To use AI effectively, development teams must understand where AI adds real security value and where human expertise remains essential. Below, we explore four key areas where AI supports secure development, along with the limitations that must be considered.
AI for Secure Code Generation
AI role
AI-powered code generation tools accelerate developers by producing boilerplate code, suggesting functions, and completing code snippets, with new capabilities being rapidly deployed. This reduces time spent on repetitive tasks and can help minimize simple human errors in day-to-day development.
Caution
AI-generated code is not automatically secure. Models may recommend insecure or outdated patterns, even when prompts are well crafted. All AI-generated code should be reviewed carefully, particularly for security-critical components such as authentication, authorization, cryptography, and input validation.
AI-Driven Static Analysis for Security
AI role
AI-enhanced static analysis tools analyse code in real time or during CI/CD pipelines to identify common security vulnerabilities early. By learning from past findings, AI can adapt to new attack patterns and propose direct mitigations.
Caution
AI static analysis may miss business logic vulnerabilities and can produce false positives or false negatives. Manual code review and dynamic security testing remain essential to ensure accurate and meaningful results.
AI in Threat Modeling
AI role
AI assists with threat modelling by identifying potential attack vectors and generating threat scenarios. This helps agile teams accelerate threat modelling activities and receive mitigation suggestions based on similar systems and known threats.
Caution
AI lacks context and domain-specific knowledge. Human oversight is critical to validate assumptions, identify subtle threats, and maintain a clear understanding of the application architecture and data flows.
AI for Unit Testing and Security Testing
AI role
AI can generate unit test cases, simulate edge conditions, and automate fuzzing. This improves test coverage and helps uncover unexpected behaviors earlier in the software development lifecycle.
Caution
AI-generated tests may still overlook critical business logic and complex edge cases. Manual QA, exploratory testing, and security-focused testing should always complement automated approaches.
Using AI to Build Secure Software – Safely
AI is already helping organizations build software faster. The challenge is ensuring it also helps teams build more secure software on a long-term basis.
When combined with strong security practices and human expertise, AI can significantly enhance secure development workflows rather than weaken them.
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