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Compliance · Version 1.2.0 · Reviewed 2026-08-02

Data Anonymization Advisor

Make a defensible decision about technique selection and re-identification risk with evidence, explicit trade-offs, and a verification plan.

4 method steps 5 documented failure modes 4 diagnostic checks 7 quality gates

Chooses masking, tokenization, and aggregation techniques that survive realistic re-identification attempts.

₹99 one-time

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What this skill helps you do

  • Technique selection
  • re-identification risk
  • Utility preservation

How Data Anonymization Advisor works

You provide

Requirements, data inventory, and evidence of current controls

It inspects

Requirement type and real implementation for technique selection

It decides

A re-identification risk gap list with owners and severity

You verify

One obligation traced end to end to its enforcing system

What it checks first

Data Anonymization Advisor chooses masking, tokenization, and aggregation techniques that survive realistic re-identification attempts. Use it when the work involves Technique selection, re-identification risk, Utility preservation.

  1. Whether an obligation is a legal requirement, a contractual commitment, or internal policy.
  2. Whether a documented control is actually implemented in the system it claims to govern.
  3. Every downstream copy of regulated data, including backups, logs, and analytics.
  4. Who is accountable for each control, since an unowned control is not a control.

Failure modes it recognizes

  • Deletion implemented in the primary store while copies persist in backups, exports, and warehouses.
  • A control described in policy with no implementation, discovered during audit.
  • A subprocessor added without an agreement or the customer notification the contract requires.
  • Retention defined but never enforced by an automated job.
  • Consent collected for one purpose and reused for another without a valid basis.

Answers it will reject

  • Treating a certification report as evidence that a specific control functions.
  • Providing a definitive legal conclusion rather than a structured analysis for qualified review.
  • Relying on contract language to mitigate a risk that is technically preventable.

Decision rules it applies

  • Map every obligation to an implemented control and its evidence, or record it explicitly as a gap.
  • Prefer technical enforcement over documented intent, because intent is not a control.
  • Escalate anything constituting legal advice to qualified counsel and say so plainly.

Evidence it asks for

  • Build a data inventory covering category, purpose, basis, location, retention, and downstream copies.
  • Trace one real request or record end to end through the control being claimed.
  • Record the artifact an auditor would accept as evidence for each control.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to technique selection.
  2. Trace concrete failure or abuse paths for re-identification risk; do not report checklist items without a mechanism.
  3. Prioritize utility preservation findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Technique selection assessment
  • re-identification risk decision and action plan
  • Utility preservation verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

We want to share a dataset with a partner. Removing names and emails should be enough, right?

Expected output

Removing direct identifiers is rarely sufficient, because combinations of quasi-identifiers such as postcode, birth date, and gender re-identify most individuals. Assess uniqueness in your actual dataset and generalize or suppress the fields that make rows unique...

Boundaries and compatibility

Ideal for

  • Technique selection: produce a decision or artifact grounded in supplied evidence.
  • re-identification risk: produce a decision or artifact grounded in supplied evidence.
  • Utility preservation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.