Connect regulatory obligations to AI services, accountable owners, controls and evidence. Give operations, risk and technology teams a shared view of how AI is governed.
Explore AI training rollout and tracking
Build your service record from the requirements you need to address or the AI activity already in operation. Both approaches connect accountable owners, controls, evidence and review.
Bring configured regulatory sources and organizational policies into the review. Your team confirms which requirements apply, identifies the AI services in scope and connects each service to owners, controls and supporting evidence.
Map how a service uses AI: the people, systems, data movements and model providers involved. Identify human oversight and use available metering to understand activity, then review that service against the requirements that matter.
An illustrative workflow for a customer support assistant. Your team assesses which obligations apply, chooses the controls and validates the evidence.
Identify a requirement for your team to assess.
Map the service, its intended use and data boundaries.
Name the person responsible for the decision.
Document a safeguard or human review step.
Reference the records that support your review.
Keep the service version, reviewer and decision together.
Explore the EU AI Act guide Understand roles and obligations, then document oversight and supporting evidence in the service record.
Explore the US governance guide Scope the jurisdiction and use case, then connect the resulting review to owners, controls and evidence.
Liquid Learn’s AI governance software connects the service map to review work. See where AI is used, who is responsible and what the record supports.
Bring the review owner, scope, linked service flows and approval cycle into one packet. Keep evidence references with the work being reviewed.
Organize automatically identified and manually maintained requirements alongside source context and review status. Your team confirms applicability and the action needed.
See regulatory tracking, metering, training and certification tracking, and the full review workflow in a product demo.
Keep team training progress, completion records and certification status connected to the roles and responsibilities they support.
For learning and development, HR, AI program, governance and risk owners coordinating learning across an organization.
Review linked requirements and course or assessment items. Use completion records and overdue assignments to focus your team’s follow-up.
Connect the rollout to your policies, approved AI uses and human-oversight responsibilities. Your team decides what learning is appropriate and what follow-up is needed.
For courses with certificates enabled, learners can view completion certificates and verification links. Review these records alongside outstanding assignments and governance evidence.
A completion certificate is a learning record. It does not establish professional accreditation, authorize AI access or certify legal compliance.
Set the requirement’s owner role and target relevant job roles or organizational units.
Choose course or assessment items available in your organization’s workspace. Your team validates suitability and external course recognition.
Publish requirements with due dates and target teams or roles. Review the resulting assignments while owners coordinate communications and follow-up.
See learner-level items, completed work, in-progress learning and overdue assignments.
Use completion records and requirement evidence references in your governance review. Revisit the requirement when duties or approved AI uses change.
Liquid Learn connects the service map to review work. Teams maintain the record as the service and its operating decisions change.
Ingest configured regulatory sources, retain document and version context, and use automatic obligation extraction and profile-based requirement matching. Review applicability and maintain requirements with their source context.
Put people, applications, AI steps, data stores and vendor boundaries in one service flow. Make the intended use and the information crossing each boundary clear.
Identify owners at the steps they are responsible for. Show human review, approval and escalation alongside AI activity so teams can agree who intervenes and when.
Record controls and data classifications on service steps and connections. Use that context to discuss safeguards, review requirements and unresolved gaps.
Bring evidence references into review packets and retain reviewed service snapshots with reviewer details and comments. Give the next review a clear starting point.
Relate metered usage and cost allocation to the service, team or business unit responsible. Use recorded activity alongside the service model and evidence to inform risk reviews.
An assistant drafts a response using approved support material. A person reviews exceptions before a response is sent. This example shows how to document the service; it is not an automated workflow integration.
Record the information entering the service and the support team responsible for it.
Control to consider: limit sensitive data.Show the knowledge source, model provider and information crossing the vendor boundary.
Evidence to consider: approved source and vendor instructions.Name the reviewer and document when a request needs escalation to the service owner.
Evidence to consider: an exercised exception procedure.Connect the reviewed flow, controls and evidence references to the next operating review.
Decision to consider: what must change before wider use?Review obligations, connect controls and evidence, and examine gaps in the service you are assessing.
Clarify handoffs, exceptions and who owns the outcome when AI supports service delivery.
Make systems, data movements, metering and external dependencies visible to reviewers.
Regulatory tracking depends on configured sources and your organizational profile; metering depends on instrumented activity. Your team confirms legal applicability, implements controls and validates evidence. Liquid Learn supports that governance process; it does not certify compliance or automatically enforce controls in third-party systems.
Practical guides with dated official sources, current legal status and clear product limitations.
Provider and deployer roles, the amended timetable, transparency and human oversight.
Read the EU guide United States · Law and guidanceCalifornia, Colorado, Texas, FTC enforcement and the voluntary NIST AI RMF.
Read the US guideSee how to connect an obligation to an AI service, accountable owner, control and evidence record. Explore how training requirements, completion records and certificates support your team’s governance review.
Enquiries are reviewed by Fluid Business in Seattle, Washington.
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