Compliance with regulatory frameworks is essential when developing AI, ML, or CV models, especially in sensitive sectors like healthcare, to avoid the risk of rendering models unusable due to non-compliance. Data compliance ensures ethical and responsible data handling, but navigating these regulations can be challenging, particularly when building models that rely on diverse and vast datasets to achieve high performance. Different jurisdictions have varying data protection laws, requiring models to be trained and deployed in compliance with the originating data's legal framework, such as HIPAA regulations in the US or GDPR in the EU. The complexities of partitioning data, maintaining auditability for annotations, and managing compliance throughout the model's lifecycle demand careful planning and documentation to prevent costly rework and ensure models can be legally and ethically deployed. Encord's platform aids in alleviating these challenges by providing tools for data annotation, active learning, and compliance management, helping organizations streamline their development process while adhering to regulatory requirements.