AI policy · updated 23.08.2026
Read →How to build an internal AI policy people can actually use
A useful internal AI policy turns real tool usage into clear rules for data, human verification, approval and incident handling.
BlackArk resources
Tools and guidance for clearer company AI decisions.
Complete public resources on internal AI policy, inventory, tool approval, workforce literacy and Company Memory.
01 · Tools
Start with the company’s real situation.
Use the complete tools directly; they are not previews or gated downloads.
AI Policy Readiness Checklist
Company AI policy readiness checklist
A practical pre-drafting check covering visibility, data, decisions, ownership, training and incidents.
Open resource →AI Usage Inventory TemplateAI usage inventory template
The minimum fields that turn an application list into a management decision register.
Open resource →AI Tool Approval / Risk MatrixAI tool approval and risk matrix
A short triage framework for purpose, data, affected people, provider controls, verification and impact.
Open resource →Management AI Governance ChecklistManagement AI governance checklist
Recurring questions that preserve visibility, decision ownership and accountability after the policy launches.
Open resource →Decision frameworkAI tool approval and risk matrix
A structured model for purpose, data, impact, verification and approval.
Open resource →Management checklistManagement AI governance checklist
A recurring review for visibility, ownership, incidents and decisions.
Open resource →02 · Insights
An editorial cluster built around real operational work.
Every guide has an author, editor, editorial dates, a RO/EN pair and links to relevant operational resources.
AI inventory · updated 23.08.2026
Read →AI usage inventory: gain visibility without freezing the team
A useful AI inventory captures tools, purposes, data, verification and real owners, then turns observations into management decisions.
AI approval · updated 23.08.2026
Read →AI tool approval workflow: a clear decision instead of a bottleneck
A short approval workflow lets a company assess purpose, data, provider, verification and ownership before an AI tool enters routine work.
AI literacy · updated 23.08.2026
Read →AI literacy for employees: what should remain after training
Useful AI training leaves employees with clear data boundaries, a verification routine and known routes for approval and incident reporting.
AI data · updated 23.08.2026
Read →Company ChatGPT policy: practical rules for data and outputs
A ChatGPT and generative-AI policy should connect account type, entered data, intended use and output verification.
AI risk · updated 23.08.2026
Read →AI risk matrix for HR: where stronger controls are needed
AI use in HR should be triaged by data, affected people, the output’s role in a decision and the ability to challenge it.
AI incident · updated 23.08.2026
Read →Company AI incident handling: the first decisions that matter
A useful AI incident flow limits impact, preserves facts and assigns ownership without treating every error as a crisis.
Have you found important gaps?
The AI Policy diagnostic provides an immediate interpretation and three priorities before any optional commercial qualification.