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AI testing platforms can send far more than test scripts. Depending on the product and feature, cloud-processed data may include source or test-file content, commit and branch metadata, test names and outcomes, input values, execution logs, screenshots, rendered page structure, network traffic, prompts, attachments, tool calls and AI results. A desktop authoring tool, a cloud runner, an AI assistant and test-selection analytics can each use a different data path. The only reliable answer is feature-specific: identify what triggers transmission, which systems receive it, how long each copy remains, where it is processed and whether it can be used for model training.
What information can an AI testing platform send to the cloud?
The payload is usually a combination of test content, execution evidence and context used by an AI feature. A platform may receive a prompt and a small text excerpt for failure analysis, or it may process a much broader run record containing browser state and network activity.
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- Test and source content: test definitions, scripts, requirements, step descriptions, source files or selected file contents.
- Execution data: pass/fail outcomes, durations, suite associations, command events, terminal output, console logs and CI environment details.
- Application evidence: screenshots, video, rendered DOM and CSS, page labels, controls, properties, test values, HTTP requests and responses, and network traffic.
- Project context: commit SHA, branch, timestamps, author details, changed-file lists, repository configuration and project terms.
- AI task material: prompts, attached files, resources from HTTP history or a site map, agent messages, tool calls and tool results.
These categories can expose credentials, personal information, business rules and application behavior even when a vendor says it does not collect repository code. A screenshot or HTTP response can contain the same secret that a source file would reveal.
Does every platform send source code or test files?
No. Some features transmit selected source or test-file content; others operate on metadata or captured run artifacts. Product boundaries matter: a locally authored project, a cloud execution service and an optional AI assistant should not be treated as one data flow.
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Local authoring versus cloud execution
Katalon describes Studio as a desktop application in which project data is typically kept on the engineer’s workstation. Its separate Katalon True and Cloud Execution services can load test suites and related data from external repositories, execute scripts in cloud environments and return execution results to Katalon True. The documented execution path is therefore different from an AI prompt path. See the Katalon Security and Privacy FAQ for the product-specific distinctions.
When a vendor says it does not capture code
Cypress says recorded runs include terminal output, test results, test definitions, configuration other than Cypress environment variables, screenshots, videos and CI or Git-related operating-system variables. Test Replay can add rendered DOM and CSS, Cypress commands and events, network traffic and browser console logs. Cypress states that Cloud AI can share test content with a session-bound AI model when enabled, while also stating that Cypress Cloud does not capture application-under-test code or code in associated repositories. That statement limits repository-code collection; it does not mean a run is free of sensitive data. Details are in Cypress’s data-storage and masking documentation.
What do major AI-enabled testing features document?
The table below separates the named feature, its documented payload and the qualification needed to interpret the claim.
| Platform or feature | Documented cloud data or behavior |
|---|---|
| Testmo AI | User-submitted input and optional context such as domain terms or project information are transmitted to an LLM. Testmo says AI runs only when explicitly chosen and that customer AI inputs and outputs are not used to train or improve underlying LLMs. Testmo policy |
| Katalon | Studio project data is generally kept on the workstation. Katalon True stores test cases, suites and execution information; Cloud Execution loads suites and related data from repositories, runs scripts in cloud environments and transfers results to Katalon True. AI-service exclusions are described separately. Katalon FAQ |
| Cypress Cloud and Test Replay | Recorded-run artifacts include terminal output, results, definitions, non-secret configuration, screenshots, videos and CI/Git environment variables. Test Replay can include DOM/CSS, commands, events, network traffic and browser console logs. Cloud AI is an enabled, session-bound feature. Cypress documentation |
| CloudBees Smart Tests | The CLI sends commit metadata, test results and source-code content. CloudBees says source and test-file contents are processed into vector embeddings and then deleted; embeddings and metadata are retained. Metadata can include commit SHA, branch, timestamps, author details, changed-file lists, test paths and names, outcomes, durations and suite associations. CloudBees documentation |
| TestingBot AI | Opt-in failure analysis sends a small text-only slice of a test. Visual region naming can send screenshot crops around changes. The trust center says detectable secrets and PII in text are masked, but screenshot crops are not; AI-provider inputs and outputs are deleted within 30 days. AI data may go to US subprocessors while core testing infrastructure is in Europe. TestingBot Trust Center |
| PortSwigger Burp AI and Burp AT | Burp AI can receive an HTTP request or vulnerability description. Burp AT task data can include prompts, HTTP-history or site-map resources, attachments, agent messages, tool calls and results. PortSwigger says Burp AT does not upload the Burp project, but sensitive requests, responses, resources and files may be sent without redaction. The disclosure is dated October 22, 2026. PortSwigger documentation |
| AssertThat Autonomous Test Management | Test data is stored in Atlassian Forge storage. AI uses Atlassian-hosted models; prompts and results stay within Atlassian, and the application says it makes no calls to external services. This scope applies to the named Forge application. AssertThat security documentation |
| Tricentis Tosca Agentic Test Automation | Inputs can include test cases and step details, requirements, execution logs and, for execution-list analysis or test generation, 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, up to 30 days as traces or feedback and up to 30 days in Microsoft Azure OpenAI systems. It says queries are not used for model training and that PII is anonymized or redacted. Tricentis policy |
Why does the purpose of the feature change the data flow?
The same test project can produce different cloud payloads because each feature needs different context. “AI testing” is not a single technical category.
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Cloud execution and run review
A cloud runner needs the suite, dependencies and environment settings required to execute tests. It may return logs, screenshots, videos and results for review. Those artifacts can include rendered pages, form values and server responses even if no source repository is uploaded.
Failure analysis and visual labeling
Failure analysis often sends a narrow text slice, while visual naming can send an image crop. TestingBot’s documentation illustrates why the distinction matters: text masking does not automatically redact pixels in a screenshot. Treat images, videos and browser captures as separate data classes when setting policy.
Test selection and embeddings
CloudBees Smart Tests uses source and test-file content to create vector embeddings that help select relevant tests. CloudBees says the original content is deleted after embedding, while embeddings and commit or test metadata remain. An embedding is not the original file, but it is still derived from proprietary material and should be covered by your review.
Interactive security or agent tasks
Burp AT can combine a prompt with selected HTTP-history or site-map resources, attachments and tool results. Because those resources can include sensitive requests and responses, an agent approval step and narrowly selected scope are more important than the product label “AI.”
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How long do vendors retain AI inputs, outputs and traces?
Retention promises differ by feature and by subprocessors. They are operational statements from the vendors, not independent audits.
- Testmo: its policy says customer AI inputs and outputs are not used to train or improve underlying LLMs.
- TestingBot: its trust center says AI-provider inputs and outputs are deleted within 30 days.
- CloudBees: it says original source and test-file content used for embeddings is deleted, while embeddings and metadata are retained.
- Tricentis Tosca 2026.1: the policy allows 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. It says queries are not used for model training.
- Cypress: retention and masking depend on the Cloud feature and account controls; its documentation describes the artifact classes and controls rather than a universal retention period.
Check whether a promise covers the vendor only or also the model provider. A statement that a vendor does not train its models does not automatically describe the provider’s separate processing terms.
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Location can change by tenant region, model provider and feature. AssertThat says its Forge application keeps prompts and results within Atlassian and uses Atlassian-hosted models without external service calls. TestingBot says core testing infrastructure is in Europe but AI data can be sent to US subprocessors. Tricentis describes tenant-region processing alongside Microsoft Azure OpenAI systems and notes that the Tosca Cloud policy may involve additional providers and regions.
Ask for the exact deployment and version. The Tosca policy cited here is for version 2026.1, and PortSwigger’s cited trust disclosure is dated October 22, 2026. A contract or data-processing addendum may narrow or expand the practical route compared with a general help page.
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What controls reduce accidental disclosure?
Controls are usually feature-specific, so verify them one by one instead of assuming a global privacy switch.
- Explicit initiation: Testmo says “AI only runs when you explicitly choose to use it. Nothing happens automatically.” Prefer user-triggered actions for unapproved projects.
- Opt-in settings: TestingBot documents opt-in AI. Keep optional analysis disabled until its payload and provider route are approved.
- Role and approval gates: Restrict who can enable AI, attach files, select HTTP resources or approve agent tool calls.
- Exclusions and masking: Remove production credentials, tokens and personal data before execution. Confirm whether masking applies to logs, prompts, screenshots and videos separately.
- Region and provider selection: Record tenant region, subprocessor country and any customer-supplied model key. Routing outside your selected region can occur at the model layer.
- Deletion requests: Determine whether you can delete run artifacts, embeddings, traces, backups and provider copies, and what timetable applies.
How should you audit a platform before sending proprietary tests?
Use this sequence for each feature, not just once for the vendor account.
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- Map the payload. List source files, test definitions, values, screenshots, DOM/CSS, network traffic, logs, prompts, attachments, metadata and tool results. If a sample run trace or payload viewer exists, inspect it.
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What should a security or compliance review ask vendors?
Request answers that distinguish the vendor’s infrastructure from its AI provider. Ask which fields leave the runner, whether source content is transient or retained as embeddings, whether screenshots and logs are masked, where each subprocessor operates, how deletion propagates, and whether inputs or outputs train any model. Also ask whether the answer changes by edition, tenant region, browser-replay setting, customer API key or contract.
Keep the vendor’s wording attached to the exact feature. For example, Cypress’s statement about not capturing repository code should not be copied into a policy for Test Replay artifacts; CloudBees’ deletion statement for source used in embeddings should not be extended to retained embeddings; and PortSwigger’s Burp AT disclosure should not be applied to every Burp feature.
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Frequently Asked Questions
Are screenshots from AI test runs automatically safe to share?
No. Screenshots can contain account names, personal data, tokens displayed in a UI and business logic. TestingBot’s policy specifically distinguishes masked text from screenshot crops that are not masked, so image handling must be verified separately.
Can embeddings reveal the original source code?
An embedding is a numerical representation rather than the original file, but it is derived from proprietary content and can still be sensitive. CloudBees says original source and test-file content used for Smart Tests embeddings is deleted while embeddings are retained; confirm how access, deletion and tenant isolation apply to those retained vectors.
Does turning off AI stop all cloud transfer from a testing platform?
Not necessarily. Cloud execution, run recording, dashboards and test replay can transmit suites, logs, screenshots, DOM, network traffic or metadata without invoking an AI assistant. Audit each enabled service and recording setting independently.
What is the difference between vendor retention and model-provider retention?
A vendor may delete its copy on one schedule while a contracted model provider has a separate processing or deletion window. Review both the platform policy and the data-processing terms naming its subprocessors, regions and maximum retention periods.
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