Internal AI Governance Quickstart

This quickstart is for risk, compliance, security, platform, audit, and business owners who need responsible internal AI use without turning Orlo into a generic GRC system.

Orlo does not replace policy registers, legal approvals, formal risk registers, or audit sign-off. It gives those systems operational evidence from the AI runtime.

Scenario

An enterprise wants employees to use AI for policy Q&A. The answers must use approved internal documents, cite sources, avoid unsupported claims, and route uncertain answers to review.

What You Will Build

  • a governed policy Q&A task
  • a source-grounding expectation
  • validation and fallback behavior
  • a feedback review path
  • an evidence pack risk and compliance teams can inspect

Step 1: Define The Policy

Start with a plain-language policy:

Policy Requirement Orlo Control
Use approved policy documents only Task-scoped document ingestion and retrieval attribution
Cite source material Retrieval-backed inference with debug/audit attribution
Do not answer when confidence is weak Validation, abstention, fallback, or review routing
Escalate sensitive answers Approval or review workflow
Preserve evidence Task versions, deployment snapshots, logs, traces, feedback

Step 2: Create The Task

Create a task for the internal policy workflow. The task should define the input, output, owner, and expected answer shape.

bash
curl https://api.useorlo.com/v1/tasks \
  -H 'Content-Type: application/json' \
  -H 'X-Orlo-Org-Id: YOUR_ORG_ID' \
  -d '{
    "name": "Internal Policy Q&A",
    "description": "Answer employee policy questions using approved internal policy documents.",
    "input_schema": {
      "type": "object",
      "properties": {
        "question": { "type": "string" },
        "employee_region": { "type": "string" }
      },
      "required": ["question"]
    },
    "output_schema": {
      "type": "object",
      "properties": {
        "answer": { "type": "string" },
        "citations": { "type": "array" },
        "needs_review": { "type": "boolean" }
      },
      "required": ["answer", "citations", "needs_review"]
    },
    "prompt_template": "Answer from approved policy sources. Cite sources. If the answer is uncertain or unsupported, set needs_review=true."
  }'

Save task_id and task_version_id.

Step 3: Attach Approved Documents

Ingest or attach approved policy documents to the task. For implementation details, see Retrieval and Documents and Documents and RAG.

The governance point is simple: approved sources become part of the task context, and retrieval attribution lets reviewers see what material supported an answer.

Step 4: Evaluate Before Deployment

Upload representative policy questions and expected answers as a dataset, then run an evaluation across candidate models.

The evaluation should include:

  • questions with clear answers
  • questions that require regional policy distinctions
  • questions where the model should abstain or request review
  • questions with tempting but unsupported answers

Step 5: Deploy With Controls

Deploy the acceptable model and strategy, then require runtime behavior that matches the policy:

  • structured output
  • citations
  • validation
  • fallback or review for unsupported answers
  • debug/audit metadata for reviewers

Step 6: Review Evidence

Ask risk, compliance, security, platform, audit, and the business owner to review the evidence pack:

Evidence Reviewer Question
Task definition Is this a permitted internal AI use case?
Dataset and evaluation Was the model tested on realistic policy questions?
Deployment snapshot Which version and model were approved for use?
Retrieval attribution Did the answer come from approved sources?
Validation result Did the output match required structure and constraints?
Feedback items Are employee corrections reviewed and reused?
Monitoring signals Are failures, review pressure, and drift visible?

What Proof You Created

By the end of this flow, the governance team can see:

  • the AI task and owner
  • the policy mapped to runtime controls
  • the model evaluation evidence
  • the approved deployment snapshot
  • source attribution for grounded answers
  • validation and review behavior
  • feedback that can improve future evaluations

That is the practical value of Orlo for internal AI governance: policy becomes controls, controls create evidence, and evidence improves the next version.