Hire an AI data use review preparer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A triage call: no assessment, assessment needed or escalate, with the reasons from your facts
• A draft privacy impact assessment when triage calls for one
• A table of conditions, each with an owner
• Sign-off questions for the decision owner
• A note on any new or serious legal risk, or why none is raised
• A list of missing facts and checks that did not run

How it does the job well

• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.
• Averages that hide important segments or outliers.

What a good result looks like

An example, not your task: A product team wants to add a feature that collects or reuses customer data, such as location or purchase history.

• Triage route. The call (no assessment, assessment or escalate) with reasons, sources and dates.
• Draft assessment. Risks, conditions and policy checks in your house format, only when triage calls for it.
• Conditions table. Each condition with its owner and due date.
• Sign-off questions. What the decision owner must answer before launch.
• Escalation note. New or serious legal risks, or why none is raised.
• Missing and checks. Gaps, and each check marked PASS, FAIL or NOT RUN.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Route the activity. Loads your practice setup first, then reads the data flows, places and policies to pick a route. Hands step 2 the route, the evidence and the open questions. Check before moving on: setup is complete, and the route says it does not approve launch.
• Draft only the required assessment. When triage calls for it, drafts the assessment in your house format with conditions, owners and policy checks. Checks current rules from primary sources when it can browse, and says so when it cannot. Hands step 3 the draft. Check before moving on: each condition has an owner.
• Escalate novel material risks. Looks across the triage and the assessment for new or serious risks. Writes a separate assessment only when the threshold is met, and otherwise says why it is not proposed. Hands back the sign-off pack. Check before moving on: the call matches the route.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id prepare-new-data-use-review-for-signoff only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/prepare-new-data-use-review-for-signoff/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• FEATURE: who the data is about, which fields, why you collect them, where they go, how long you keep them, who can see them and which vendors get them
• PRACTICE_SETUP: your assessment template, house rules and who signs off
• FOOTPRINT: where you operate, who decides, and who receives the result
• SOURCE_PACK: the product spec, your current privacy policy, an earlier assessment and any dated legal guidance you rely on (attach what you have)

Helpful context (optional)

• Your current privacy policy and assessment template.
• A named decision owner and reviewer.

Tools and access

• A chat AI with file support and web access: It can read your spec and policy files, and check live legal rules from primary sources. Without web access it marks that check NOT RUN.

Functions and deliverables

• Prepare a new data use for review: Check a new use of customer data before launch: get a triage call, a privacy assessment if needed and sign-off questions with owners.
  • A triage call: no assessment, assessment needed or escalate, with the reasons from your facts
  • A draft privacy impact assessment when triage calls for one
  • A table of conditions, each with an owner
  • Sign-off questions for the decision owner
  • A note on any new or serious legal risk, or why none is raised
  • A list of missing facts and checks that did not run
• New data use privacy screener: Describe the data, people and purpose. Check company rules and legal triggers, then route the use for the right privacy review.
  • A proceed, assessment or stop call
  • The reasoning behind it
  • Conditions to meet
  • A handoff into a full assessment
  Source: Anthropic.
• Privacy impact assessment writer: Describe a feature, its personal data and the regions it serves. Get an assessment-need check, source-based risks and launch conditions with owners.
  • Need-for-assessment check
  • Completed assessment
  • Policy consistency check
  • Conditions with owners
  Source: Anthropic.
• Product feature legal risk reviewer: Name a risky product feature. Get likely harms, severity, mitigations and a first-pass risk review.
  • A 2 to 4 page assessment you can hand on
  • A list of specific risk scenarios
  • Likelihood and severity for each, with reasons
  • Suggested fixes
  • Open questions for a lawyer
  Source: Anthropic.
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• A product team wants to add a feature that collects or reuses customer data, such as location or purchase history.
• Someone asks "does this need a privacy assessment?" and you have no standard answer.
• A vendor or tool will receive personal data for the first time.
• Your reviewer wants one tidy pack to sign off.

Limits to check before starting

• You have no privacy rules, assessment template or named reviewer yet. Set those up first. The Privacy management audit planner shows what a privacy setup needs.
• The data belongs to staff, such as an HR tool. Use Employee data privacy reviewer or the Employee data privacy checker.
• You want your code scanned for GDPR risks. Use the GDPR privacy checker.
• You want legal advice or a launch approval. Only a qualified reviewer can give those.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack, unzip it and give the whole folder to Claude, ChatGPT or Gemini. Start with START_HERE.txt, then answer what the coordinator skill asks.
• Fill the four inputs: FEATURE, PRACTICE_SETUP, FOOTPRINT and SOURCE_PACK. With no PRACTICE_SETUP you get a setup checklist, not a call.
• Read the triage route and its reasons. Check that the data flows it lists match your product.
• If an assessment was drafted, give each condition to its owner to confirm. Fix anything wrong before it goes further.
• Send the pack to your privacy reviewer. Nothing here approves a launch.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/prepare-new-data-use-review-for-signoff for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.