The Canvas Learning Management System (LMS) is used in thousands of universities across the United States and internationally, with a strong and growing presence in K-12 and higher education markets. Analyzing the development of the Canvas LMS, we examine 1) 'frictionless' data transitions that bridge K12, higher education, and workforce data 2) integration of third party applications and interoperability or data-sharing across platforms 3) privacy and security vulnerabilities, and 4) predictive analytics and dataveillance. We conclude that institutions of higher education are currently ill-equipped to protect students and faculty required to use the Canvas Instructure LMS from data harvesting or exploitation. We challenge inevitability narratives and call for greater public awareness concerning the use of predictive analytics, impacts of algorithmic bias, need for algorithmic transparency, and enactment of ethical and legal protections for users who are required to use such software platforms.