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Sometimes—but never by default. An AI vulnerability scanner is safe for confidential code only when you can document exactly what data it processes, where that data goes, how long it is retained, which model providers or subprocessors can access it, and how deletion and access controls work. Treat a hosted scanner as receiving source code or security context unless its technical documentation and contract clearly establish a narrower flow. Local or customer-managed scanning reduces third-party transfer, but it still runs with local permissions and must be isolated and governed.
What “safe” means for a confidential repository
Confidentiality is not a single vendor checkbox. A scanner can avoid model training and still expose code through temporary storage, prompts, logs, support access, subprocessors, mis-scoped credentials, or a report retained in another system. Evaluate safety across the whole path from checkout to final finding.
- Data flow: identify every service, runner, model endpoint and integration that handles files or findings.
- Data minimisation: establish whether the scanner needs complete files, selected snippets, abstract syntax trees (ASTs), file names, code locations, HTTP messages or only vulnerability metadata.
- Lifecycle: define collection, processing, temporary storage, backups, logs, deletion and legal-hold behaviour.
- Access: verify tenant isolation, operator permissions, support access, audit logs and credential scope.
- Evidence quality: keep AI suggestions inside the normal SAST, dependency, secret-scanning and human-review process.
If a vendor cannot answer these questions for the exact product edition and configuration you plan to use, you do not yet have enough assurance to upload proprietary code.
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Does an AI scanner upload a private repository?
A hosted feature may send more than the line that triggered a warning. Depending on the product, model requests can contain source files or snippets, file names, code locations, HTTP messages, prompts and vulnerability metadata. Some products correlate SAST and DAST results, so dynamic-test context can be transmitted alongside source-derived information.
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“The scanner runs in our CI” does not prove that processing is local. A runner may check out code locally and then call a vendor API for analysis. Inspect network destinations, plugin settings, CI variables and the product’s documented architecture. Ask the vendor to mark each item as sent, stored, logged or not collected:
- Complete files and generated files.
- Selected snippets, ASTs, symbols, embeddings or prompts.
- Repository names, branch names, commit identifiers and file paths.
- HTTP requests and responses from DAST correlation.
- Secrets, credentials or environment variables accidentally included in context.
- Findings, traces, remediation text and user comments.
- Operational telemetry, error logs, support bundles and backups.
Run a test with a uniquely identifiable, non-production repository and inspect outbound connections and the resulting logs. Use a sanitized repository for validation; do not use real secrets merely to test whether redaction works.
Hosted, local and hybrid scanners compared
| Deployment | Typical data path | Confidentiality advantage | Security and operational trade-off | Questions to resolve |
|---|---|---|---|---|
| Hosted SaaS | Repository or CI runner sends files, snippets or security context to the vendor service and its configured model infrastructure. | Fast deployment, managed updates and access to broad model capability. | Third-party transfer, provider and subprocessor access, retention and regional-processing exposure. | Which inputs leave the runner? Which providers process them? Are prompts and findings retained? Where and for how long? |
| Local scanner with local model | Scanner, model endpoint and data remain in infrastructure you operate. | Strongest control over external transfer and residency. | You must secure the operating system, model, dependencies, plugins, update channel, credentials and internal access. | Can the process make outbound connections? What local files and environment variables can it read? How are model and scanner updates verified? |
| Hybrid scanner | Core analysis runs locally or in a private runner; selected context is sent to a provider for AI analysis. | Scope, redaction and routing can reduce the amount transferred. | A small, sensitive snippet can still reveal proprietary logic; defaults may change as features evolve. | Can you configure the exact data classes, redaction rules, region and provider? Are those choices enforced and logged? |
Local execution reduces transfer exposure; it does not make a scanner automatically trustworthy. A local process inherits the permissions of its user, runner and plugins. OpenAI’s Codex Security policy, for example, instructs operators to scan only repositories they own or are explicitly permitted to assess and to keep credentials, private source and scan results out of model requests, logs, reports and unauthorised network destinations. Those are useful operating rules for any local or hosted deployment.
“Not used for training” is only one question
A statement that customer code is not used to train a model addresses model-improvement use, not every other disclosure path. Obtain separate answers for:
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Retention and deletion
Ask whether source, prompts, snippets, embeddings, findings and logs are retained; the exact duration; whether backups follow the same schedule; and how deletion is demonstrated. A one-session storage promise is materially different from an account-level retention period. Checkmarx states that uploaded source code is stored only for the duration of the scan session. That statement still needs to be matched to the product, plan, region and associated logs you will use.
Model providers and subprocessors
Determine whether the scanner calls an external model provider, a customer-selected endpoint or a vendor-operated model. Ask whether providers may use data for training, abuse monitoring, human review or service debugging. PortSwigger says it uses providers that can guarantee data is not used for training; confirm what other processing and retention terms apply to the specific Burp AI feature.
Access, isolation and geography
Confirm encryption in transit and at rest, tenant isolation, regional processing, administrator and support access, and immutable access logs. A vendor may prohibit training yet still allow a support engineer to view a stored prompt, or replicate data to a region your contract does not permit.
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Contract and configuration alignment
Read the product documentation, data-processing terms and security addendum together. Marketing language may describe a default while your edition, connector or optional AI feature follows another path. Record the approved configuration and review it when the vendor changes models, subprocessors or hosting regions.
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Vendor statements illustrate why the details matter
Public documentation provides useful evidence, but it is not an independent audit and does not guarantee that every customer configuration is safe.
- Snyk: its documentation says SAST/DAST correlation can process “Customer source code” and related vulnerability metadata, while stating that “Customer proprietary software code is not retained by the provider of these AI models.” Ask how that promise applies to Snyk’s own services, logs, regions and integrations.
- Checkmarx: the statement that uploaded source code is stored only for the duration of the scan session is a clear retention claim. Verify the session boundary and whether prompts, findings, telemetry or backups are covered.
- PortSwigger: its description of providers that can guarantee data is not used for training answers the training question, not the questions of access, location, retention or deletion.
- OpenText Fortify: its AI-powered SAST documentation explains that selected source files are passed to a configured large language model and that off-cloud users can provision their own model instance. “Selected” should be translated into an explicit file and snippet policy before approval.
Procurement checklist before any proprietary upload
- Request a data-flow diagram. It should name the scanner service, CI runner, model endpoint, subprocessors, storage systems and support paths.
- Inventory every transmitted object. Get a written list covering files, snippets, ASTs, prompts, file paths, HTTP messages, logs and findings. Ask what is never sent.
- Document retention and deletion. Record storage duration, backup handling, deletion triggers, evidence supplied after deletion and any legal-hold exception.
- Check model-use terms. Confirm whether source, prompts or findings are used for training, abuse monitoring, debugging or human review, and whether the answer differs by plan or region.
- Verify protection controls. Confirm encryption in transit and at rest, tenant isolation, regional processing and access logging. Ask who can access raw content and under what approval.
- Use least privilege. Prefer read-only repository tokens, short-lived credentials, isolated runners and narrowly scoped project access. Keep secrets out of the checkout and scanner environment.
- Validate with a sanitized repository. Include harmless marker strings and representative code patterns, then inspect network traffic, logs, reports and deletion behaviour.
- Keep independent controls. Require normal SAST, dependency and secret scanning plus human review; treat the AI feature as an additional analysis path, not the sole gate.
Controls for a hosted deployment
Limit what the scanner can read
Run scans from an isolated job with a read-only token. Exclude generated artifacts, documentation and unrelated repositories when the product permits it. Prevent environment variables containing credentials from being inherited by scanner plugins or model adapters. If redaction is available, test it rather than assuming it removes secrets from prompts, logs and error reports.
Control where requests go
Allow-list the documented service and model destinations at the runner’s network boundary. Alert on new destinations and on unusual upload volume. Record the scanner version, AI feature setting, model endpoint and region for each run so a later investigation can reconstruct the data path.
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Restrict access to reports because remediation text can disclose architecture even when source is not retained. Set report permissions independently from repository permissions, and define retention for exported findings in ticketing, chat and archive systems.
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How to secure a local or off-cloud scanner
- Isolate the process. Use a dedicated runner, container or virtual machine with a minimal filesystem view and no unnecessary outbound network access.
- Constrain credentials. Use a read-only checkout identity and remove cloud, package-registry and deployment credentials from the environment before starting the scan.
- Audit plugins and hooks. A scanner extension, repository hook or model adapter can make its own network request or read files outside the checkout. Approve versions and verify checksums through your normal software-supply-chain process.
- Protect the model endpoint. Require authentication, encrypt local traffic where applicable, restrict callers and log administrative access. Treat model weights, prompt templates and vector stores as sensitive assets.
- Manage updates deliberately. Pin scanner and model versions for regulated or high-confidentiality work, review release notes, and test updates against the sanitized repository before promotion.
- Destroy temporary data. Locate caches, prompt histories, crash dumps and intermediate artifacts on the runner, then delete them according to your repository’s retention policy.
Off-cloud operation shifts responsibility rather than eliminating it. A compromised runner or over-privileged plugin can expose the same source code without any SaaS provider being involved.
Keep AI findings inside established security review
AI-assisted SAST can suggest a vulnerability, explain a trace or propose remediation, but it is not a confidentiality guarantee or a blanket detection guarantee. Require a human to verify exploitability, affected code, false-positive risk and the proposed change. Compare AI results with established SAST rules, dependency analysis and secret scanning. Record the scanner and model version when a finding influences a release decision; model behaviour can change after an update.
Do not paste proprietary source into a general-purpose chat window to obtain a second opinion unless that separate service has passed the same data-flow and contract review. Keep credentials, private source and scan results out of model requests, logs, reports and network destinations that the operator has not authorised.
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| Repository situation | Default posture | Minimum evidence before use |
|---|---|---|
| Public or deliberately open-source code | Hosted scanning is generally easier to approve, subject to normal access and integrity controls. | Understand report access, model use and cost; prevent secrets from entering the scan. |
| Internal business code | Hosted or hybrid can be acceptable when transmission and retention are contractually bounded. | Written data inventory, retention/deletion terms, provider list, region and access logs. |
| Highly confidential product or customer code | Prefer local or customer-managed execution, or a hosted service with demonstrably narrow, redacted inputs. | Validated data-flow test, least-privilege runner, isolation, approved region and documented incident response. |
| Code subject to a strict contractual or regulatory residency rule | Do not upload until processing location and every subprocessor are explicitly allowed. | Binding terms, region enforcement, deletion evidence and an architecture review for each integration. |
This is a risk decision, not a product ranking. A local model may be the wrong choice if your team cannot patch and monitor it, while a hosted service may be acceptable for a lower-confidentiality repository when its controls are verified.
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Common failure modes and what to do
The vendor says “zero retention,” but logs contain code
Ask whether zero retention applies to model requests only or also to application logs, support bundles, telemetry, backups and findings. Disable diagnostic uploads where possible, rotate any exposed credentials and request deletion confirmation for affected artifacts.
A CI token grants access to every repository
Revoke it, issue a project-scoped read-only token and rerun the scanner in an isolated job. Review repository and scanner logs for the period in which the broad token was active.
A local scanner makes unexpected network calls
Stop the job, preserve network and process logs, and inspect plugins, hooks, update checks and model configuration. Re-run only after outbound destinations are allow-listed and the software is approved.
An AI finding includes a secret or unrelated file
Remove the secret, rotate it, restrict report access and determine whether the content was sent to a provider or retained in logs. Adjust checkout scope and redaction rules, then test with marker data before resuming production scans.
Terms changed after approval
Recheck retention, model-provider, regional-hosting and subprocessors whenever the vendor adds an AI feature, changes a model or updates its policy. Freeze the integration or revert to a previously approved configuration until the review is complete.
Bottom line
Use an AI vulnerability scanner with confidential source code only after proving its complete data path and constraining its permissions. Hosted tools can be practical when transfer, retention, provider access, region and deletion are explicit and contractually acceptable. Local and hybrid designs reduce external exposure but demand stronger runner, model, plugin and credential controls. In every deployment, keep AI assistance behind least privilege, established scanning and human review.
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