Meta has developed secure-by-default frameworks to enhance security in mobile applications while keeping developer experience intact. These frameworks wrap potentially unsafe OS and third-party functions, making security seamless for developers. They mirror existing APIs, which minimizes friction and complexity, promoting wider adoption among developers. However, implementing these frameworks poses challenges, especially in balancing security with usability and performance. If a framework is too complex, developers might bypass it; if it’s too simple but resource-heavy, it could deter use for performance reasons. AI tools are crucial in scaling the adoption of these frameworks across Meta’s extensive codebase. They help identify insecure patterns, suggest secure replacements, and monitor compliance. This automation aims to enhance security enforcement consistently and efficiently across applications. One prominent example of a secure framework is SecureLinkLauncher (SLL), designed to prevent intent hijacking in Android applications. SLL wraps Android's intent launching methods with security checks, ensuring sensitive data isn’t exposed through improper intent targeting. Developers interact with SLL using familiar method calls, maintaining the ease of use while enhancing security. For instance, instead of calling the standard startActivity() method, they use SecureLinkLauncher to ensure that intents target the correct internal apps. This approach effectively prevents data leaks to malicious applications by enforcing strict scope verification. The design principles behind these frameworks emphasize minimizing cognitive load for developers and ensuring compatibility with stable public APIs, which helps maintain software security across different app versions.