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AI testing platforms may send far more than a prompt—or far less than a source-code upload. Depending on the product and feature, cloud processing can involve test definitions and values, commit metadata, execution results, logs, screenshots, rendered page structure, network traffic, attachments, prompts, and AI tool results. The practical answer is feature-specific: find out what is sent, when it is sent, where it goes, and how long each recipient keeps it before enabling the feature.
What information can an AI testing platform send?
The payload depends on the work the platform is doing. A cloud runner needs files and configuration to execute a test; an AI assistant needs the prompt and whatever context is supplied to answer it; test-selection analytics may process code and test metadata to estimate which tests matter. These are distinct data flows, even when one vendor offers them all.
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Data sent to a platform or its AI provider can include:
- Code and test content: source files, test scripts, test definitions, test steps, requirements, or project context.
- Identifiers and metadata: commit SHA, branch, timestamps, author details, changed-file lists, test names and paths, suite associations, outcomes, and durations.
- Execution artifacts: terminal output, logs, screenshots, videos, browser console messages, and operating-system or CI environment details.
- Application observations: rendered DOM and CSS, network requests and responses, page controls, properties, labels, and values.
- AI task material: prompts, optional project context, attached files, resources taken from HTTP history or a site map, agent messages, tool calls, and tool results.
Those categories can expose sensitive information even when a vendor says it does not collect application source code. A screenshot may show customer records; a request or console log may reveal a token; a test value may identify a real person. Assess the actual artifacts and fields, not just whether a product uploads a repository.
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Why do different features send different data?
Start with the feature’s purpose: it usually explains why particular information is needed. Running tests in a hosted environment, analyzing a failure, choosing a smaller test set, replaying a browser session, and generating a test from a prompt do not require identical inputs.
Cloud execution
A hosted runner must obtain the suite and associated data needed to execute it. Katalon describes Studio as a desktop application and separately documents a flow in which Cloud Execution loads test suites and related data from external repositories, runs scripts in cloud environments, and transfers results to Katalon True. Katalon True stores test cases, suites, and execution information. Those product paths should not be conflated with an AI prompt flow. See Katalon’s Security and Privacy FAQ.
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CloudBees Smart Tests uses source and test-file content to create vector embeddings for test selection, according to its data privacy and protection documentation. CloudBees says the original source content is deleted after embedding, while embeddings are retained. Its CLI also sends commit metadata and test results; metadata can include the commit SHA, branch, timestamps, author details, changed-file lists, test paths and names, outcomes, durations, and suite associations. An embedding is not the original source file, but it is still derived from that content and should be treated as a retained data product.
Run recording and replay
Recorded-run services can retain artifacts so teams can diagnose or replay what happened. Cypress Cloud documents terminal output, test results, test definitions, configuration other than Cypress environment variables, screenshots, videos, and OS environment variables related to CI and Git for recorded runs. Test Replay adds rendered DOM and CSS, Cypress commands and events, network traffic, and browser console logs. Cypress says Cloud AI may share test content with a session-bound AI model when enabled, and that it does not capture application or repository code. The distinction is important: not capturing source code does not mean a run contains no sensitive application data. The details and controls are in Cypress’s data storage and controls documentation.
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An assistant may transmit a user-selected prompt and contextual information, rather than continuously sending a whole project. Testmo says its AI runs only when a user explicitly chooses it, and that inputs can include optional context such as domain terms or project information. Testmo also says customer AI inputs and outputs are not used to train or improve underlying LLMs. These statements concern Testmo AI as described in its AI data policy; they should not be generalized to every integration or contract.
PortSwigger documents Burp AI and Burp AT as separate scopes. Burp can send an HTTP request or vulnerability description to PortSwigger AI infrastructure. Burp AT task data can also include prompts, attached resources, files, agent messages, tool calls, and their results. PortSwigger says Burp AT does not upload the Burp project, but sensitive requests, responses, resources, and files may be sent without redaction. Its AI trust and data handling page, dated October 22, 2026, makes the selected test scope and approval controls especially relevant.
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The following examples show why a single claim such as “the AI uploads your code” or “the service never sends code” is too broad. Each row describes only the product or feature named and the vendor’s published account of it.
| Product or feature | Documented data flow | Key scope point |
|---|---|---|
| Testmo AI | User-submitted input and optional context, such as domain terms or project information, go to an LLM. | Testmo says use is explicit and inputs and outputs are not used to train or improve underlying LLMs. |
| Katalon | Studio project data is typically on the engineer’s workstation; Cloud Execution loads suites and related data from external repositories, runs scripts in cloud environments, and transfers results to Katalon True. | Studio, Cloud Execution, Katalon True, and AI services have different paths and controls. |
| Cypress Cloud | Recorded-run artifacts include test definitions and results, screenshots, videos, terminal output, selected configuration and CI/Git environment information. Test Replay adds DOM/CSS, network traffic, commands, events, and console logs. | Cypress says Cloud AI can share test content with a session-bound model when enabled; it says it does not capture application or repository code. |
| CloudBees Smart Tests | CLI sends commit metadata, test results, and source content used to produce embeddings; test and commit metadata are also processed. | CloudBees says source and test-file content is deleted after embedding and that embeddings are retained. |
| TestingBot AI | Opt-in failure analysis can send a small text-only test slice; visual region naming can send screenshot crops around changes. | TestingBot says detectable secrets and PII in text are masked, but screenshot crops are not. It says AI data may go to US subprocessors while core testing infrastructure is in Europe. |
| Burp AI and Burp AT | Depending on the task, data can include HTTP requests, vulnerability descriptions, prompts, resources, attachments, files, agent messages, tool calls, and results. | PortSwigger says Burp AT does not upload the Burp project; sensitive task data may be sent without redaction. |
| AssertThat Autonomous Test Management | Test data is stored in Atlassian Forge storage; AI uses Atlassian-hosted models, with prompts and results staying within Atlassian. | AssertThat says the app makes no calls to external services. This applies to the named Forge application, not all Jira apps or Atlassian AI. |
| Tricentis Tosca Agentic Test Automation | Inputs can include test cases and steps, requirements, execution logs, and screenshots with application labels, controls, properties, and values. | The Tosca 2026.1 policy describes tenant-region processing, up to 24 hours in a processing database, and up to 30 days for traces or feedback and in Microsoft Azure OpenAI systems. |
Where is the data processed, and how long is it retained?
“In the cloud” does not identify every recipient or location. A vendor’s cloud, a model provider’s service, a tenant’s selected region, and a subprocessor may be separate stages. Ask which feature routes data to which service, and whether the route follows the tenant region or crosses it.
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Published retention statements also have different scopes. TestingBot says AI provider inputs and outputs are deleted within 30 days, and says its AI data can be sent to US subprocessors even though its core testing infrastructure is in Europe. Tricentis’s Tosca 2026.1 policy specifies up to 24 hours in a processing database, up to 30 days for traces or feedback, and up to 30 days in Microsoft Azure OpenAI systems. These are vendor policy figures, not independent measurements, and they are not interchangeable: one describes TestingBot’s provider data, while the other names multiple Tosca processing and trace stages. Consult the applicable version of the TestingBot Trust Center and Tricentis Tosca policy.
Training language needs the same precision. Testmo says its customer AI inputs and outputs are not used to train or improve underlying LLMs. Tricentis says queries are not used for model training and that PII is anonymized or redacted. Those are distinct vendor statements; neither establishes what every other platform or subprocessor does. The Tosca Cloud policy may describe additional model providers and regions, so verify the actual deployment and version rather than relying on a similarly named product page.
How should you evaluate a platform before enabling AI?
Compare the exact feature and deployment, not just the company’s general privacy page. A useful review records the payload, trigger, purpose, destination, retention, and controls for each feature.
- Identify the feature and trigger. Is data sent during every recorded run, only for cloud execution, after an explicit AI action, or when an administrator enables an opt-in setting?
- Inspect a representative payload. Look for scripts, test values, environment details, screenshots, DOM, network requests and responses, prompts, attachments, and tool results. If the product provides a run trace or sample payload, use it to see what actually leaves your environment.
- Map each processing recipient. Distinguish the platform’s own infrastructure from model providers, subprocessors, and a customer-selected model key. Check the tenant region and any routing outside it.
- Read retention and training terms by data type. Find out what is retained, for how long, and whether the statement covers vendor systems and providers alike. Check the current DPA and the product/version-specific policy.
- Verify controls in the relevant feature. Look for opt-in switches, roles and permissions, exclusions, masking, deletion mechanisms, and approval gates. Do not assume a control documented for one feature applies to another.
- Test the controls with safe data. Use synthetic values and non-production credentials, then confirm whether sensitive strings are removed from text artifacts and whether images, logs, or network details remain visible.
Masking deserves particular scrutiny. TestingBot distinguishes text masking from screenshot handling: it says detectable secrets and PII in text are masked, while screenshot crops are not. A policy that mentions redaction is not enough unless it identifies the data type and artifacts covered.
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For applications with strict confidentiality requirements, disable features that are not approved and limit the test scope to the minimum needed. PortSwigger’s description of potentially unredacted requests, responses, resources, and files illustrates why an approval gate and a deliberately scoped target matter in security testing. These are practical safeguards, not a claim that any named service is universally safe or unsafe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does this mean for proprietary code and realistic test data?
Do not treat a vendor’s “we do not collect source code” statement as a blanket guarantee that no sensitive material leaves your environment. It may describe a specific product or artifact category while the same service processes test definitions, logs, screenshots, request bodies, or configuration. Conversely, a vendor that processes source for a stated purpose may describe deletion of the original while retaining derived embeddings or metadata.
For a meaningful risk decision, make an inventory at the feature level:
- What exact test or project information is required for the feature to work?
- Does a human initiate the transfer, or can it happen automatically as part of CI or recording?
- Could test inputs, screenshots, or requests contain credentials, regulated information, customer data, or unreleased product behavior?
- Which service receives the data, in what region, and under what retention and training terms?
- Can your organization exclude sensitive projects or fields, require approval, or turn the feature off?
Then reduce exposure before sending: use synthetic records, short-lived or scoped credentials, and a test environment that does not contain production customer data. Review what is visible in screenshots and logs, not only what is written in the source repository. If the policy does not clearly answer a material question, treat that as unresolved for the specific feature and seek contractual clarification before using real sensitive data.
What should you remember when comparing data policies?
A useful comparison separates payload from purpose and destination. A platform may store a test artifact for replay, transmit source content briefly to create embeddings, or send a narrow user-selected prompt to a model. Those flows have different security implications and cannot be summarized accurately with one company-wide “uploads code” label.
Read the current documentation for the product edition, feature, region, and contract you will actually use. Vendor policy pages explain the vendor’s stated handling; they are not independent audits, and a policy for one feature should not be extended to another.
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Can an AI testing platform see secrets in my CI environment variables?
It depends on what the specific feature collects. Cypress documents recorded-run configuration other than Cypress environment variables, while also listing certain OS environment variables related to CI and Git; inspect the product’s exact fields instead of assuming all environment data is either included or excluded.
Are screenshots covered by the same masking rules as test logs?
Not necessarily. TestingBot says detectable secrets and PII in text are masked but screenshot crops are not, so a text-redaction statement alone does not answer whether visual artifacts are protected.
Does deleting source files also delete data derived from them?
Not always. CloudBees says original source and test-file content is deleted after it is processed into embeddings, while the embeddings are retained; ask separately about derived representations and metadata.
Can I rely on a vendor privacy page as a contract guarantee?
A public policy describes the vendor’s stated practices, but coverage can depend on edition, deployment, region, and contract. For regulated or confidential data, check the current DPA and applicable product terms for binding scope.
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