Use of AI by software developers: for now, prudence should be the rule

A developer argues that software engineers should apply the Zero Trust principle to AI-generated code, conducting thorough manual reviews to prevent security vulnerabilities and technical debt. The article advises treating AI output as a suggestion rather than a final solution, especially in critical systems like financial and government platforms.

Starting in 2022, with the proliferation of publicly available LLMs such as ChatGPT, we began to witness the rapid rise of generative artificial intelligence tools. As expected, AI has also had a profound impact on the software development lifecycle. The unprecedented productivity gains are undeniably a major part of this appeal. However, speed cannot come at the expense of security. In information security, the Zero Trust model “Never trust, always verify” has become a fundamental guiding principle. This same principle should be extended to software engineering, becoming an indispensable practice, particularly when building critical systems. There is no doubt that AI drives efficiency by accelerating code generation . However, accepting its output without question poses a significant risk. A security flaw, for example, introduced into a system could lead to the leakage of sensitive information and, consequently, seriously damage the reputation of both the system and the company that provides it. From this perspective, developers need to adopt a vigilant mindset and apply Zero Trust to code generated by automated agents. This involves conducting thorough manual reviews, auditing the logic, checking for security vulnerabilities, and removing unnecessary redundancies or boilerplate that can diminish code quality. Blind acceptance and indiscriminate AI-generated code tend to accelerate software entropy. When large blocks of code are incorporated without proper scrutiny, we introduce maintenance problems that will surface sooner rather than later. The result is the emergence of “automated technical debt,” which degrades the architecture, makes ongoing maintenance significantly more difficult, and, ultimately, can contribute to shortening the software’s useful lifespan. Beyond maintainability, operational reliability is also put at risk. Language models can introduce subtle vulnerabilities, outdated dependencies, or unexpected behavior in edge cases. Treating AI-generated code as a suggestion — rather than a final solution — ensures that technical responsibility remains with the software engineer, preserving the robustness of the application. Today, the technology industry is clearly at an inflection point. It is still too early to accurately measure the full long-term impact of AI on systems development, whether positive or negative. In the face of this uncertainty, the safest path is not to reject the technology, but rather to adopt renewed technical rigor. For now, prudence should be the rule , especially in high-impact environments such as the financial sector, government platforms, and systems that manage sensitive citizen data. The key to sustainability in modern software development does not lie in producing more lines of code per minute, but in ensuring that every line integrated into a project is strictly necessary and secure.