Protecting data in an AI-built app takes two separate efforts: secure the app while it handles information at runtime, and control what your AI coding assistant can see while you build it. Start by collecting and retaining less data, then limit access, protect the data you do need, independently review security-critical code, and keep maintaining the app after launch. AI assistance does not make an app inherently unsafe, but generated code and passing tests are not proof that it is secure.
1. Map the data your app handles, then reduce it
Before adding protections, find out what the app actually collects, creates, logs, transmits, and retains. Include information in analytics, crash reports, support tools, backups, and third-party services—not just fields in the signup form. For each item, record its purpose, who or what can access it, where it goes, how long it is kept, and how it is deleted.
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- Remove fields and permissions that do not serve a necessary feature.
- Set a retention period tied to a real product or legal need; delete information when that need ends.
- Check logs, exports, backups, and vendor integrations for copies that could outlast the main account record.
- For health-related features, consider whether de-identification, less precise location, or aggregated location can meet the need. Removing names alone does not establish that information cannot be re-identified.
The Federal Trade Commission’s App Developers: Start with Security guidance puts the principle plainly: “Don’t collect or keep data you don’t need.” The brochure was published in May 2017; its data-minimization advice is useful, but it is not a current protocol specification or a legal safe harbor.
2. Find out what your AI coding assistant can access
An app’s runtime data flow is not the only exposure path. During development, project files, terminal output, and code context may be sent to an assistant provider. OWASP’s Secure Coding with AI Cheat Sheet says: “AI coding assistants send code context (open files, project structure, terminal output) to the model provider’s API.” This describes a risk to investigate, not a claim that every assistant sends every kind of context in every configuration.
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Inspect the actual tool and its settings
Before using an assistant on a project, check its current documentation and settings. Determine whether it can send open-file contents, broader project context, terminal output, or other data, and what retention or training controls are available. Behavior varies by product and configuration; do not infer it from the editor interface alone.
Exclude secrets and sensitive material
- Use the assistant’s own exclusion or ignore controls for
.envfiles, credentials, private keys, production data, and sensitive directories. - Keep secrets outside ordinary project files where possible, using environment variables or a secrets manager. Do not paste credentials into a prompt or a terminal session visible to the assistant.
- Do not open sensitive files in an assistant-enabled context unless you have established what the tool can transmit.
- Do not rely on
.gitignoreto protect files from an AI tool: it controls Git behavior, not necessarily what an assistant can read.
If the assurance level warrants it, use request logging or a network proxy to inspect outbound traffic. For highly sensitive code, OWASP advises considering self-hosted or air-gapped tools. Those choices change the hosting and access model; they do not remove the need to inspect the tool’s behavior and your development workflow.
3. Limit permissions and access in the app
Apply least privilege to both device permissions and access to stored information. Request a permission only when a feature needs it, and prefer a narrower, platform-mediated choice when available—for example, letting a person select one contact rather than granting access to the entire address book. Set sharing to private by default when that fits the feature.
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Design authentication and authorization around the consequences of account misuse. Users and administrators should have only the access they need. Plan how account recovery, password resets, access revocation, lost devices, and account closure work; avoid default credentials. Never store plaintext passwords. The FTC’s mobile health app guidance recommends salted hashes and slow hash functions for passwords.
Restrict API access to trusted clients or other parties with a legitimate need, but do not treat a client-side check as a substitute for server-side authorization. Platform security features can help, but they must be configured correctly and tested.
4. Protect data on the way in, on devices, and on servers
Use current, industry-standard transport encryption for sensitive data, configure certificate validation correctly, and protect locally stored information with platform mechanisms where available. Secure the server, database, and administrative interfaces as well as the app client; protecting one layer does not secure the others.
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If you use a cloud provider, document which controls and updates the provider handles and which remain your team’s responsibility. Test input handling for common flaws such as injection and cross-site scripting. The FTC’s 2017 app security guidance is a foundational resource, not a recommendation for a particular current protocol version. Check current official platform and cryptography documentation before choosing implementation details.
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5. Review AI-generated changes independently
Treat generated code as a proposal, not as a security review. Use human review and independent analysis for authentication, authorization, input validation, cryptography, and other security-critical logic. Add adversarial tests designed to probe misuse and unexpected inputs rather than relying only on tests generated alongside the feature. A passing test suite shows that its tested cases passed; it does not establish that untested attack paths are safe.
Inspect changes that run with elevated trust
Give special scrutiny to changes in build scripts, package hooks, CI workflows, containers, and deployment configuration. These files can execute automatically or in privileged environments. Review what they run, what secrets they can access, and where they send artifacts or data. Review dependency changes as well, including new packages and version updates, rather than accepting them solely because the app builds.
OWASP’s AI coding guidance recommends explicit review and controls for generated changes. NIST’s Secure Software Development Framework (SSDF) 1.1, published in February 2022, provides a broader process reference. Its AI-specific companion, NIST SP 800-218A, published in July 2024, supplements the SSDF with considerations for developing AI models and systems. It is a process reference, not a certification that an app is secure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Check readiness before launch
Use a launch review to catch gaps across data handling, code, operations, and legal scope. For an app that handles sensitive information, an independent application security review or penetration test may be a useful additional step if its scope matches the app and the reviewer’s claims are verified. Neither a paid assessment nor an automated scan guarantees security or compliance.
- Data: Can you account for collection, logs, third-party transfers, retention, deletion, and backups?
- Access: Are permissions necessary, sharing defaults appropriate, and authorization enforced where the data is accessed?
- Secrets: Are production credentials kept out of source code, prompts, logs, and exposed build contexts?
- Changes: Have security-critical code, dependencies, build scripts, and deployment settings been reviewed and tested independently?
- Operations: Is someone responsible for security, monitoring notices, receiving vulnerability reports, and shipping fixes?
- Legal scope: Have you assessed relevant jurisdictions, data types, users, vendors, and business relationships?
7. Keep the app secure after release
Security work continues as dependencies, app features, vendors, and threats change. Assign a person responsibility for it. Keep libraries, server software, and app code updated; monitor vulnerability notices; and establish a clear way for users or researchers to report flaws. Decide how you will assess, prioritize, and release fixes. Revisit access and retention when features or vendors change, since a new integration can alter where data goes and who can use it.
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The FTC’s app security guidance and NIST’s SSDF both support treating secure development as ongoing work rather than a one-time launch task.
8. Check which legal requirements apply
Legal obligations depend on the product, its users, the information it handles, its business model, and the jurisdictions involved. Before launch, identify whether the app serves children, processes health or financial information, or shares data with vendors or business partners, and obtain qualified legal advice where needed.
Do not assume that every consumer health app is covered by HIPAA. The FTC’s health-app guidance discusses HIPAA de-identification requirements for entities covered by HIPAA; that does not make every health app a covered entity. The FTC also notes that rules for children’s, health, and financial data can be more complex. The available facts about an unspecified app cannot determine its legal status.
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