A digital twin is a virtual representation that accurately replicates a physical object and allows real-time interaction between them. Digital twins must analyze a vast quantity of data throughout the operation to perform specified jobs. Since digital twin rely heavily on the data, security and privacy considerations seem to be the most challenging in their implementation. The aim of this article is to provide an industry perspective on major digital twin challenges, with a focus on privacy and security issues. Using a systematic literature review strategy, 150 articles were identified and reviewed. As a result, 26 articles fulfill the eligibility criteria. Thus, the originality of this work lies in the fact that a literature analysis was undertaken focusing on articles employing qualitative research approaches that assess the perspectives of experts, including interviews, focus groups, and Delphi investigations. The study reveals privacy challenges that include: (1) data sensitivity, (2) data ownership, and (3) ethics and trust. Also, it identifies security challenges that hinder the implementation of digital twin: (1) cybersecurity, (2) authentication and access, and (3) integration.