Trust Center

Clear answers on privacy, AI guidance, and security.

This page brings together the trust signals schools, bootcamps, and learners usually want before rollout: plain-language privacy information, support routing, AI-use expectations, and a published security disclosure path.

Privacy Policy Support

Last updated March 14, 2026.

Principles

How InAppCode builds trust

Student-friendly onboarding

Core practice flows are designed to start without a forced account-creation step.

Plain-language policies

Privacy, support, and usage expectations are documented in pages that buyers can audit quickly.

Responsible AI guidance

AI feedback is positioned as coaching support, not a substitute for teacher review or grading policy.

Public security path

Security researchers and deployment owners have a published route for responsible disclosure.

Privacy

How we think about learner data

InAppCode aims to reduce unnecessary personal-data collection by removing account friction from the core learner experience. To operate the service, the app and website may still process submitted code, challenge selections, usage events, diagnostics, and security logs.

Full details live in the privacy policy, including service-provider processing, retention notes, and education-setting reminders for managed rollouts.

AI Guidance

How AI feedback should be used in classrooms

  • Use AI feedback as learning support, hints, and practice guidance.
  • Do not treat generated output as guaranteed correct, complete, or policy-compliant.
  • Review AI-assisted work before using it for grading, assessment, or production use.
  • Do not submit secrets, regulated data, or confidential material in code prompts.

Security

Security reporting and incident communication

Vulnerability reports should use the published channel in security.txt. That file points to the security-reporting section of the support page and is the canonical disclosure path for the public website.

  • Include clear reproduction steps, affected pages or flows, and expected impact.
  • Avoid public disclosure before the issue has been reviewed and remediated.
  • If you run a managed deployment, also notify the program owner responsible for that environment.