# Make governance actionable across AI systems > Turn your AI governance frameworks into action with built-in tools that automate evidence collection and assessment. **Source:** https://deeploy.ai/product/document-assess/ **About this file:** Machine-readable markdown version of the page above, for LLMs and AI assistants. Regenerated automatically whenever the page is updated. Don’t get stuck defining controls. Turn your AI governance framework into action with built-in tools that help every onboarded system meet the right standards from day one. ## Hard to operationalise AI policies across AI systems? Turning AI governance into action can be slow and complex. Teams struggle to complete controls, generate documentation, and maintain explainability, creating gaps between policy and practice. - ✗ No touchpoints between governance & AI teams - ✗ Scattered, incomplete, and inconsistent documentation - ✗ Duplicate evidence across the organisation ## Bridge the gap between governance and implementation Deeploy simplifies the process of demonstrating your AI systems’ compliance with a control framework, helping your teams stay compliant without slowing innovation. Prove control implementation with multiple evidence types: automated checks, organisational documentation, links, images, and text, giving auditors instant proof that your controls are implemented. Automated checks work in the background, instantly confirming when critical actions like adding model cards or evaluating predictions are complete. Standardise model and use case documentation across your organisation with custom templates, or choose from a library of ready-made templates for common assessments. Skip the repetitive work; AI-powered assessments extract information from your documentation and models to auto-complete documentation templates. Maintain complete transparency and compliance with automatic logging that records every action, ensuring full traceability across your system. ## Real-time monitoring in action Discover how Deeploy can be applied in various industries ## Frequently asked questions ### How do automated evidence checks work? Automated checks are built-in validations that verify whether an AI control requirement has been met. For example, a check can confirm whether required documentation exists, whether a risk assessment was completed, or whether monitoring rules are configured. When an automated check passes, the control status is updated automatically, helping teams track AI compliance and risk controls without manual oversight. ### What are documentation templates in Deeploy and how are they managed? Documentation templates are reusable questionnaire that Workspace owners and reviewers create to standardise how use cases are documented across a team. You can create custom templates or choose from a library of existing ones. To create custom templates, download a copy of the .csv template file available under "Workspace > Documentation Templates". This template has been pre-filled with example input. Replace the example input with the questions that you want to include in your documentation template. Optionally, assign a question to a category or add a subtitle. You can then upload the templates to a Workspace where they become available for all use cases within it. Deeploy frequently uploads documentation templates to help you stay aligned with current regulations and frameworks. Available templates include: - AI vendor assessment - ISO/IEC 42001 AI system technical documentation - GPAI model documentation - EU AI Act | Risk management system (Article 9) - EU AI Act | Data and data governance (Article 10) - EU AI Act | Technical documentation (Article 11) - EU AI Act | Record-keeping (Article 12) - EU AI Act | Transparency and provision of information to deployers (Article 13) - EU AI Act | Human oversight (Article 14) - EU AI Act | Accuracy, robustness and cybersecurity (Article 15) - Information to be submitted by deployers upon the registration of high-risk AI systems - Instructions for use - Data protection impact assessment (DPIA) - Bias profile - Fundamental rights and algorithms impact assessment (FRAIA) ### How does Deeploy's audit log support regulatory traceability requirements? Deeploy automatically logs every successful change to resources across your organisation, covering actions like control updates, evidence submissions, deployment changes, and approval decisions. Each log entry is available in three views: a plain summary, a formatted breakdown, and raw data. This creates a complete, tamper-evident record of who did what and when, which is exactly what regulators like the EU AI Act require organisations to maintain for high-risk AI systems. Deployment-specific events are tracked separately through Deployment events logs for even more granular traceability. ### What is the relationship between controls, evidence, and framework completion in Deeploy? Controls represent the governance requirements your use case needs to meet. Providing evidence against a control, whether through an automated check, a link, an image, or text, moves its status to "In progress." A control only counts as complete once you explicitly mark it as completed, giving teams deliberate control over what they're signing off on. Overall framework completion for a use case is calculated based on the number of completed controls relative to the total applicable controls for that use case's risk classification, role, and lifecycle stage. Controls from future stages can be addressed early, but they won't count toward completion until the use case reaches that stage. --- **Get in touch:** You can book a demo at https://deeploy.ai/book-demo/ or reach the team via https://deeploy.ai/contact/.