SkillVaultskills Browse all 500 skills

Security · Version 1.6.0 · Reviewed 2026-08-02

Privacy Design Reviewer

Find and prioritize exploitable risk in data-flow minimization and consent enforcement with evidence, explicit trade-offs, and a verification plan.

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

Reviews systems for data minimization, purpose limitation, consent, retention, access, deletion, pseudonymization, and privacy-risk boundaries.

₹99 one-time

Get this skill archive

What this skill helps you do

  • Data-flow minimization
  • Consent enforcement
  • Deletion design

How Privacy Design Reviewer works

You provide

Obligations, data inventory, and current controls

It inspects

Requirement type and implemented control for data-flow minimization

It decides

A consent enforcement gap register with accountable owners

You verify

Each control mapped to evidence an auditor would accept

What it checks first

Privacy Design Reviewer reviews systems for data minimization, purpose limitation, consent, retention, access, deletion, pseudonymization, and privacy-risk boundaries. Use it when the work involves Data-flow minimization, Consent enforcement, Deletion design.

  1. Whether the obligation is a legal requirement, a contractual commitment, or an internal policy — the escalation path differs.
  2. The specific data categories and lawful basis, rather than a general statement about compliance.
  3. Data flows across jurisdictions and processors, which determine transfer obligations.
  4. Retention and deletion behavior in every downstream copy, including backups and analytics.
  5. Who is accountable for the decision, since a compliance analysis without an owner is not actionable.

Failure modes it recognizes

  • Deletion implemented in the primary store while copies persist in backups, exports, logs, and warehouses.
  • Consent collected for one purpose and reused for another without a valid basis.
  • A subprocessor added without a data-processing agreement or customer notification.
  • Retention policy defined but never enforced by an automated job.
  • A control documented in policy but not implemented in the system it describes.

Answers it will reject

  • Providing a definitive legal conclusion rather than a structured analysis for qualified review.
  • Treating a compliance certification as evidence that a specific control works.
  • Relying on contractual language to mitigate a technical risk that is technically preventable.
  • Presenting risk without severity, likelihood, and the accountable owner.

Decision rules it applies

  • Separate legal requirement, contractual obligation, and internal policy in every finding.
  • Escalate to qualified counsel for anything that constitutes legal advice, and say so plainly.
  • Map every obligation to a specific implemented control and its evidence, or mark it as a gap.
  • Prefer technical enforcement over documented intent, because documented intent is not a control.

Evidence it asks for

  • Build a data inventory: category, source, purpose, basis, location, retention, and downstream copies.
  • Trace one deletion request end to end and enumerate every store it must reach.
  • Record the evidence artifact that would satisfy an auditor for each control.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to data-flow minimization.
  2. Trace concrete failure or abuse paths for consent enforcement; do not report checklist items without a mechanism.
  3. Prioritize deletion design 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

  • Data-flow minimization assessment
  • Consent enforcement decision and action plan
  • Deletion design verification checklist

Evidence requirements

  • Code, configuration, data flows, and trust boundaries
  • Identity, authorization, and deployment context
  • Threat model, controls, and known assumptions

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

Review a feature that records detailed user interactions to personalize recommendations.

Expected output

The design collects raw interaction events indefinitely although the model only needs weekly aggregates. Minimize at collection, separate experimentation consent, expire raw events, and prevent support tooling from exposing behavioral history broadly...

Boundaries and compatibility

Ideal for

  • Data-flow minimization: produce a decision or artifact grounded in supplied evidence.
  • Consent enforcement: produce a decision or artifact grounded in supplied evidence.
  • Deletion design: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Authorizing offensive actions against systems without permission
  • Reporting theoretical issues as exploitable without a path

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.