Key takeaways
- AI-generated code introduces vulnerabilities in ~45 % of cases, so validate early.
- Automate guardrails with Policy as Code and Netlify Secrets Controller.
- Protect AI endpoints with Netlify WAF and DDoS mitigation.
The rapid adoption of AI development tools has fundamentally changed how we build and deploy applications. While AI can generate code faster than ever before, it has also introduced new security considerations that require immediate attention. Recent research from Veracode shows that AI-generated code introduces security vulnerabilities in 45% of cases, highlighting the critical need for enhanced validation processes. In many respects, good practices for deploying AI-generated code mirror those for human-generated code. If you’re already validating build and runtime security, performing penetration testing, and deploying into compliant environments, you have a solid foundation for the AI era.
However, times are changing fast, and certain areas require heightened scrutiny when using AI-generated code in production. Unlike traditional development where framework choice often drives security decisions, AI-generated applications are more outcome-focused. AI can select optimal frameworks for specific use cases, but this flexibility demands a more comprehensive approach to security validation.
If you already use Netlify Secrets Controller and Netlify WAF, you have a head start. But AI-scale demands automated, continuous validation.
Why AI‑generated code needs new security guardrails
The challenge with AI-generated code isn’t just about the code itself—it’s about the speed and scale at which it’s produced. Human developers trained in security naturally apply secure development patterns, and traditional code review processes help catch vulnerabilities. But when AI generates thousands of lines of code in minutes, our traditional review mechanisms simply can’t keep pace.
This shift requires us to rethink our security approach. We need automated guardrails, enhanced policy enforcement, and more sophisticated validation techniques to ensure AI-generated applications meet the same security standards as human-developed ones. The stakes are high. IBM’s Cost of a Data Breach report found that 97% of organizations experiencing breaches involving AI tools lacked proper AI access controls, demonstrating the critical importance of implementing comprehensive security measures from the start.
Critical areas for AI security focus
Data architecture and compliance
The foundation of any secure application, AI-generated or not, starts with proper data architecture. It’s crucial to ensure that the design and data flows meet compliance and data privacy requirements from the ground up.
Mapping out data flows within the application is key to success and can be front-loaded into design requirements provided to the AI coding agent. This approach allows you to enforce compliance requirements even during the code generation phase, potentially through Model Control Protocols (MCP) that guide AI behavior.
However, assumptions must always be validated. Deploy your AI-generated application to a staging environment and conduct targeted penetration testing before any real user data enters the system. This validation step is essential for compliance and security assurance. There have been more than one case recently where AI agents without proper guardrails deleted production data, causing outages and reputational harm.
Vulnerability management: Build and runtime
Effective vulnerability management requires a two-pronged approach covering both build-time and runtime security.
Build-Time security
Static Application Security Testing (SAST) tools, which analyze source code for security vulnerabilities without executing the application, should be integrated within both your build environment and AI code editor through MCP integration. Focus on tools that can identify application security vulnerabilities covered in the OWASP Top 10, as well as vulnerabilities in underlying frameworks and code dependencies.
The emergence of AI-generated code security tools adds significant capability beyond traditional static code analysis and dependency graph analysis. These tools can understand context and identify subtle security issues that might be missed by conventional scanners. Unlike legacy SAST tools that rely on pattern matching and predefined rules, AI-powered security tools can analyze code semantically, understanding the intent and logic flow to identify complex vulnerabilities like business logic flaws, authorization bypasses, and data flow issues that span multiple functions or modules.
Runtime security
Nothing substitutes for comprehensive application security penetration testing. Emerging AI security vendors are beginning to automate portions of this process through AI agents, similar to what Dynamic Application Security Testing (DAST) tools have provided historically.
The most effective approach currently combines human expertise with AI-assisted tools. Professional offensive security tools like Burp Suite now include AI-assisted plugins that help human testers work more efficiently. This human-controlled, AI-assisted approach yields the best results for identifying runtime vulnerabilities and validating security assumptions around data privacy.
Avoid debugging with real-time alerts
Policy as Code
Policy as Code (PaC) represents a critical security guardrail for AI-generated code. Traditional human review processes, while still important for code quality, cannot efficiently review every line of AI-generated code for security issues.
The solution lies in automated Policy as Code systems that ensure AI-generated code output conforms to security and compliance requirements. These guardrails, proven effective in CI/CD pipelines for infrastructure security, can be applied within AI code editors or build pipelines through a MCP integration.
Secret management
Proper secret scoping is essential for AI-generated applications. Secrets and tokens must be scoped to only the parts of the application that require them. Globally scoped variables create unnecessary risk exposure. This issue is particularly concerning given that 74% of cybersecurity leaders report being aware of sensitive data being inputted into public AI models despite having established protocols to prevent it. Secret scanning is equally important. At Netlify, we’ve observed that many AI-generated projects from customers unintentionally expose secrets in publicly accessible assets. We implemented Secret Scanning in our build process to proactively identify and alert customers to potential exposure issues.
For secret scoping, solutions like Netlify’s Secrets Controller allow you to precisely control secret access—limiting exposure to specific build phases or functions as needed.
Building a sustainable AI security program
The key to successful AI security lies in automation and integration. Manual processes that worked for traditional development simply don’t scale to the speed and volume of AI-generated code. Your security program must evolve to include:
- Automated policy enforcement during code generation
- Real-time security validation in CI/CD pipelines
- Continuous monitoring and testing of deployed applications
- Regular updates and patch management processes
- Training programs that help developers understand AI-specific attack surfaces
Runtime environment protection
Once deployed, your application needs active protection against evolving threats.
Web application firewall and DDoS protection
A core tenet of the CIA security model (Confidentiality, Integrity, Availability) is availability. Active protections that mitigate OWASP Top 10 attacks, maintain website availability under DDoS attacks, and manage bot traffic are essential components of a comprehensive security strategy.
The Netlify WAF can be configured to address the specific attack patterns targeting composable architectures that are often favored by AI-generated applications. For example, AI-generated applications frequently implement serverless functions and API-first architectures that can be vulnerable to injection attacks targeting JSON endpoints or GraphQL queries. The Netlify WAF includes OWASP Core Rule Set protections that can detect and block these attacks before they reach the backend application logic.
Continuous security validation
Application security penetration testing should be conducted regularly, not just as a one-time validation. Consider augmenting traditional penetration testing with bug bounty programs and continuous testing models—both human-led and automated approaches have their place in a mature security program.
Vulnerability management extends beyond initial deployment. Regular updates to frameworks and dependencies, combined with frequent deployment cycles, help protect against newly discovered runtime vulnerabilities.
Conclusion
Securing AI-generated applications requires a comprehensive approach that builds on traditional security practices while addressing the unique challenges of AI development. The speed and scale of AI code generation demands automated security controls, continuous validation, and proactive monitoring.
Deploy your first AI‑generated app on Netlify and use this checklist to validate your security posture or talk to us about Enterprise if you’re adopting AI development workflows.
Success depends on integrating security throughout the AI development lifecycle—from design requirements fed to LLMs through runtime protection of deployed applications. Organizations that implement these practices today will be better positioned to harness the power of AI while maintaining the security standards their customers expect.
The future of web development is AI-augmented, but it doesn’t have to be less secure. By implementing the practices outlined in this checklist, you can confidently deploy AI-generated applications that meet the highest security standards.
Ready to enhance your AI security posture? Start by assessing your current capabilities against this checklist, then systematically address any gaps through automated tooling and enhanced security practices.