1. AI Code Limitations and Security Risks
While AI-assisted coding tools can efficiently prototype and generate software, they inherently lack engineering judgment and context. AI-generated code often reproduces structural vulnerabilities, misses crucial non-functional requirements (such as Row Level Security and rate limiting), and can introduce hardcoded credentials or unverified dependencies directly into your application.
2. Database Vulnerabilities and Data Protection
Applications built through AI prompt generation frequently fail to sanitize user inputs, leaving the backend highly exposed to database exploitation and data breaches. Implementing robust database access policies, encryption, and secure storage remains entirely the responsibility of the principal party deploying the code base.
3. Client Liability and Ownership
As the principal deploying the software, the client assumes full liability for the application's performance, security posture, and regulatory compliance (e.g., GDPR, CCPA). You acknowledge that you are responsible for any damages, security incidents, or data loss that may arise from deploying un-audited or insufficiently reviewed AI-generated code into a production environment.
4. The Necessity of Human Review and Audits
AI-generated code should never be treated as production-ready without thorough testing and security auditing. It is highly recommendedβand sometimes required by platforms like Appleβto have technical professionals audit the code infrastructure for authorization bypasses and deep logic flaws before release.