gdpr-audit

GDPR Audit

A detection-guide-driven audit skill. Reads code / plan / schema / IaC artifacts and returns a structured findings report with GDPR article citations, severity, confidence, evidence, and recommended fixes.

Output is "technical GDPR audit of provided materials" — never "GDPR compliant" or "non-compliant". Compliance is a legal determination; this skill provides technical evidence and gap analysis. Always include the disclaimer at the end of the report.

When to use

• Auditing an existing codebase, repo, or directory tree
• Reviewing an implementation plan, design doc, or RFC for GDPR implications
• Reviewing a database schema or data model for personal data handling
• Reviewing IaC (Terraform / Pulumi / CDK / k8s manifests) for transfers, encryption, region constraints
• Pre-DPIA technical scoping
• Vendor onboarding (auditing the integration code of a new processor / SDK)

When NOT to use

• Drafting privacy notices, DPAs, or DPIAs from scratch — that's a writing task, not an audit
• Compliance certification, regulatory filings, or DPA correspondence — needs a qualified DPO / lawyer
• Org-maturity assessment (training, governance, DPO independence) — not visible from code / plan input
• Auditing non-GDPR jurisdictions (CCPA, LGPD, PIPL) — citations and triggers will be wrong

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Audit workflow

Run these phases in order. Do not skip phases.

Phase 1 — Scope statement

State explicitly what was reviewed and what wasn't, before any findings.

## Scope
- Materials reviewed: <repo, commit/SHA, branch, paths or "full tree">
- Date of audit: <YYYY-MM-DD>
- Excluded: <e.g., binary assets, vendored deps, test fixtures, .git history>
- Assumptions: <e.g., "controller role assumed unless evidence indicates processor role">
- Missing context: <list of org/policy artifacts not provided to the audit>

This protects the audit from being misread as exhaustive.

Phase 2 — Processing map (build first, find against second)

Before scanning for issues, build a processing map. Every later finding ties back to a row here.

| Activity | Data categories | Subjects | Purpose | Lawful basis (claimed / inferred / unclear) | Recipients / processors | Country / region | Retention (claimed / inferred / unclear) |

Sources for the map: API routes, DB schemas, env vars, IaC region constraints, third-party SDK invocations (Stripe, Auth0, Segment, GA, Sentry, Datadog, Supabase, AWS clients), background jobs, cron, queue consumers, ML / AI training pipelines.

If no processing map can be built (truly opaque artifact), say so and stop. A findings report without a processing map is unfounded. See references/01-data-discovery.md for detection signals.

Phase 3 — Detection sweep using the reference guides

For each row in the processing map, walk the relevant guides in references/. Each guide is a detection guide with: what to look for, signals, severity rules, false-positive controls, stack-specific examples, evidence needed.

Guides to walk for every audit: 01, 02, 06, 07, 09, 10, 14, 15. Conditional guides (see Trigger overlays below): 03, 04, 05, 08, 11, 12, 13.

Phase 4 — Findings emission

Emit findings using the schema below. One finding per discrete gap.

Phase 5 — Report assembly

Order: Scope → Processing map → Findings table (sorted Critical → Low within each finding type) → Finding details → Summary of finding-type distribution → Recommended next actions → Disclaimer.

Phase 6 — Drill-down offer

After the report, offer:

"I can draft concrete patches for any of these findings on request. Tell me the finding number(s) and I'll generate the actual code / SQL / IaC / markdown changes."

Do not produce patches in the initial report — they bury the audit signal.

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Finding schema

Every finding has these fields. Missing fields = finding is incomplete.

id: F-NN                       # sequential within report
title: <short noun phrase>
severity: Critical | High | Medium | Low
finding_type: confirmed_issue | likely_issue | evidence_gap | advisory
confidence: High | Medium | Low
risk_to_rights: <what could happen to a data subject — not "fines">
location: <file:line | section heading | "(absence)">
articles: [Art. 5(1)(c), Art. 25(2), Recital 39]
guidance: [EDPB Guidelines 4/2019 on Article 25, ICO Right of Access guidance]   # optional
affected_data: [email, IP, payment, health, children, employees, ...]
affected_subjects: [end users, employees, vendor staff, ...]
processing_activity: <signup | billing | analytics | recommendation | support_export | ...>
evidence: |
  <verbatim snippet from code / plan, with file:line>
recommended_fix: |
  <1–3 sentences. Not a patch.>
verification_needed: |
  <what specific evidence would close this finding>

Severity rubric

Severity	Meaning
Critical	Direct violation with high risk to rights AND high confidence. Examples: clear-text PII in public logs; consumer EU data sent to third country with no transfer mechanism; high-risk profiling without DPIA.
High	Direct violation OR high-risk gap with material confidence. A supervisory authority would likely act if discovered.
Medium	Material gap with rectifiable design choice. Would be remediation in a real audit.
Low	Best-practice deviation. No immediate risk.

Severity is not likelihood × impact — that pseudo-precision misleads. Severity captures the audit-judgment call; confidence is the separate axis.

finding_type

Type	Meaning	Example
confirmed_issue	Positive evidence shows a violation	analytics.track(user.email) fires before consent is recorded
likely_issue	Strong signal but missing context to be certain	High-risk profiling code present; lawful basis claim unclear
evidence_gap	A control SHOULD be visible but isn't	Stripe SDK detected; DPA evidence not found in provided materials
advisory	Technically defensible, design-level improvement	Pseudonymization could replace direct identifiers in analytics_events

Discipline rule for evidence_gap: only flag absence when there is positive evidence of processing AND a reasonably expected control. "No docs/ropa.md" is not a finding. "Stripe SDK + cross-border data flow + no DPA artifact in provided materials" is an evidence_gap. The skill does not invent organizational policy gaps from thin air.

confidence

Level	Meaning
High	Direct code evidence, no plausible alternative interpretation
Medium	Strong inference from signals; alternative interpretations exist but are less likely
Low	Pattern-based suspicion; needs human verification

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Trigger overlays

Apply these when their triggers fire. They escalate severity, force chapter walks, and add national context.

Children's data overlay

Trigger: schema/forms accept date of birth, age, school, parent contact; product is consumer/social/educational; signup form lacks age gate. Effect: every finding involving children's data goes up one severity tier. Cite Art. 8 + Recital 38. Member State age threshold (13–16) flagged for verification. Data minimization scrutinized harder.

Special category overlay (Art. 9, 10)

Trigger: schema fields suggest health, biometric, genetic, racial/ethnic, political, religious, trade-union, sex life, sexual orientation, criminal convictions; medical/HR/insurance domain. Effect: severity floor of High for any unprotected processing. Art. 9(2) basis must be explicitly cited. Security expectations rise (Art. 32 + Art. 9 combined). DPIA almost certainly required (force ch14).

Profiling / automated decision overlay (Art. 22)

Trigger: code performs scoring, ranking, fraud detection, recommendation, embeddings on personal data, automated KYC, automated employment decisions. Effect: force walk of ch13 and ch14. Flag Art. 22 even if the system is not "solely automated" but is being used that way in practice (humans rubber-stamping the model output count as solely automated under EDPB guidance).

Cross-border / vendor overlay

Trigger: SDK or HTTP client points to non-EU endpoint; AWS region outside eu-*; GCP region outside europe-*; external SaaS without obvious EU data residency. Effect: force walk of ch11 and ch12.

National context overlay

Trigger: user mentions a specific Member State, repo contains DE/FR/IT/ES/NL locale, employment context, or healthcare/insurance/finance domain. Add notes per jurisdiction:

• Germany (BDSG): DPO mandatory ≥ 20 employees doing automated PII processing (§ 38); § 26 employment data; § 4 video surveillance; § 31 credit scoring; works council co-determination on monitoring tools.
• France (LIL/CNIL): stricter cookie guidance; employee monitoring requires CNIL-compliant impact assessment.
• UK (UK GDPR + DPA 2018): ICO guidance; international transfers use UK IDTA or Addendum to EU SCCs.
• Italy (Garante): opinions issued on AI/training data sources; cookie enforcement.
• Other Member States: flag for verification — derogations vary.

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DPIA routing rule (mandatory regardless of which chapter triggers)

Before the report ships, check whether the processing activities crossed any DPIA threshold. If two or more of the EDPB 9 criteria apply, emit a finding for ch14 even if the rest of the audit found nothing wrong:

1. Evaluation or scoring (including profiling and predicting)
2. Automated decision-making with legal/significant effect
3. Systematic monitoring (incl. publicly accessible areas)
4. Sensitive data or data of a highly personal nature
5. Data processed on a large scale
6. Matching or combining datasets
7. Data concerning vulnerable subjects (children, employees, patients)
8. Innovative use or new technological/organizational solutions (AI, IoT, biometric)
9. Processing that prevents data subjects from exercising a right or using a service/contract

EDPB adopted (April 2026) a DPIA template for consultation — cite that, not legacy WP29 templates.

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Output format

# GDPR Audit Report

## Scope
<Phase 1 block>

## Processing map
<Phase 2 table>

## Findings — confirmed issues (Critical → Low)
| ID | Sev | Conf | Article(s) | Activity | Title |

## Findings — likely issues
| ID | Sev | Conf | Article(s) | Activity | Title |

## Findings — evidence gaps
| ID | Sev | Conf | Article(s) | Activity | Title |

## Findings — advisory
| ID | Sev | Conf | Article(s) | Activity | Title |

## Finding details
F-01
  Severity: Critical / Confidence: High / Type: confirmed_issue
  Articles: Art. 5(1)(f), Art. 32(1)
  Risk to rights: <...>
  Location: src/auth/login.py:42
  Affected data: email, IP address
  Affected subjects: end users
  Processing activity: signup
  Evidence:
    logger.info(f"login attempt: {email} from {request.client_host}")
  Recommended fix:
    Hash the user identifier in log output; mask IP to /24 for security analytics if needed; configure structured-logging redaction.
  Verification needed:
    Show the redaction config and a sample log line after change.

F-02 ...

## Summary
- Confirmed issues: <N>
- Likely issues: <N>
- Evidence gaps: <N>
- Advisory: <N>
- DPIA recommended: yes/no — <reason>

## Recommended next actions
1. <ranked list, highest-leverage first>

## Disclaimer
This is a technical GDPR audit of the provided materials. It is not legal advice and does not constitute a compliance determination. Consult a qualified DPO or data protection lawyer for legal questions, supervisory authority engagement, or material decisions.

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Reference guide index

The detection guides live in references/, numbered 01–15 (referred to as ch01–ch15 in the overlays above).

Ref	Title	Primary articles	Walk when
01	Data discovery, classification, processing map	Art. 4, 30	Always (drives Phase 2)
02	Purpose limitation, lawful basis, special-category basis	Art. 5(1)(b), 6, 9, 10	Always
03	Transparency and notices	Art. 12–14	User-facing collection points exist
04	Consent and preference management	Art. 6(1)(a), 7, 8	Consent claimed/required for any activity
05	Cookies, tracking, analytics, SDKs	Art. 6, ePrivacy 5(3)	Browser-side tracking/analytics/marketing detected
06	Privacy by design / default + minimization	Art. 5(1)(c), 25	Always
07	Retention, deletion, backups, derived data	Art. 5(1)(e), 17	Always
08	DSAR and data subject rights workflows	Art. 12, 15–22	Product holds personal data of identifiable subjects
09	Security, access control, encryption, resilience	Art. 32	Always
10	Logging, telemetry, audit trails, overcollection	Art. 5(1)(c), 5(2), 32	Always
11	Vendors, processors, subprocessors, controller / processor roles	Art. 24, 26, 28, 29	Third-party processors detected
12	International transfers and TIAs	Art. 44–49, Schrems II	Non-EU data flows detected
13	Profiling, automated decisions, AI, model training	Art. 22, Recital 71	Profiling / AI overlay triggered
14	DPIA / high-risk triage + prior consultation	Art. 35, 36	Always (DPIA routing rule)
15	Accountability evidence: RoPA, LIA, DPA, SCC/TIA, breach register, breach notification	Art. 5(2), 30, 33, 34	Always

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Common rationalizations to resist

Excuse	Reality
"There's no PII here, it's just emails."	Email is personal data (Art. 4(1)).
"We use legitimate interest, so we don't need consent."	LI requires a documented LIA. Direct marketing still triggers Art. 21 right to object.
"This is just analytics, it's anonymous."	Individual-level events with stable identifiers (cookie, fingerprint, user_id) = pseudonymous, not anonymous. Still personal data.
"We're a processor, not a controller."	Processors have direct obligations under Art. 28(3), 32, 33(2).
"Stripe handles payment compliance."	Stripe's posture covers Stripe's processing. The integrating system has its own controller obligations.
"We don't need a DPIA, we're not doing anything weird."	DPIA threshold = Art. 35(3) + EDPB 9-criteria. Many "normal" SaaS hits 2+ (analytics + employees + vendors + scoring).
"Our cloud provider is GDPR-compliant."	Provider compliance ≠ your compliance. You still need DPA, transfer mechanism, region selection.
"User opted in to ToS."	ToS opt-in is not consent under Art. 7. Bundled consent is invalid.
"We delete on request, that's enough."	Erasure (Art. 17) is one of seven rights. Access, rectification, portability, restriction, objection, automated-decision rights all need workflows.
"We don't need a DPA — they're a Data Sub-processor of our Data Sub-processor."	Sub-processor chain obligations under Art. 28(2) and 28(4). Each link needs contractual flow-through.