privacy-data-handling
Defines how personal data is retained, accessed and deleted. Use when setting up or auditing data handling.
- License 许可
- MIT
- 所属仓库
- vibbs/company-os
- 最近更新
- 2026-10-21
- Stars
- ★ 6
- Forks
- 2
01 前言 Front Matter
- name
- privacy-data-handling
- runtimes
- { "claude": { "user-invocable": true } }
- description
- Defines how personal data is retained, accessed and deleted. Use when setting up or auditing data handling.
02 资源文件 2
- 参考文档references/api_reference.md17.0 KB
- 脚本scripts/query_clinpgx.py14.0 KB
03 SKILL.md 正文
Privacy & Data Handling
Reference
- ID: S-RISK-02
- Category: Risk
- Inputs: data inventory, regulatory requirements (GDPR, CCPA), system architecture
- Outputs: data handling policy document → artifacts/risk/
- Used by: Ops & Risk Agent
- Tool scripts: ./tools/artifact/validate.sh
Purpose
Establishes clear policies for data retention, access controls, and deletion procedures that comply with privacy regulations and protect user data throughout its lifecycle from collection to destruction.
Procedure
Step 0: Identify Data Processors
Read company.config.yaml and extract third-party data processors:
analytics.provider(e.g., PostHog, Mixpanel, Plausible)email.provider(e.g., Resend, SendGrid, Postmark)observability.error_tracking(e.g., Sentry, Datadog)ai.llm_provider(e.g., Anthropic, OpenAI)support.platform(e.g., Intercom, Crisp)
For each identified processor, add a specific line to the DPA (Data Processing Agreement) checklist:
- [ ] DPA signed with [provider]
- [ ] Data residency confirmed (EU/US)
- [ ] Sub-processor list reviewed
- [ ] Deletion/export API documented
- Inventory all personal and sensitive data collected: what data, where stored, why collected.
- Classify data by sensitivity level: public, internal, confidential, restricted.
- Define retention periods for each data category based on business need and regulatory requirements.
- Design access control policies: who can access what data, under what conditions, with what audit trail.
- Define the data deletion procedure: user-initiated deletion, retention-based expiry, right-to-be-forgotten flows.
- Document data processing lawful bases (consent, legitimate interest, contractual necessity).
- Define data breach notification procedures: detection, assessment, notification timeline.
- Design audit logging for all data access and modifications.
- Map data flows to third-party processors and define DPA requirements.
- Save the data handling policy to
artifacts/risk/. - Validate the artifact using
./tools/artifact/validate.sh.
Quality Checklist
- [ ] All personal data fields are inventoried
- [ ] Data classification levels are assigned
- [ ] Retention periods are defined and justified
- [ ] Access controls follow principle of least privilege
- [ ] Deletion procedures cover all storage locations (DB, backups, caches, logs)
- [ ] Breach notification timeline meets regulatory requirements
- [ ] Third-party data processors are documented with DPAs
- [ ] Artifact passes validation
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