Data Privacy Controls

Data Privacy Controls helps define an inventory of held data and the controls attached to it. The workflow lists a data category, records its purpose, identifies ownership and access, adds a retention rule and schedules review. It starts with what is held and why so that controls refer to identified data rather than an incomplete inventory.

The skill separates advice, review, conversion and build requests before producing artifacts. Advice receives a short recommendation and an offer of a relevant build. An explicit build request proceeds once essential missing facts are resolved. It asks one question at a time, reuses answers already supplied and stops when further answers would not change the deliverable. Unknown information remains unknown rather than receiving invented values.

Use it for a GDPR or CCPA control register, data privacy inventory or data protection tracker. It fits scale-stage businesses documenting a process previously kept in spreadsheets, documents or inboxes.

It produces empty templates rather than enforcing compliance or operating privacy controls. Compliance obligations require a qualified reviewer; the skill does not provide legal advice. The skill does not calculate payroll, tax, leave balances or KPIs. Automation, reminders and synchronization require an integration layer. Legal, tax and HR review is required before templates drive real decisions.

Inputs cover data categories, employee or customer scope, affected people, purposes, consent, special categories, access, retention, deletion processes, existing notices and breach history. The requested result can be an inventory, a control list or both. Fields record control, data category, module, legal basis, retention period, access roles, encryption, consent requirement, owner, last review, status and control ID. Category options include personal, sensitive, financial, health, biometric and location data. Control statuses span draft, review, approval, implementation and retirement.

Requested artifacts can include CSV, SQL DDL, JSON Schema, Notion property mappings or an Excel workbook. The formats derive from one field list: CSV and JSON retain matching display names, while SQL uses normalized identifiers. Dates retain date types, money retains currency types and cross-table references retain relation types. Select options are a starting set that can be renamed to match the business.

One SQL example is:

CREATE INDEX idx_data_privacy_controls_status ON data_privacy_controls (status);

Templates are empty by default. Example rows are produced only on request and remain obviously synthetic. Sensitive identifiers should not be repeated unnecessarily during a requested review. Machine-readable data stays separate from explanatory prose and any material validation failures. Excel-compatible CSV uses UTF-8 with a byte order mark and accompanying guidance about number, date and currency columns. An XLSX workbook is a separate requested output rather than a renamed CSV.

The Notion output routes to notion-manual-import for CSV, property mappings, import steps and verification. Manual mappings require no connection. Requested live workspace changes require actual tool access, a verified target and authorization. Imported columns may initially be Text and need their mapped property types applied. Relation links resolve after both relevant databases are imported. A static mapping alone does not establish a completed workspace change.