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Privacy By Design

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Privacy by Design (PbD) knowledge base for assessing proactive privacy practices against the 7 Foundation Principles, with data retention and disposal lifecycle checks and cross-jurisdictional mappings (GDPR, CCPA, APP).
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Privacy by Design (PbD) skill provides a comprehensive knowledge base and assessment protocol for evaluating software systems against the 7 Foundation Principles established by Dr. Ann Cavoukian. It enables users to conduct proactive privacy reviews, identify deficiencies, and map findings to regulatory requirements including GDPR, CCPA/CPRA, and Australian Privacy Principles. The skill includes structured checklists, implementation patterns, and a finding schema to ensure consistent, evidence-based privacy assessments across the development lifecycle.

What it does

  • Assesses system designs against the 7 Foundation Principles of Privacy by Design.
  • Provides a structured protocol for conducting privacy impact assessments.
  • Maps privacy findings to GDPR, CCPA/CPRA, and Australian Privacy Principles.
  • Evaluates data retention, disposal, and lifecycle management practices.
  • Identifies privacy-related anti-patterns in architecture and configuration.
  • Generates actionable remediation items for identified privacy deficiencies.

When to use it

  • During the design phase of a new system or feature involving personal data.
  • When performing a proactive privacy audit to identify potential compliance gaps.
  • To verify that privacy controls are embedded into the architecture rather than added post-deployment.
  • When establishing data retention and disposal schedules for sensitive information.
  • To generate structured privacy assessment reports for stakeholders.

Advantages

  • Promotes a proactive, preventative approach to privacy rather than reactive compliance.
  • Standardizes privacy assessments using a clear, evidence-based schema.
  • Facilitates cross-jurisdictional compliance mapping.
  • Integrates privacy considerations directly into the software development lifecycle.
  • Provides clear, actionable remediation guidance for identified risks.

Usage examples

“Assess our new user registration flow against the 7 Privacy by Design principles.”
The agent will walk through each of the 7 principles, checking for proactive design, default privacy settings, and end-to-end security, ultimately providing a compliance verdict and specific remediation steps.
“Check our data retention policy for compliance with PbD-05.”
The agent will evaluate the current retention and disposal lifecycle against Principle 05, identifying gaps in data minimization or legal hold procedures and suggesting improvements.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/privacy-by-design/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip privacy-by-design.zip -d .agents/skills

Documentation by Microsoft

Privacy by Design

This SKILL.md is the entrypoint for the Privacy by Design skill.

The skill provides structured assessment references for the 7 Foundation Principles of Privacy by Design (Cavoukian, 2009). Consumers that explicitly load it can assess privacy practices across the software lifecycle. It complements privacy-standards with principle-level criteria, retention and disposal checks, regulatory mappings, a finding schema and implementation patterns. Current agent wiring is described under Integration below.

[!NOTE] This skill is a planning aid, not legal advice. Its standards summaries support privacy reasoning and review preparation; they do not substitute for qualified legal counsel or a formal regulatory interpretation.

Goal

Produce an evidence-based assessment against the 7 PbD Foundation Principles, with per-principle verdicts, explicit evidence gaps, deficiency severity, and remediation for established deficiencies. Assessment labels describe this planning rubric, not legal certification.

Success criteria

  • All 7 principles have a verdict, evidence completeness, and severity (N/A when no deficiency is established).

  • Each FAIL or PARTIAL finding includes a specific, actionable remediation.

  • Findings use the structured schema defined in finding-schema.md.

  • Cross-jurisdictional obligations are identified for the applicable regulatory scope.

  • Retention and disposal are assessed for any system that stores personal data.

  • An overall assessment verdict (COMPLIANT / PARTIALLY_COMPLIANT / NON_COMPLIANT / INCOMPLETE) is determined without treating missing evidence as a failed control.

  • Finding, principle, checklist and severity counts reconcile under the finding schema.

When to use this skill

Use when you need to:

  • Assess a system, feature, or data flow against the 7 PbD Foundation Principles

  • Verify that privacy is embedded proactively (not bolted on after incidents)

  • Check data retention schedules, disposal methods, and legal hold procedures

  • Map privacy requirements across GDPR Art. 25, CCPA/CPRA, and Australian Privacy Principles

  • Generate per-principle PASS/PARTIAL/FAIL/INSUFFICIENT_EVIDENCE results with evidence-backed severity

  • Identify code-level and configuration-level privacy patterns or anti-patterns

  • Produce backlog items from privacy assessment findings

Do not use for:

  • General privacy standards mapping without principle assessment (use privacy-standards)

  • Security threat modeling (use security-planning or OWASP skills)

  • RAI assessment (use rai-planner)

  • Legal advice or regulatory compliance certification

Stop rules

  • Stop the assessment if the system boundary cannot be established.

  • Stop and escalate if processing occurs in a jurisdiction not covered by this skill's mappings.

  • Do not fabricate evidence; record INSUFFICIENT_EVIDENCE and the evidence needed. Continue supported checks without inventing deficiency remediation for unknowns.

  • Do not provide legal opinions; frame all findings as planning guidance requiring qualified review.

  • Do not duplicate security findings that the security-planning skill already covers; cross-reference them.

Normative references (PbD 7 Foundation Principles)

  1. 00 Principle Index

  2. 01 Proactive Not Reactive

  3. 02 Privacy as the Default

  4. 03 Privacy Embedded into Design

  5. 04 Full Functionality

  6. 05 End-to-End Security

  7. 06 Visibility and Transparency

  8. 07 Respect for User Privacy

Operational references

Supplementary references

Skill layout

  • SKILL.md — this file (skill entrypoint).

  • references/ — the PbD normative documents, operational guides, and supplementary material.

    • 00-principle-index.md — index of all principle identifiers, categories, source mappings, and cross-references.

    • 01 through 07 — one document per PbD Foundation Principle with checklist, controls, anti-patterns, and regulatory mappings.

    • assessment-protocol.md — phased assessment protocol for agents conducting PbD reviews.

    • finding-schema.md — YAML-based finding and summary schema for structured output.

    • implementation-patterns.md — code-level and configuration-level patterns agents can verify.

    • data-retention-and-disposal.md — Principle 05 deep-dive: retention schedules, disposal methods, legal holds.

    • cross-jurisdictional-mapping.md — regulatory equivalence matrix across GDPR, CCPA/CPRA, and APP.

    • industry-privacy-profiles.md — telco, healthcare, financial sector profiles with .pbd-config.yml customization schema.

Citation-field vocabulary

Use these fields when capturing a finding so the reviewer can assert a stable source-control reference:

  • pbd_principle: Principle number (1–7) with short name, e.g., PbD-01 Proactive Not Reactive

  • gdpr_article: GDPR article reference, e.g., Art. 25

  • app_principle: Australian Privacy Principle number, e.g., APP 1

  • ccpa_section: CCPA/CPRA section reference

Finding severity conventions

SeverityMeaning
CRITICALEstablished deficiency with severe direct risk, or FAIL under an effective CRITICAL industry priority
HIGHPrinciple is violated with direct risk to data subjects
MEDIUMPrinciple is partially met; gaps exist that could lead to non-compliance
LOWMinor gap; principle intent is largely met but documentation or controls could improve
N/ANo deficiency established, including PASS and unknown-only results

Data sensitivity informs the impact of a proven deficiency; it does not create one. Preserve CRITICAL in records and summaries. CRITICAL and HIGH map to P1, with CRITICAL ordered first; MEDIUM maps to P2 and LOW to P3. PASS and unknown-only results do not create remediation backlog items.

Finding verdict conventions

VerdictMeaning
PASSAll applicable checks are MET, with no unresolved evidence or anti-patterns
PARTIALA deficiency is established but the FAIL threshold is not met
FAILMore than half of applicable checks are NOT MET, or an uncompensated critical anti-pattern is established
INSUFFICIENT_EVIDENCENo deficiency established, but unknown evidence or zero applicable checks prevents PASS

Apply the ordered rules in the assessment protocol. Record evidence completeness independently so a proven deficiency is not hidden by unknown checks. Suppressions require a documented applicability reason and do not count as MET.

Overall assessment verdicts

VerdictCondition
NON_COMPLIANTOne or more principles are FAIL; retain any evidence gaps separately
INCOMPLETENo FAIL, but any principle or applicable retention/jurisdiction check has incomplete evidence
PARTIALLY_COMPLIANTComplete evidence, no FAIL, and at least one PARTIAL
COMPLIANTComplete evidence and all 7 principles PASS

Evaluate these rows in order. Never use null for a verdict or interpret an unknown jurisdictional baseline as met or failed.

Integration with Privacy Planner and Reviewer

This skill complements privacy-standards (which provides the NIST PF, GDPR, CCPA, OWASP backbone). A consumer performing PbD assessment should explicitly load both:

  • privacy-standards — standards mapping, data-flow reasoning, DPIA thresholds

  • privacy-by-design — principle-level assessment, retention/disposal checks, cross-jurisdictional equivalence, implementation pattern verification

The current Privacy Planner and Privacy Reviewer do not explicitly load privacy-by-design. This skill supplies assessment knowledge, not automatic integration; dedicated agent wiring is outside this skill change. It remains background-only (user-invocable: false).

Interaction with other skills

SkillRelationship
privacy-standardsProvides data inventory, DPIA thresholds, and standards backbone; PbD builds on top
security-planningSecurity controls overlap with PbD-05; reference rather than duplicate
rai-plannerRAI data-use concerns feed into PbD-04 (full functionality) and PbD-07 (user respect)
secure-by-designSBD-11 (Secure Deprecation) aligns with PbD-05 lifecycle; cross-reference for decommissioning

Attribution and licensing posture

SKILL.md and references/ contain original assessment guidance and paraphrased summaries under CC BY 4.0. The cited publications remain authoritative and retain their own rights; this license does not relicense upstream text. Principle names and legal identifiers are preserved for traceability. No upstream document is reproduced or bundled.

Cavoukian PbD Foundation Principles

  • Author: Dr. Ann Cavoukian, Information and Privacy Commissioner of Ontario

  • Source: https://www.ipc.on.ca/sites/default/files/legacy/2018/01/pbd.pdf

  • Modifications: Principles restructured into agent-consumable assessment checklists with cross-references to GDPR, CCPA, and APP; original principle text paraphrased with attribution

  • Note: The 7 Foundation Principles are widely referenced in academic and regulatory literature; content here is paraphrased for planning use

GDPR Art. 25 (Data Protection by Design and by Default)
Australian Privacy Principles (APP)
CCPA/CPRA

Files

references/implementation-patterns.md

title: Implementation Patterns description: Pragmatic code-level and architecture-level patterns agents can verify when assessing PbD principle adherence

Implementation Patterns

This reference provides pragmatic, verifiable implementation patterns that an agent can look for in code, configuration, and architecture when assessing PbD principle adherence. Each pattern maps to one or more principles and provides concrete indicators.

PbD-01: Proactive indicators in code

Privacy Impact Assessment integration

Look for:

  • Pre-commit hooks or CI checks that flag new personal data collection points

  • Architecture Decision Records (ADRs) with privacy as a decision driver

  • Issue templates or PR templates that include privacy impact questions

  • Privacy-tagged user stories or acceptance criteria in backlog items

Privacy threat modeling artifacts

Look for:

  • STRIDE analysis files that include privacy-specific threats (information disclosure, elevation of privilege for data access)

  • Data flow diagrams annotating personal data boundaries

  • Privacy risk registers in project documentation

PbD-02: Default setting patterns

Configuration-level checks
# PASS indicators — privacy-protective defaults
analytics:
  tracking_enabled: false        # Disabled by default
  anonymize_ip: true            # Anonymized by default
  consent_required: true        # Opt-in required

cookies:
  third_party: disabled         # No third-party by default
  session_only: true            # Minimal persistence

user_profile:
  visibility: private           # Private by default
  search_indexing: false        # Not indexed by default
# FAIL indicators — privacy-invasive defaults
analytics:
  tracking_enabled: true        # Tracking without consent
  anonymize_ip: false           # Full IP collection
  consent_required: false       # No consent gate

user_profile:
  visibility: public            # Public by default
  search_indexing: true         # Indexed without consent
Consent mechanism patterns
PASS: Consent flows use opt-in (unchecked checkboxes, explicit "I agree" action)
FAIL: Pre-checked consent boxes, bundled consent, consent buried in terms
PASS: Consent withdrawal is a single-action operation accessible from the same location
FAIL: Withdrawal requires contacting support or navigating obscure settings
API default patterns
PASS: API endpoints return minimal fields by default; expand via explicit ?fields= parameter
FAIL: API endpoints return all user data by default including sensitive fields
PASS: Pagination defaults to smallest reasonable page size
FAIL: Endpoints return unbounded result sets with full user records

PbD-03: Embedded design indicators

Architecture patterns
PatternPbD alignmentWhat to look for
Data classification in schemaPbD-03Column/field annotations marking PII, sensitive, public
Purpose-binding at collectionPbD-03Data tagged with processing purpose at ingestion point
Privacy boundaries in microservicesPbD-03Separate services for identity vs. analytics vs. business logic
Tokenization/pseudonymizationPbD-03User identifiers replaced with tokens in analytics pipelines
Field-level encryptionPbD-03Sensitive fields encrypted independently of storage encryption
Code-level indicators

Look for:

  • Data classification decorators or annotations on model classes

  • Purpose-binding metadata attached to data collection endpoints

  • Separate data access layers for PII vs. non-PII queries

  • Privacy-aware logging that redacts or masks personal data

  • Input validation that rejects unnecessary personal data fields

# PASS indicator — data classification in code
@data_classification(category="sensitive", purpose="billing")
class PaymentInfo:
    card_last_four: str  # Only last 4 stored
    billing_zip: str

# PASS indicator — privacy-aware logging
logger.info("Payment processed", extra={"user_id": mask(user_id), "amount": amount})

# FAIL indicator — logging PII
logger.info(f"Payment for {user.email} with card {card_number}")

PbD-04: Positive-sum indicators

Non-discrimination patterns

Look for:

  • Feature flags or configuration that ensures opted-out users retain full core functionality

  • A/B test configurations that do not degrade experience for privacy-conscious cohorts

  • Terms of service that do not condition service access on optional data collection

  • No differential pricing or feature gating based on consent choices

Code indicators
PASS: Service works fully with analytics disabled
FAIL: Features break or degrade when tracking is blocked
PASS: Rate limits and quotas are identical regardless of consent status
FAIL: Higher quotas or premium features require accepting additional data collection

PbD-05: Lifecycle security patterns

Encryption verification
CheckEvidence
Encryption at restStorage configuration shows AES-256, managed keys, or customer-managed keys
Encryption in transitTLS 1.2+ enforced; HTTP redirected to HTTPS; HSTS headers present
Key rotationKey management policy shows rotation schedule (e.g., 90 days)
Cryptographic erasure capabilityKey management supports key deletion as a disposal method
Retention enforcement patterns
# PASS — automated retention enforcement
storage:
  lifecycle_rules:
    - condition:
        age_days: 365
        matches_prefix: "user-analytics/"
      action: delete

  backup_retention:
    max_days: 90
    auto_purge: true

# FAIL — no lifecycle management
storage:
  lifecycle_rules: []  # No rules defined
  backup_retention:
    max_days: unlimited
Deletion verification

Look for:

  • Cascading delete logic that removes data from all stores (primary, cache, backup, CDN)

  • Deletion confirmation logs or audit trail entries

  • Soft-delete to hard-delete pipeline with defined timelines

  • Data subject access request (DSAR) handling workflows

PbD-06: Transparency indicators

Privacy notice patterns

Look for:

  • Privacy policy accessible from every page (footer link, modal, or dedicated route)

  • Plain-language summaries alongside legal text

  • Version history of privacy notices (git history or CMS versioning)

  • Automated notification to users when privacy policy changes materially

Data access patterns
PASS: Self-service DSAR portal or API endpoint for data export
FAIL: Users must email support to request their data
PASS: Data export includes all categories with clear labeling
FAIL: Data export is partial or requires multiple requests
PASS: Response within defined SLA (e.g., 30 days per GDPR)
FAIL: No defined timeline for data access requests
Records of processing

Look for:

  • Processing activity register (spreadsheet, database, or documentation)

  • Each entry lists: purpose, legal basis, categories, recipients, retention, safeguards

  • Register is maintained and updated when processing changes

PbD-07: User-centric indicators

Privacy control accessibility

Look for:

  • Privacy settings reachable within 2 clicks/taps from main navigation

  • Granular consent management (per-purpose, not all-or-nothing)

  • Account deletion available as self-service (not "contact support to delete")

  • Data portability export in standard formats (JSON, CSV)

  • WCAG 2.2 AA compliance for privacy-related UI elements

Consent granularity patterns
PASS: Separate consent for analytics, marketing, third-party sharing, profiling
FAIL: Single "I agree to everything" consent covering all purposes
PASS: Per-partner consent for data sharing (can consent to Partner A but not B)
FAIL: Blanket "share with partners" consent
Automated decision-making

Look for:

  • Disclosure of automated profiling or algorithmic decisions affecting users

  • Mechanism to request human review of automated decisions

  • Explanation capability for algorithmic outcomes (even if simplified)

Cross-cutting indicators

CI/CD privacy checks
CheckPrincipleWhat to verify
PII scanner in pipelinePbD-01, PbD-03Automated detection of new PII collection points
Privacy test suitePbD-02, PbD-05Tests verifying privacy defaults and data lifecycle
Consent flow testsPbD-02, PbD-07Automated verification of opt-in behavior
Data classification linterPbD-03Schema changes require classification annotation
Retention policy validatorPbD-05New data stores require retention configuration
Infrastructure-as-code indicators
PASS: Terraform/Bicep includes lifecycle rules on storage resources
PASS: Network policies restrict data egress to approved destinations
PASS: Logging configuration excludes PII fields from log sinks
FAIL: Storage resources created without lifecycle management
FAIL: No network-level data exfiltration controls
FAIL: Application logs contain unmasked personal data

Patterns synthesized from privacy engineering best practices, GDPR technical guidance (Article 29 Working Party), and architecture patterns observed in privacy-mature systems. Provided as planning reference material with attribution.

Source principles: IPC Ontario, Privacy by Design; regulatory basis: GDPR Article 25. Indicators are examples, not proof of a whole-principle PASS; apply the assessment protocol to all applicable evidence.

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

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Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/project-planning/privacy-by-design · commit f425e94
Licence
Creative Commons Attribution 4.0 International (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
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