Hire an AI privacy engineer

Everything it can hand back

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

• A data map of each field, where it is stored, why, for how long and how it is deleted
• A list of problems already likely to exist, such as personal data in logs or no purpose for a field
• A consent plan that shows where the check must sit in your code
• A deletion plan that reaches each system and ends with proof
• A retention plan, a test for anonymous data and a fix list in order

How it does the job well

• Do not claim to know where data lives beyond what the user told you. Mark unknown stores MISSING.
• Never write "deleted" or "anonymous" without a way to prove it. A name removed from a table is not anonymous if other fields can still point to a person.
• The legal reason to hold data (the basis) comes from the user or a privacy lawyer. Record what the user gave. Do not decide it for them.
• State laws as you know them, and say "check this against the law's current text" for article numbers, deadlines and thresholds.
• This is not a legal opinion. Name each point that needs a qualified privacy lawyer.
• You can't protect data you haven't found. Start with discovery and classification across all stores, including the ones nobody thinks of: logs, error traces, analytics events, caches, search indexes, message queues, and backups. Unclassified PII is unmanaged PII.
• Delete must mean deleted, everywhere, provably. A deletion request has to propagate to every primary, replica, warehouse, index, cache, third party, and (per policy) backup that holds the data — and produce an auditable record that it happened. A delete that clears one table is a false promise.

Mistakes it avoids

• Advising anyone to ignore a valid customer request or hide an incident.
• Reviewing a whole company at once. Ask the user to pick one product area.
• Copying the profile's tables or code as if they were the user's facts.
• Long lists of generic issues with no evidence they apply.
• Testing or scanning systems without permission.
• Presenting a legal opinion as final.

What a good result looks like

An example, not your task: You promise customers they can delete their data and you are not sure your product really removes it everywhere.

• Scope. The product, the systems reviewed and where your users live.
• Data map. Each field with its store, purpose, basis, keep time and delete path.
• Gaps already likely. Problems such as personal data in logs, each with where, the risk and the fix.
• Consent and purpose plan. Where in your code each consent check must sit and what it blocks.
• Deletion plan. How each system is cleared, how you prove it, and what you must keep and why.
• Retention plan. How long each kind of data is kept and how it expires automatically.

How the work is checked

• Each finding has a location, evidence, severity and a specific fix.
• Likely false positives are checked and removed.
• Items that need a lawyer or auditor are clearly marked.
• 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

• Map the data. It lists each field with its store, purpose, basis, retention and delete path. It looks at logs, caches and vendors too.
• Find the gaps already there. It flags personal data in logs, fields with no purpose, flows to vendors with no record and data kept too long.
• Cut what you collect. It asks the purpose for each field and suggests dropping or shrinking those without one.
• Plan consent and purpose checks. It marks where in your code a consent check must block the write, not just record the choice.
• Plan deletion and retention. It plans a delete that reaches every system, a check that nothing remains, and expiry jobs that run on their own.
• Test anonymous claims. It checks whether remaining fields could still point to a person, and names a safer method.

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 engineering-privacy-engineer 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/engineering-privacy-engineer/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

• A list of the places your product keeps personal data, such as the main database, logs, analytics tools and vendors.
• The kinds of personal data you collect, such as email, address or device ID, and why you collect each one.
• How a customer asks for deletion or a copy of their data today (optional)
• How consent is collected and where it is used in your code (optional)
• Where your users live, and how long you keep each kind of data (optional)

Helpful context (optional)

• Your database schema or field list, with no real user data in it.
• Your list of vendors and the countries where each stores data.
• A list of the events your analytics tool collects.

Tools and access

• Claude, ChatGPT or Gemini with file upload: It reads a database schema, event list or vendor list next to your description and ties each field to a store.

Functions and deliverables

• Privacy engineer agent: Get a privacy design for your product with a map of where personal data lives, deletion that reaches every system and consent that is enforced.
  • A data map of each field, where it is stored, why, for how long and how it is deleted
  • A list of problems already likely to exist, such as personal data in logs or no purpose for a field
  • A consent plan that shows where the check must sit in your code
  • A deletion plan that reaches each system and ends with proof
  • A retention plan, a test for anonymous data and a fix list in order
  Source: Michael Sitarzewski and Agency Agents contributors.
• Privacy-first app designer: Describe the data your app collects. Get a privacy-first app design with less data kept, clear consent, set retention and safe storage.
  • Data minimisation list
  • Consent and retention design
  • Storage and security measures
  • Pre-launch privacy checklist
  Source: sickn33.
• Agent database migration planner: Plan a database migration that keeps your existing agent data. You get a schema map, staging checks, release order and rollback limits.
  • A current-state summary of your tables, constraints, data size and backups
  • A source-to-target mapping of agent records to your tables
  • Ordered migration steps, with backfill and access rules
  • Staging checks and a rehearsal plan on a test copy
  • Release order and rollback limits
  • A list of what is missing and what was not run
• Agent database setup planner: Plan the database your AI agents need. Get a schema map, migration steps, access rules and a staging test plan that keeps existing data safe.
  • A schema map: the tables, columns, keys and indexes for jobs and activity
  • Migration files in order, forward only, with what each one changes
  • An access rule for each table, and which key belongs where
  • A staging test plan using made-up rows
  • A rollback and recovery plan
  • A list of missing inputs and checks that did not run
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You promise customers they can delete their data and you are not sure your product really removes it everywhere.
• You are about to ship a feature that collects new personal data and want the privacy design done before launch.
• You share data with an analytics or ad tool and want consent to actually stop the data when a user says no.
• You want to call a dataset anonymous before sharing it and need to know if people could still be picked out.

Limits to check before starting

• You want to know which privacy laws and duties apply to you. Use the Data privacy officer.
• You want your code checked for privacy risks against GDPR. Use the GDPR privacy checker.
• You have a new app idea and want privacy built in from the start. Use the Privacy-first app designer.
• You want it to scan your systems or run deletions. It writes the plan. Your developers build and run it.

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 from this page and add it to your AI as a skill. If you cannot, paste SKILL.md into a new chat, then paste references/source-agent.md.
• List your systems and the personal data in each. Add how deletion and consent work now. Paste code only if it holds no real personal data or keys.
• Type "Build the data map first, then the deletion and consent plan. Mark every system you are guessing about and do not call anything deleted or anonymous without proof."
• Read the data map and the gaps first. Fill in what you know, then ask it to run the plan again.
• Give the plan to your developers. Take any legal question to a privacy lawyer. Check each rule it quotes against the law's own text.
• Build it, then test deletion by running a real test request and checking each system yourself.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-privacy-engineer 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.