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AI-first development introduces a lot of risks that traditional human-driven development automatically manages or even altogether eliminates. A common scenario is that the time gained thanks to AI tools is then just spent on managing the issues and risks caused by the overreliance on these tools.
The more complex the development is, the more likely it is that risks occur and compromise the success of development. Having humans in the loop is paramount to eliminating and managing common AI-associated risks when developing digital products optimized for efficiency and security.
In this article, we’ll first take a look at some of the most common risks introduced by irresponsible use of AI in software development. After that, we’ll explore how experienced developers are able to effectively manage these risks, preventing them from turning into major problems. Let’s get started.
Four main areas of AI-associated risks in software development
With more and more people adopting AI tools, several common risk areas are being uncovered. While content creators need to understand the risks of inaccurate or plagiarized content, there are four main areas of AI-associated risks that software developers should be particularly mindful of.
Security & privacy risks
A major AI-associated risk that was identified early on is the risk of working with confidential project/company information on a public LLM and accidentally exposing this sensitive information. This is particularly relevant for white-label development services and for highly competitive industries where compromised information might be especially devastating if somehow acquired by competitors.
Inadequate understanding of business context
AI tools are optimally leveraged by someone who has a keen understanding of the business context as well as any technical limitations for achieving key business goals. While it is of course possible to provide this information to an AI coding assistant, this is often difficult to do without the risk of exposing confidential information which we just mentioned. Without access to the full picture, AI may propose more general solutions that fail to consider things that a human would have top of mind.
Recommendation of insecure solutions
AI hallucinations are one of the best known shortcomings of AI, widely accepted even among the biggest AI proponents. In the context of software development, this often looks like AI models recommending packages and/or libraries which may be outdated, unofficial, unlicensed, or otherwise insecure. While AI may adequately implement certain security measures, it might completely ignore certain other vulnerabilities. Our development manager Janez gives a great example:
“I asked an AI model to provide an example for a PHP script of a HTML form. Then I asked it to provide a script for saving the form data into a MySQL database. While it did use proper input sanitization when saving to the database it never thought about providing a CSRF token in the form to protect it from a CSRF vulnerability or even mentioned it as a potential issue.”
Performance issues
Another consequence of irresponsible AI use in software development are performance issues, with the worst case scenario being potential downtime which logically has the biggest negative impact on revenue. This is the perfect example of a risk that cannot be effectively managed retroactively, especially for cases such as ecommerce websites, or internal admin applications, where any downtime can severely impact the brand perception.
Best practices & tips for managing risk in software development
Let’s now take a look at some essential best practices for effectively managing these risks with a human-first development approach.
Responsible use of AI
The most important set of practices for the most risk averse implementation of AI into the software development process revolves around responsible AI. This includes:
- Experienced humans in the loop to mitigate and minimize common AI shortcomings
- Proper guidelines to ensure optimal long-term implementation and use of AI
- Standardization on optimal AI models for the team, preferably more robust paid versions that come with fewer security risks
- Training/upskilling that includes both AI best practices as well as common pitfalls, with mentorship if needed
- Critical evaluation of solutions and technologies proposed by AI models
Culture of collaboration and knowledge sharing
The foundation for responsible AI use should ideally begin with the company culture itself. A company culture based on collaboration and knowledge sharing makes human solutioning more rewarding than overly relying on AI, both in terms of personal growth and development efficiency. Fostering a psychologically safe environment supported through a strong team spirit promotes human-driven development while minimizing AI-associated risks.
Including developers in strategy / R&D
One of the most effective ways to ensure optimal human-driven development is to include developers in the strategy / R&D phase of a project. As we’ve already highlighted, developers can make better use of AI when they have a deep understanding of the business context and main project objectives. Being familiar with key requirements and limitations upfront enables a proactive rather than reactive approach to managing risk, greatly minimizing the risks of issues related to security, privacy and performance.
Conclusion
Despite the huge promise of AI in software development, the best results come when development is human-first rather than AI-first. Human-driven development based on responsible AI use can effectively mitigate many of the risks associated with an overreliance on AI tools, resulting in more robust and secure websites and applications.
