Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.

Producing secure software is challenging. The poor usability of security Application Programming Interfaces (APIs) makes this even harder. Many recommendations have been proposed to support developers by improving the usability of cryptography libraries—rooted in wider best practice guidance in software engineering and API design. In this SLR, we systematize knowledge regarding these recommendations. We identify and analyze 65 papers, offering 883 recommendations. Through thematic analysis, we identify seven core ways to improve usability of APIs. Most of the recommendations focus on helping API developers to construct and structure their code and make it more usable and easier for programmers to understand . There is less focus, however, on documentation , writing requirements , code quality assessment , and the impact of organizational software development practices . By tracing and analyzing paper ancestry, we map how this knowledge becomes validated and translated over time. We find that very few API usability recommendations are empirically validated, and that recommendations specific to usable security APIs lag even further behind.

Does the act of writing a specification (how the code should behave) for a piece of security sensitive code lead to developers producing more secure code? We asked 138 developers to write a snippet of code to store a password: Half of them were asked to write down a specification of how the code should behave before writing the program, the other half were asked to write the code but without being prompted to write a specification first. We find that explicitly prompting developers to write a specification has a small positive effect on the security of password storage approaches implemented. However, developers often fail to store passwords securely, despite claiming to be confident and knowledgeable in their approaches, and despite considering an appropriate range of threats. We find a need for developer-centered usable mechanisms for telling developers how to store passwords: lists of what they must do are not working.