Security overview
Build trust with evidence.
Security work is scoped to the actual product, data, threat model, and deployment boundary. The practices below describe ZamDev AI's engineering approach; they are not a blanket certification or guarantee for every client system.
Data authorization
Privileged database credentials stay on trusted servers. Sensitive data paths are expected to enforce authorization at the database or server boundary, not only in the interface.
Secret management
Production credentials belong in managed environment storage. Repository history, browser bundles, logs, webhook URLs, and client-visible configuration are reviewed for accidental exposure.
Delivery gates
Projects can include type checking, linting, automated tests, dependency thresholds, production builds, migration review, rollback steps, and post-deployment smoke checks.
AI system controls
AI workflows are designed around representative evaluations, structured validation, explicit tool permissions, human approval for sensitive actions, observable failures, and cost controls.
Webhook and email handling
Inbound integrations should authenticate requests, limit payloads, validate participants, deduplicate events, escape untrusted content, and expose retryable failures instead of silently dropping them.
Evidence boundaries
A code review, automated scan, checklist, or launch-readiness audit is not described as a penetration test or compliance certification unless that exact scope was separately contracted and performed.
Responsible disclosure
If you believe you found a security issue on zamdevai.com, email hello@zamdevai.com with the affected URL, impact, and safe reproduction steps. Do not access data that is not yours, disrupt service, or include active credentials in the report.
For a product review, start with the AI codebase hardening scope or request a codebase risk review.