Privacy By Design

Privacy by Design

Overview

Integrate privacy protections into software architecture from the beginning, not as an afterthought. This skill applies Privacy by Design principles (GDPR Article 25, Cavoukian's framework) when designing databases, APIs, and user flows. Protects real users' data and builds trust.

When to Use This Skill

• Use when building apps that collect personal data (names, emails, locations, preferences)
• Use when designing database schemas, APIs, or authentication flows
• Use when the user mentions forms, user accounts, analytics, or third-party integrations
• Use when deploying to production—verify privacy controls before launch

Legal Frameworks

GDPR (EU) — Primary reference. Article 25 mandates "data protection by design and by default." Applies to EU users and often adopted globally.

CCPA (California) — Right to know, delete, opt-out of sale. Similar principles: minimize, disclose, allow control.

LGPD (Brazil) — Aligned with GDPR. Purpose limitation, necessity, transparency. Applies to Brazil users.

Design for the strictest framework you target; it often satisfies others.

Core Principles

1. Data Minimization

Collect only what is strictly necessary. Every field needs a documented justification. Avoid "we might need it later."

2. Purpose Limitation

Store the purpose of each data point. Do not reuse data for purposes the user did not consent to.

3. Storage Limitation

Define retention periods. Implement automated deletion or anonymization when retention expires. Never keep data "forever" by default.

4. Privacy as Default

Opt-in for optional collection, not opt-out. Sensitive settings (analytics, marketing) off by default. No pre-checked consent boxes.

5. End-to-End Security

Encrypt at rest and in transit. Use RBAC. Log access to sensitive data for audit.

6. Transparency

Document what is collected and why. Clear privacy policies. Easy access and deletion for users.

User Rights (GDPR)

Ensure these are implementable from day one:

Deep Dive: Why It Matters

Data minimization — Less data = less breach impact, lower storage cost, simpler compliance. Each field is a liability.

Purpose limitation — Reusing data without consent is illegal under GDPR. Document purpose in schema or metadata.

Retention — Indefinite storage increases risk and violates GDPR. Define retention_days per data type; automate cleanup.

Logging — Logs often leak PII. Redact emails, IDs, tokens. Use structured logging with allowlists.

Third parties — Every SDK (analytics, crash reporting, ads) may send data elsewhere. Audit dependencies; require consent before loading.

Code Examples

JavaScript/Node — Minimal User Model

// BAD: Collecting everything "just in case"
const user = { email, name, phone, address, birthdate, ipAddress, userAgent, ... };

// GOOD: Minimal, documented purpose
const user = {
  email,        // purpose: authentication
  displayName,  // purpose: UI display
  createdAt,    // purpose: account age
};

JavaScript — Consent Before Tracking

// BAD: Track first, ask later
analytics.track(userId, event);

// GOOD: Check consent first
if (userConsent.analytics) {
  analytics.track(userId, event);
}

Python — Safe Logging

# BAD: Logging PII in plain text
logger.info(f"User {user.email} logged in from {request.remote_addr}")

# GOOD: Redact or hash identifiers
logger.info(f"User {hash_user_id(user.id)} logged in")
# Or: logger.info("User login", extra={"user_id_hash": hash_id(user.id)})

SQL — Schema with Purpose and Retention

-- GOOD: Document purpose and retention in schema
CREATE TABLE users (
  id UUID PRIMARY KEY,
  email VARCHAR(255) NOT NULL,  -- purpose: auth, retention: account lifetime
  display_name VARCHAR(100),   -- purpose: UI, retention: account lifetime
  created_at TIMESTAMPTZ,      -- purpose: audit, retention: 7 years
  last_login_at TIMESTAMPTZ    -- purpose: security, retention: 90 days
);

-- Add retention policy (PostgreSQL example)
-- Schedule job to anonymize/delete last_login_at after 90 days

API — Return Only Needed Fields

# BAD: Returning full user object
return jsonify(user)  # May include internal fields, hashed passwords

# GOOD: Explicit allowlist
return jsonify({
    "id": user.id,
    "email": user.email,
    "displayName": user.display_name,
})

Common Pitfalls

Third-Party Audit

Before adding a dependency that touches user data:

• What data does it collect or receive?
• Where does it send data (servers, countries)?
• Is it loaded before or after user consent?
• Can we disable it if user opts out?
• Does their privacy policy align with ours?

Implementation Checklist

When building a feature that touches user data:

• Is this data necessary? Can we achieve the goal with less?
• Do we have explicit consent for this use?
• Is it encrypted (at rest and in transit)?
• Do we have a retention/deletion policy?
• Can the user export or delete their data?
• Are third-party services disclosed and consented?
• Are logs free of PII?
• Are backups included in retention policy?

Best Practices

• ✅ Ask "do we need this?" for every new data field
• ✅ Design deletion and export flows from day one
• ✅ Use hashing or tokenization for sensitive identifiers when possible
• ✅ Document purpose and retention in schema or metadata
• ❌ Don't log passwords, tokens, or PII in plain text
• ❌ Don't share data with third parties without explicit consent
• ❌ Don't assume "we'll add privacy later"—it rarely happens
• ❌ Don't expose stack traces or internal errors to clients

When to Use

This skill is applicable when building software that collects, stores, or processes personal data. Apply it proactively during design and implementation.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.

# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Privacy By Design"

Storing Results

After completing work, store security decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
  --type technical --project <project> \
  --tags privacy-by-design security

Multi-Agent Collaboration

Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
  --project <project>

Signed Audit Trail

All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.

Semantic Cache for Compliance

Cache compliance check results (semantic_cache.py) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.