and what is in the feedback. OpenAI says consumer ChatGPT conversations may be used to train its models unless you opt out, and that its business products (ChatGPT Business, Enterprise, Edu and the API) do not train on an organization's data by default. An IT team then checks the contract, retention, certifications, personal data in the text and where the results end up. If your IT team has already said no to customer data in a general AI assistant and you still need every review, ticket and survey analyzed, Dimension Labs is built for that case: SOC 2 Type II and ISO 27001, data processing agreements, and personal data redacted at ingest, before any enrichment.
This post sets out what OpenAI says on its own pages, read today, what IT and security teams check before they approve any AI tool for customer data, and the questions a feedback analytics vendor should be able to answer.
Is it safe to upload customer reviews and support tickets to ChatGPT?
Start by separating two things that often get mixed up: a person pasting tickets into a personal ChatGPT account, and a company using a ChatGPT business product under a contract. OpenAI describes different defaults for each.
From OpenAI's own pages, read Nov 7, 2026
OpenAI states different defaults for its consumer and business products
What OpenAI says about training, ownership, encryption and compliance, by product type.
| Question | Consumer ChatGPT | ChatGPT Business, Enterprise, Edu and the API |
|---|---|---|
| Used to train models? | Conversations may be used for training unless you opt out; Temporary Chats are not used to train models | Not by default: OpenAI says it does not train on organizations' data, including inputs and outputs, by default |
| Who owns the data? | Not covered on the pages read | OpenAI says organization data is owned by the customer |
| Encryption | Not covered on the pages read | AES-256 at rest and TLS 1.2 or higher in transit |
| Compliance | Not covered on the pages read | Practices aligned with SOC 2 Type 2 and ISO/IEC 27001, 27017, 27018 and 27701; a Data Processing Addendum is available |
| Retention and residency | Not covered on the pages read | Retention controls for qualifying organizations; data residency in several regions for eligible customers |
| Access | Individual sign-in | SAML single sign-on (listed for Enterprise) and Member, Admin and Owner roles |
On a business plan, those are the same kinds of commitments IT teams ask any software vendor for, and they can be reviewed the same way. The risk sits mostly in the gap between policy and practice: an employee on a personal account, a file exported with more columns than the job needed, or a conversation shared outside the team. Those are the things an IT policy is usually written to stop.
What is actually in a file of customer feedback?
More than most people expect. Public reviews are already public, though they still carry names and sometimes photos. Survey responses and support tickets are different. A ticket export can include the customer's name, email address, phone number, order or account number and the agent's notes, and the free text itself often has an address, a card's last digits, a health condition or a child's name typed in by the customer. Survey exports often come with the respondent ID, email and every embedded field the survey tool holds.
Two habits cut most of the exposure, whatever tool you use. Export only the columns the analysis needs: the text, a date, a score and a non-identifying key that joins back to your own systems. And redact personal data before the text reaches any model, or use a tool that does it at ingest. Our post on analyzing a CSV of customer feedback lists which columns a useful analysis needs.
Our IT team blocked ChatGPT for customer data. What should a feedback analytics vendor prove?
The same things, with documents. A vendor that analyzes customer conversations for a living should hand over an audit report and a data processing agreement without a long back-and-forth. The checklist below is the one we would use ourselves; the right column is what Dimension Labs states on its security page and in its privacy policy.
A checklist for IT and security review
Ten questions to ask any AI tool before customer feedback goes in
The same questions apply to a general AI assistant and to a feedback analytics vendor. The right column shows what Dimension Labs publishes on its security page.
| What IT checks | Evidence to ask for | Dimension Labs, as published |
|---|---|---|
| Is our data used to train models? | The contract or data processing terms, not a blog post | Ask in the security review; not stated on /security |
| Independent audit | SOC 2 Type II report, ISO 27001 certificate | SOC 2 Type II and ISO 27001 |
| Privacy law | Data processing agreement, transfer mechanism | GDPR and CCPA compliant, DPAs signed with customers; EU-U.S. Data Privacy Framework participant |
| Personal data in the text | Where redaction happens and what it covers | PII redacted at ingest, before enrichment |
| Hosting and tenancy | Region, single or multi-tenant | Cloud hosted, multi or single tenant; on-premise for enterprise |
| Access control | SSO, roles, audit of who saw what | SSO and role-based permissions |
| Deletion | How and how fast data is deleted | Full deletion on request |
| Where results go | Exports and their access controls | Write-back to Snowflake, Databricks or BigQuery, where your own access controls apply |
| Subprocessors | The current list and change notices | Ask in the security review |
| Who can share outputs | Sharing settings for reports and links | Ask in the security review |
One row deserves a closer look: whether customer data trains a model. Ask every vendor, including one that sits on top of a large language model, which models process your data, under which terms, and whether those terms allow training. The answer belongs in the contract. A related question is which model runs underneath at all; an operator's IT team asked us exactly that when they restricted ChatGPT for customer data.
Also check the privacy policy's scope. Dimension Labs' privacy policy says its obligations for customers' end-user data ("Customer Data") are defined in its agreements with customers rather than in the policy itself, which is common for business software: the data processing agreement is the document that governs your customers' feedback.
Where does the safety question end and the workflow question begin?
Even with security settled, pasting feedback into a chat window is a way to read a few hundred comments, not a way to run a monthly analysis. The questions that come next are practical: will the categories be the same next month, can every row be labelled and not only the ones that fit in a prompt, can the labels join to revenue or renewals, and who else can open the result. Our post on what an AI analytics agent is covers those differences, and how to tell if your AI tagging is accurate explains the hand check any method needs.
What does Dimension Labs bring to a security review?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For the security reviewer, that means one approved place for tickets, chats, calls, reviews and surveys instead of copies pasted into several tools. Data comes in through connectors and APIs and, on the Prove plan and up, goes back to your own Snowflake, Databricks or BigQuery, where your access controls apply. Personal data is redacted at ingest, before the Dimensions are added, and the Dimensions themselves are written for your business, so the output is structured fields rather than free text passed around. The reports, decks and dashboards are built inside the platform, behind single sign-on and role-based permissions, and shared from there.
The security page puts it plainly: the platform is built to handle Fortune 500 customer data, with the controls enterprise security teams ask for, on every plan. Its customers include the NBA, Travelers, Intuit, Dropbox, Netflix and Disney.
When a ChatGPT business plan is the better choice: your company already has one approved, the job is reading and summarizing a modest set of comments, and nobody needs the same labels next month. That is a reasonable use, and the checklist above still applies to it.
FAQ
Does ChatGPT train on the customer data I paste into it?
OpenAI says consumer ChatGPT conversations may be used for training unless you opt out or use Temporary Chat, and that ChatGPT Business, Enterprise, Edu and the API do not train on an organization's data by default. Check which one your company uses.
Is ChatGPT Enterprise safe for customer feedback?
OpenAI states it does not train on Enterprise data by default, encrypts data with AES-256 at rest and TLS 1.2 or higher in transit, and offers a DPA and retention controls. Whether that meets your policy is your IT team's call; the checklist above covers what they will look at.
What should we remove from a feedback file before any AI tool reads it?
Names, email addresses, phone numbers, account and order numbers you do not need for a join, and any personal details customers typed into the text. Keep the text, a date, a score and a non-identifying key.
Our IT team restricted ChatGPT. Can Dimension Labs still be approved?
Often, yes. It publishes the controls those reviews look for: SOC 2 Type II, ISO 27001, GDPR and CCPA compliance with DPAs, PII redaction at ingest, SSO and roles, and single-tenant or on-premise options for enterprise. Send your questionnaire with the security page as a starting point.
If you would like your security team to see the output first, start with one file of public reviews, which carry no internal customer data, and bring the result to the review.