Policy in. Work executed with guardrails. Evidence and audit out.
Regulated AI needs more than a responsible AI statement. Forlex explains the boundaries around data, models, human review, evidence, audit trails, policy controls and professional responsibility.
Reviewable AI boundaries
Reviewed trust statement
Data boundary
Model boundary
Human boundary
Evidence boundary
Audit boundary
Policy boundary
The preview shows where data, model access, evidence, people, audit and policy controls meet before rollout.
Governance boundaries
Evaluate how Forlex frames data, model access, human review, evidence, auditability and policy controls.
Data boundary
What data enters Forlex, where it is processed, how long it is retained and which controls apply.
Model boundary
Which AI paths are used, how provider access is controlled and how training-related statements are evidenced.
Human boundary
Where people review, approve, override, escalate or reject AI-assisted work.
Evidence boundary
When outputs are source-grounded, when they are not and how citations or uncertainty are displayed.
Audit boundary
What is logged for administrators, reviewers and later compliance inspection.
Policy boundary
How teams configure permitted agents, workflows, retention and permissions.
Does Forlex position AI as a replacement for professional judgment?
No. Forlex prepares work for accountable humans to review, approve, route or reject, especially in legal and regulated workflows.
What makes AI output trustworthy in Forlex?
Trust comes from visible sources, clear limitations, review responsibility, permission boundaries and auditability around each workflow.
How can organizations govern AI usage?
Forlex helps teams define permitted workflows, retention expectations, role access, human review points and escalation paths before expanding AI use.