Designing Secure AI-based Systems: a Multi-Vocal Literature Review

Simon Schneider, Ananya Saha, Emanuele Mezzi, Katja Tuma, Riccardo Scandariato

Abstract: AI-based systems leverage recent advances in the field of AI/ML by combining traditional software systems with AI components. Applications are increasingly being developed in this way. Software engineers can usually rely on a plethora of supporting information on how to use and implement any given technology. For AI-based systems, however, such information is scarce. Specifically, guidance on how to securely design the architecture is not available to the extent as for other systems.We present 16 architectural security guidelines for the design of AI-based systems that were curated via a multi-vocal literature review. The guidelines could support practitioners with actionable advice on the secure development of AI-based systems. Further, we mapped the guidelines to typical components of AI-based systems and observed a high coverage where 6 out of 8 generic components have at least one guideline associated to them.

External IDs:dblp:conf/secdev/SchneiderSMTS24