The most common AI policy failures in organizations

AI adoption in mid-market organizations is progressing rapidly, outpacing the development of policies, controls, and oversight necessary to manage associated risks effectively. This lack of formal AI governance often leads to policy failures, including unclear data-sharing rules, assumptions about tool safety based on popularity, the risk of AI-generated inaccuracies, and the exclusion of AI from existing compliance frameworks, creating significant gaps in security and accountability. Many organizations fail to maintain a validated list of approved AI tools, which hampers consistent security controls and compliance. There is also a tendency to view AI solely as a productivity tool, overlooking its potential risks such as data exposure and intellectual property loss. The absence of clear accountability for AI usage exacerbates these issues, as no one is responsible for approving, updating, or auditing AI policies. To address these challenges, visibility and control are crucial starting points, requiring updated compliance programs, governance around data localization, and clear guidelines for AI tool usage. Upsun offers solutions to bridge these gaps by integrating AI policy with operational controls through features like Git-driven YAML configurations, automatic previews, multi-service orchestration, and platform-level compliance and security controls, ensuring organizations can balance developer speed with operational oversight.