Application Security Strategies Are Changing as AI-generated Code Floods the SDLC
AI coding tools have moved from experiment to daily development aid, helping software teams to draft functions, explain unfamiliar code, generate tests, and move through repetitive changes faster. For security teams, the harder question is how much AI-shaped code reaches a pull request before anyone validates its safety.
A recent Stack Overflow survey found that 46% of developers distrust the accuracy of AI tool output, while 33% trust it. That concern becomes visible during a routine security review. For instance, a generated API handler may compile and pass a unit test while missing object-level authorization. Meanwhile, a suggested dependency may look legitimate while being abandoned, vulnerable, or suspiciously named.
The OWASP Top 10 for Large Language Model Applications treats supply chain exposure as one of the major risksaround LLM-enabled systems. The list covers prompt injection, insecure output handling, sensitive information disclosure, excessive agency, and supply chain vulnerabilities. Today,...
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