The emergence of artificial intelligence assistants in software development has marked a before and after in the productivity of technical teams. Tools like GitHub Copilot promise to accelerate code writing, reduce syntactic errors, and allow developers to focus on business logic. However, when it comes to security APIs—those critical interfaces that protect systems, data, and communications—the promise of AI encounters a more complex reality. A recent empirical study with 44 professional developers reveals that, although Copilot improves functional correctness of tasks, it does not achieve a significant advance in the secure use of these APIs. Participants rarely raised security concerns during interaction with the assistant, and many did not even realize that their final implementations remained insecure. This finding brings to the table a crucial issue for companies developing custom applications: speed should not sacrifice security.
In the current context, where digital transformation demands agile and frequent releases, AI assistants have become almost indispensable allies. However, their impact on cybersecurity—especially in handling authentication, encryption, and access control APIs—requires careful attention. The study, from which we take only general concepts, highlights that developers tend to trust the assistant's suggestions without questioning their validity from a security standpoint. This is particularly dangerous in environments where cloud services like cloud AWS/Azure are integrated, as a misconfigured security API can expose sensitive data to millions of users. The research also points out that assistants can reduce certain unsafe patterns, but the effect is marginal and not systematic. That is, AI helps write code that works, but not necessarily secure code.
For software development and technology companies like Q2BSTUDIO, this finding reinforces the need to combine automation with expert human oversight. It is not about rejecting AI, but integrating it within a quality assurance process that includes code reviews, penetration testing, and vulnerability analysis. In fact, the study suggests that developers rarely activate a 'security mode' when interacting with the assistant; most assume the tool generates correct code by default. This is a conceptual error that organizations must correct by training their teams in cybersecurity and the critical use of generative tools.
From a technical perspective, security in APIs is not an optional addition: it is an architectural requirement. When a developer uses an AI assistant to generate code that manages OAuth tokens, TLS certificates, or authorization policies, each line must be verified against best practices and industry standards. The empirical study shows that AI can improve productivity but does not replace human judgment. Therefore, at Q2BSTUDIO we recommend a hybrid approach: use AI to accelerate repetitive tasks, but always accompanied by peer reviews and static security analysis tools. Additionally, integrating BI/Power BI into development pipelines can help monitor security metrics and detect anomalies in real time, closing the loop between generated code and its behavior in production.
Another relevant aspect is the lack of awareness about risks. The study shows that many developers do not identify their own implementations as insecure, indicating a deficit in security training. Companies investing in AI and automation must balance that investment with continuous training programs. At Q2BSTUDIO, we offer consulting services that help organizations design workflows where AI assistants act as support tools, not substitutes for technical judgment. This is especially critical when developing AI agents that interact with security APIs, as a single error in authorization logic can have catastrophic consequences.
In conclusion, the empirical study on the impact of AI assistants on security APIs leaves us with a clear lesson: technology advances, but security remains a human responsibility. Companies adopting AI assistants must do so with open eyes, implementing review processes, training their developers, and using complementary analysis tools. At Q2BSTUDIO, we understand that the true value of custom software lies not only in its functionality but in its ability to resist attacks. That is why we integrate security from the design phase, combining the power of AI with the rigor of traditional software engineering. The future of development is collaborative between humans and machines, but always with security as a fundamental pillar.
