Insight
Build an AI governance framework around an inspectable record of each use case, owner, data boundary, evaluation, approval, incident route, and review date.
An AI governance framework is the set of policies, practices, roles, and controls an organization uses to direct AI decisions across the system lifecycle (Snowflake). For a boutique consulting firm, the useful output is an inspectable operating record, not a responsible-AI statement. Give every recorded use case an owner, permitted inputs, prohibited data, an evaluation, an approval decision, an incident route, and a review date. IBM describes a central AI system and data register, ownership, policies, evaluations, approvals, incident reporting, and review as parts of implementation (IBM). This Jungle Roots template organizes those parts for a small firm. It does not certify the firm, establish legal compliance, or replace legal, security, privacy, or domain review.
The operating record should let a reviewer trace a use case from its purpose and inputs to its decision, incident route, and next review. The fields below are Jungle Roots' editorial template. They are not a standard or certification.
| Record field | What to write | Source connection |
|---|---|---|
| Use case | The intended work, output, and context | IBM starts implementation with purpose and scope, then registers AI use cases and models (IBM). |
| Owner | The person accountable for the use case and its decisions | Databricks calls for clearly defined roles and responsibility for outcomes and escalation (Databricks). |
| Inputs | The data and source material the use case may receive | Databricks includes data sources, lineage, access policies, and controlled access in its governance guidance (Databricks). |
| Prohibited data | The data the use case must not receive | IBM says policies should state what is permitted and prohibited in AI development and use (IBM). |
| Evaluation | The test material, rule, reviewer, and saved result | Databricks includes model evaluation, documentation, monitoring, and performance thresholds in its framework (Databricks). |
| Approval | The named decision-maker, decision, and evidence considered | IBM describes risk profiling, required reviews, approvals, reassessment, and reapproval (IBM). |
| Incident route | The person or team that receives an issue and the record they receive | IBM describes incident reporting, escalation, correction, and incident logs (IBM). |
| Review date | The date on which the owner will inspect the record again | IBM says governance processes and policies should be reviewed and refreshed (IBM). |
The record is useful only when its fields refer to the same use case. An evaluation for one output cannot support approval of a different output. An incident route that names no recipient leaves escalation unresolved. A review date without an owner leaves the review unassigned.
Start with the named use case, then record only the decisions and evidence that govern that use case. IBM presents implementation as a progression from purpose and scope through roles, policies, a central register, risk assessment, development controls, monitoring, incidents, and review (IBM). For this template:
Use the AI readiness assessment to expose missing evidence before a tool purchase or build. Use the business process automation brief when the use case needs a bounded process, acceptance evidence, exception owner, and stop condition.
The NIST AI Risk Management Framework is a voluntary framework for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems (NIST AI RMF overview). NIST released AI RMF 1.0 on January 26, 2023 (NIST AI RMF overview).
Its Core has four functions (NIST AI RMF 1.0 PDF):
MAP, MEASURE, and MANAGE can be applied in context-specific orders rather than as a fixed sequence (NIST AI RMF 1.0 PDF). This operating-record template maps evidence to those functions. That mapping helps a team organize its records. It does not certify conformity, safety, or effectiveness.
NIST's official page says AI RMF 1.0 is being revised as part of the White House AI Action Plan (NIST AI RMF overview). This article does not predict the revision or treat draft material as final.
The captured sources do not use one shared pillar count or one shared list. Databricks groups its framework into five pillars: AI organization; legal and regulatory compliance; ethics, transparency, and interpretability; data, AI operations, and infrastructure; and AI security (Databricks). Snowflake presents four commonly recognized principles: transparency, accountability, fairness, and human oversight (Snowflake). Snowflake also describes NIST through four functions, which are not the same list as its four principles (Snowflake).
Keep each count attached to its source:
The operating-record fields in this article are Jungle Roots' editorial template. They are not another pillar taxonomy.
Keep NIST AI RMF and the EU AI Act distinct. NIST describes a voluntary risk-management framework (NIST AI RMF overview). The EU AI Act sets risk-based rules for providers and deployers for specific uses of AI (European Commission AI Act overview). Neither determines what an operating record proves.
| Reference | Description in the captured source | What this article does not infer |
|---|---|---|
| NIST AI RMF | AI RMF is intended for voluntary use and to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems (NIST AI RMF overview). Its Core has the GOVERN, MAP, MEASURE, and MANAGE functions (NIST AI RMF 1.0 PDF). GOVERN is cross-cutting, while MAP, MEASURE, and MANAGE can be applied in context-specific orders (NIST AI RMF 1.0 PDF). | NIST says Core actions do not constitute a checklist and are not necessarily an ordered set of steps, and that functions may be performed in context-specific orders (NIST AI RMF 1.0 PDF). This article treats its operating-record mapping as an editorial aid only; the mapping does not certify conformity, safety, effectiveness, or compliance. |
| EU AI Act | The AI Act is Regulation (EU) 2024/1689 and sets risk-based rules for providers and deployers for specific uses of AI (European Commission AI Act overview). The Commission defines four risk levels: unacceptable, high, transparency, and minimal or no risk (European Commission AI Act overview). | The article does not infer a reader's legal category, duty, compliance, or deadline from the general rules or dates (European Commission AI Act overview). It does not claim that an operating record establishes legal compliance (European Commission AI Act overview). |
The AI Act entered into force on August 1, 2024, and became applicable on August 2, 2026, with exceptions and staged dates (European Commission AI Act overview). Prohibited-practice and AI-literacy obligations applied from February 2, 2025 (European Commission AI Act overview). GPAI rules became effective in August 2025 (European Commission AI Act overview). Transparency rules came into effect in August 2026 (European Commission AI Act overview). The Commission page states that certain high-risk obligations start on December 2, 2027 (European Commission AI Act overview).
These general dates do not decide a use case's legal status (European Commission AI Act overview). Check the official Commission guidance and seek qualified legal, privacy, and security review before assigning a category, duty, or deadline.
A governance policy becomes hard to inspect when it states principles but does not connect them to a use case, owner, evidence, and decision. Databricks describes transparency through accessible documentation of data sources, assumptions, evaluation processes, lineage, versioning, and model behavior (Databricks). IBM describes a central register, named roles, approval workflows, monitoring, incident logs, and review (IBM).
Return the framework for completion when:
This check does not prove that a completed record is safe, compliant, or effective. It shows whether the operating facts requested by this template are present and reviewable.
Choose a named use case and complete its operating record before making a broad governance claim. Ask the owner to inspect the inputs, prohibited data, evaluation, approval evidence, incident route, and review date. Preserve unresolved fields as unresolved. Do not fill them with a framework name.
The record can also support a precise public explanation of how the firm governs a specific use of AI. Keep that explanation within the evidence the owner can inspect. A published policy is not proof that every use case follows it, and the presence of a record does not guarantee an outcome.
For adjacent operating work, read the AI readiness assessment and the business process automation brief. Get the operations audit to turn a proposed workflow into an owned brief and evidence record.
The NIST AI Risk Management Framework is voluntary and intended to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems (https://www.nist.gov/itl/ai-risk-management-framework).
The NIST AI Risk Management Framework is voluntary and intended to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems (https://www.nist.gov/itl/ai-risk-management-framework). Its Core has four functions: GOVERN, MAP, MEASURE, and MANAGE (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf).
The source vocabulary differs. Snowflake lists four commonly recognized principles, transparency, accountability, fairness, and human oversight, and separately describes the four NIST functions (https://www.snowflake.com/en/artificial-intelligence/ai-governance/framework/).
No. This article provides a Jungle Roots editorial operating-record template. It does not certify an organization, establish legal compliance, or replace legal, security, privacy, or domain review.
