Source-code plagiarism has been a persistent problem in the academic computing environment, but its prevalence intensified due to the disruption caused by the emergency remote teaching and learning that the COVID-19 pandemic enforced. This is especially a concern in the context of teaching novice programmers to code. The pandemic compelled residential universities to accommodate students in ways previously unheard of, even in the context of non-residential establishments. Students found themselves in an environment where preparation for assessments was not necessary since they had access to material, peers and whatever help they could line up to pass. The pandemic is a distant memory, but academia will not return to its pre-Covid status, and to add to this situation, generative artificial intelligence emerged in 2023 as a playmate on this very playground. In this context, tutoring is of utmost importance when teaching novice programmers to code, and therefore it makes sense to learn from past approaches to guide this support function in future. With this premise in mind, an interpretivist perspective was adopted, and qualitative data were gathered, centering on focus group interviews, as well as interviews with tutors unavailable for focus group sessions. These tutors supported classes of introductory programming students over a period of three years. Within this setting, the lecturer-as-researcher reflects on mitigating source code plagiarism holistically. This paper has as its goal proposing a model to enable and support tutors in their role to attenuate source code plagiarism.