Image placeholderPrivacy, compliance and data-governance control register placeholder
Overview
Privacy-aware data governance for data classes, roles, retention, consent assumptions, export and deletion paths, and AI data-use boundaries.
Blackstone Digital does not replace legal counsel. The service creates the practical data-governance layer a digital system needs: data classes, purpose notes, access roles, retention expectations, consent assumptions, export paths, deletion evidence and review rules before sensitive records flow into automation or AI assistance.
What it includes
Data classification for customer, staff, operational, financial, audit-only and AI-eligible records
Role, permission, export and access-review logic for sensitive information
Retention, archive, deletion, anonymization and legal-hold assumption mapping
Consent, privacy-policy input and customer-facing data-use wording support
Governance notes for CRM, portals, dashboards, forms, documents and AI workflows
Risk caveats, review ownership and evidence requirements before launch or expansion
Business outcome
Data use is easier to explain before systems expand
Teams know which records can be viewed, exported, retained, deleted or automated
Sensitive fields are separated from ordinary operational reporting
AI, dashboards and integrations have clearer eligibility limits
Privacy and compliance questions surface before launch pressure
How the project works
A practical build path for this service
01
Map
Inventory the data types, systems, forms, exports, owners and sensitive workflows.
02
Define
Classify records by purpose, role, retention need, export risk and AI eligibility.
03
Build
Define access, review, deletion, retention, consent and evidence rules for the first scope.
04
Review
Connect the governance model to the CRM, portal, backend, dashboard or automation layer.
05
Improve
Hand over the data-governance register, review cadence and unresolved legal-review list.
Enterprise Runbook
Scope, controls and evidence before handover.
This runbook layer turns Privacy & Governance into an operating service, not only a
page description. It uses NIST CSF 2.0, OWASP SAMM and CISA Secure by Design as
structure references for governance, verification and recovery; NIST Privacy Framework
logic for data boundaries; and OpenAI-style structured outputs, tool contracts,
tracing/evals and human approval only behind backend controls.
01 Scope gate
Service boundary, owner register and first operating process are explicit.
02 Control gate
Access, data class, audit, approval and recovery controls are mapped before build expansion.
03 Evidence gate
Artifacts remain reviewable after handover, not only during the project.
04 AI / data gate
Automation or AI uses only approved data classes, backend controls and human review paths.
Service boundary / in scope
Practical privacy, data-governance and access-control operating rules
Record classes, retention assumptions, deletion paths and evidence needs
AI, reporting and automation eligibility boundaries
Explicitly out of scope
Formal legal advice, regulatory representation or compliance certification
Guarantees that past data handling was compliant
Autonomous processing of sensitive data without review ownership
Owner Register
Business OwnerData Protection OwnerTechnical OwnerReview Owner
Evidence Pack
Data classification register
Access and export review map
Retention, archive and deletion schedule
Consent and privacy-input checklist
AI data eligibility and blocked-use list
Operating Cadence
30 days: review sensitive fields, exports and access assumptions
60 days: check retention, deletion and privacy wording gaps
90 days: approve governance changes before AI, dashboard or integration expansion
Control Matrix
Access, data class, audit, approval and recovery rules.
Access
Sensitive fields and exports limited by role and review need
Data class
Records classified by purpose, sensitivity, retention and AI eligibility
Audit event
Export, deletion, access change and sensitive automation events recorded
Approval rule
New data use, AI eligibility and retention changes require named approval
Recovery rule
Governance register and deletion evidence stay available after handover
Go
Data classes, owners, retention assumptions and sensitive-use limits are documented.
Hold
A data owner, legal-review item, deletion path or AI boundary is unresolved.
Reduce scope
Limit launch to non-sensitive records until governance evidence is ready.
Use cases
Where this service becomes useful
Customer Portals
Uploads, requests, documents and messages have access and retention rules.
CRM and Sales Records
Account, contact, pipeline and communication fields are classified before reuse.
AI Workflow Readiness
Only approved data classes can be summarized, searched or routed by AI assistance.
Compliance Cleanup
Old exports, shared folders and uncertain retention habits become visible.
The first step is choosing one process that should become clearer.
A review phase can sort the existing website, documents, forms, spreadsheets,
customer questions and workflows, then turn them into the first useful build.