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

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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

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/privacy-by-design/download

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)

Operational references

Assessment Protocol — step-by-step protocol for conducting a PbD assessment
Finding Schema — structured output format for findings and backlog handoff
Implementation Patterns — pragmatic code-level and architecture-level verification patterns

Supplementary references

Data Retention and Disposal — Principle 05 deep-dive: retention schedules, disposal methods, legal holds
Cross-Jurisdictional Mapping — regulatory equivalence matrix across GDPR, CCPA/CPRA, and APP
Industry Privacy Profiles — sector-specific principle priorities, checks, and .pbd-config.yml customization

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

Severity	Meaning
CRITICAL	Established deficiency with severe direct risk, or FAIL under an effective CRITICAL industry priority
HIGH	Principle is violated with direct risk to data subjects
MEDIUM	Principle is partially met; gaps exist that could lead to non-compliance
LOW	Minor gap; principle intent is largely met but documentation or controls could improve
N/A	No 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

Verdict	Meaning
PASS	All applicable checks are MET, with no unresolved evidence or anti-patterns
PARTIAL	A deficiency is established but the FAIL threshold is not met
FAIL	More than half of applicable checks are NOT MET, or an uncompensated critical anti-pattern is established
INSUFFICIENT_EVIDENCE	No 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

Verdict	Condition
NON_COMPLIANT	One or more principles are FAIL; retain any evidence gaps separately
INCOMPLETE	No FAIL, but any principle or applicable retention/jurisdiction check has incomplete evidence
PARTIALLY_COMPLIANT	Complete evidence, no FAIL, and at least one PARTIAL
COMPLIANT	Complete 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

Skill	Relationship
`privacy-standards`	Provides data inventory, DPIA thresholds, and standards backbone; PbD builds on top
`security-planning`	Security controls overlap with PbD-05; reference rather than duplicate
`rai-planner`	RAI data-use concerns feed into PbD-04 (full functionality) and PbD-07 (user respect)
`secure-by-design`	SBD-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

Source: https://eur-lex.europa.eu/eli/reg/2016/679/oj
Modifications: Mapped to PbD principles as regulatory equivalence; paraphrased with attribution

Australian Privacy Principles (APP)

Source: https://www.oaic.gov.au/privacy/australian-privacy-principles
Copyright: © Commonwealth of Australia
Modifications: Mapped to PbD principles as cross-jurisdictional equivalence; paraphrased with attribution

CCPA/CPRA

Source: https://oag.ca.gov/privacy/ccpa
Modifications: Mapped to PbD principles as cross-jurisdictional equivalence; paraphrased with attribution

Files

ATTRIBUTION.mdAdded by AutoGineer977 B
LICENSEAdded by AutoGineer16.6 KiB
SKILL.md12.9 KiB
references/00-principle-index.md4.0 KiB
references/01-proactive-not-reactive.md4.4 KiB
references/02-privacy-as-the-default.md4.5 KiB
references/03-privacy-embedded-into-design.md4.7 KiB
references/04-full-functionality.md4.4 KiB
references/05-end-to-end-security.md5.8 KiB
references/06-visibility-and-transparency.md5.6 KiB
references/07-respect-for-user-privacy.md5.5 KiB
references/assessment-protocol.md9.7 KiB
references/cross-jurisdictional-mapping.md13.3 KiB
references/data-retention-and-disposal.md7.1 KiB
references/finding-schema.md10.8 KiB
references/implementation-patterns.md10.1 KiB
references/industry-privacy-profiles.md11.3 KiB

references/06-visibility-and-transparency.md

title: 'PbD-06: Visibility and Transparency — Keep It Open' description: Privacy by Design reference for ensuring visibility and transparency in personal data practices

06 Visibility and Transparency

Identifier: PbD-06 Category: Openness

Source mapping

Cavoukian Principle 6 — Visibility and Transparency — Keep It Open
GDPR Art. 12–14 — Transparent information, communication, and modalities
APP 1 — Open and transparent management of personal information
APP 5 — Notification of the collection of personal information
CCPA §1798.100(a) — Right to know what personal information is collected

Description

People need understandable information about actual processing, and reviewers need evidence that can confirm or challenge those disclosures. Assess both the accessibility of notices and the accuracy of their claims against current operating records; publishing a policy is not proof that the system follows it.

Transparency builds trust. Organizations must be able to demonstrate that their data handling practices match their stated policies, and data subjects must have visibility into how their data is used. Accountability and auditability are the mechanisms through which transparency is maintained.

Principle checklist

A clear, accessible privacy notice accurately describes collection, use, sharing and retention, consistent with current operational evidence.
Privacy notices are written in plain language appropriate for the target audience.
Data processing activities are documented in a processing register (records of processing activities).
Data subjects can request and receive information about what data is held about them.
Third-party data sharing is disclosed including recipients and purposes.
Privacy practices are subject to independent verification or audit.
Changes to data practices are communicated to affected users before taking effect.
Complaint and inquiry mechanisms are accessible and responsive.

Reconcile contradictory evidence before PASS. For example, a 30-day destruction promise with eSIM data still recoverable at day 120 establishes an accuracy gap unless an applicable, disclosed exception explains the difference. Record both sources and dates, assess the affected transparency check as NOT MET, and cross-reference PbD-05. Preserve MET credit for independently verified notice accessibility, contact routes and other unaffected controls. If the evidence cannot establish which account applies, use INSUFFICIENT EVIDENCE for that check, not an unqualified PASS.

Controls and mitigations

Maintain a publicly accessible privacy notice that is reviewed and updated at least annually.
Implement a Records of Processing Activities (ROPA) register for internal accountability.
Provide a self-service data access mechanism (data subject access request portal or API).
Publish a transparency report covering government requests, data incidents, and privacy metrics.
Conduct periodic independent privacy audits or certify against privacy standards (ISO 27701, SOC 2 Type II Privacy).
Version-control privacy notices and notify users of material changes.
Establish a privacy contact (DPO, privacy team) with clear escalation paths.
Log and respond to privacy inquiries within defined SLAs.

Anti-patterns

Privacy notices are written in dense legal language that users cannot understand.
No records of processing activities exist internally.
Users have no mechanism to discover what data is held about them.
Data sharing with third parties is undisclosed or buried in terms and conditions.
Privacy practices cannot be independently verified.
Changes to data handling are made silently without notification to affected users.
Complaint mechanisms are non-functional or unresponsive.
Published retention or deletion promises conflict with verified operational behavior.

Regulatory cross-references

Regulation	Reference	Relevance
GDPR	Art. 12	Transparent, intelligible, easily accessible communication
GDPR	Art. 13–14	Information to be provided at collection and when obtained indirectly
GDPR	Art. 15	Right of access by the data subject
GDPR	Art. 30	Records of processing activities
APP	APP 1.3–1.6	APP privacy policy requirements
APP	APP 5	Notification of collection
APP	APP 12	Access to personal information
CCPA/CPRA	§1798.100(a)	Right to know categories and purposes
CCPA/CPRA	§1798.110	Right to know specific pieces of information
CCPA/CPRA	§1798.130	Methods for submitting consumer requests

Content paraphrased from Dr. Ann Cavoukian's 7 Foundation Principles of Privacy by Design (2009) with attribution. Regulatory mappings are planning references, not legal interpretations.

Official sources: IPC Ontario, GDPR, APPs, and CCPA.

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)

Synced

2026-11-08

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