SOP ID: SOP-1303-01-AI_AI-Data-Protection-and-Privacy
Title: AI Data Protection & Privacy
Version: 1.0
Effective Date: (Enter Date)
Previous Version: None (New SOP)
Reason for Update: Initial Release
Owner: Data Privacy Office / AI Governance Office
Approver: (Name/Title)
1. Purpose
This SOP defines the processes, responsibilities, and controls required to ensure proper data protection and privacy practices when handling data for AI systems governed under the AI-IRB (Institutional Review Board) or other regulatory frameworks. It aims to align with relevant data protection laws and regulations (e.g., GDPR, HIPAA) and uphold high ethical standards in the use of personal or sensitive data within AI initiatives.
2. Scope
This SOP applies to all AI-related projects or systems that utilize sensitive, personal, or proprietary data within the organization’s AI-SDLC (Software Development Life Cycle) pipeline. It covers data handling steps from acquisition through decommission, including but not limited to:
Data collection and ingestion
Data storage and encryption
Data anonymization or pseudonymization
Data usage, sharing, and access
Data retention, archival, and destruction
3. Roles and Responsibilities
| Role | Responsibility |
|---|---|
| Data Privacy Office (DPO) | Owns the data protection policies, ensures alignment with regulatory requirements, and oversees compliance with data privacy laws. |
| AI Governance Office (AIGO) | Facilitates governance processes, reviews data protection plans, and collaborates with the AI-IRB to ensure compliance with organizational policies. |
| AI-IRB | Provides review and approval of data usage in AI projects, focusing on ethical implications and compliance with legal and regulatory frameworks. |
| Project Sponsor | Provides overall project funding and ensures that data privacy requirements are resourced and prioritized. |
| Data Custodian | Maintains primary responsibility for the security, retention, and confidentiality of specific datasets within AI projects. |
| Data Scientist/Engineer | Implements privacy controls (e.g., anonymization methods), ensures data usage aligns with the approved protocols, and logs all relevant data transformations. |
| Quality Assurance (QA) | Verifies and audits data protection measures, ensures that all required controls are tested and documented. |
| Operations (OPS) | Manages infrastructure where data is stored and processed, enforces technical measures such as encryption at rest and in transit. |
| Technical Support (TS) | Provides user support on data access issues, ensures that data requests follow the privacy guidelines, and escalates to the DPO when needed. |
4. Definitions
Personal Data: Any information related to an identified or identifiable natural person.
Sensitive Data: Data requiring higher protection due to its confidential or protected nature (e.g., health data, financial data).
Data Minimization: Strategy to limit personal data collection to what is necessary for the defined purpose.
Anonymization: Process that irreversibly alters data so individuals can no longer be identified.
Pseudonymization: Replaces identifying data with artificial identifiers, but still possible to re-identify under certain conditions.
5. Procedure Activities
5.1 Data Collection and Requirements Definition
Identify Data Requirements
Data Scientist/Engineer works with Project Sponsor to determine minimal data needed.
Data Custodian provides data inventory and classification (personal, sensitive, etc.).
AI Governance Office (AIGO) ensures alignment with policy requirements and checks for AI-IRB compliance triggers.
AI-IRB Review
Data Privacy Office (DPO) notifies the AI-IRB if personal or sensitive data is included in scope.
AI-IRB reviews use case and data attributes.
If AI-IRB approval is required, project data usage must not proceed until formal approval is granted.
5.2 Data Ingestion and Storage
Access Approvals
Data Custodian verifies request authenticity and grants access according to the principle of least privilege.
Operations sets up secure environment with encryption at rest.
QA ensures correct permissions are in place.
Anonymization/Pseudonymization
Data Scientist/Engineer applies pre-approved anonymization or pseudonymization techniques.
Data Privacy Office (DPO) verifies that transformations meet the defined anonymization guidelines.
Encryption and Protection
Operations ensures all data transmissions are encrypted in transit (e.g., TLS/SSL).
Data Custodian monitors logs to confirm data usage matches approved protocols.
5.3 Data Processing for AI
Privacy Impact Assessment
Data Scientist/Engineer completes privacy impact checklist before running AI training or analysis.
AI-IRB or AIGO reviews if changes in data usage occur.
Monitoring and Logging
Operations implements logging for data access.
QA audits logs monthly or upon special request to ensure compliance.
5.4 Data Sharing and Access Control
Data Access Requests
Technical Support receives user or team requests for data access.
Data Custodian approves or denies requests based on established rules.
If complex or new request, forward to AI-IRB for approval.
Third-Party Data Sharing
Data Privacy Office (DPO) ensures that all third-party data sharing meets relevant contractual and regulatory compliance.
Operations sets up secure data transfer if approved.
5.5 Post-Processing Retention and Destruction
Retention Policies
Data Custodian confirms compliance with retention timelines as stated in the project’s Data Management Plan (DMP).
Operations archives data, ensuring encryption remains intact.
Secure Destruction
Operations performs secure wipe or cryptographic erase once data surpasses retention period or upon request from Data Privacy Office (DPO).
QA audits destruction logs for completeness.
5.6 Non-Compliance Handling
Incident Reporting
Any stakeholder discovering data mishandling must inform Data Privacy Office (DPO) immediately.
DPO logs incident and coordinates investigation with relevant parties.
Corrective Actions
Data Custodian and Data Scientist/Engineer assist in investigation, providing logs.
AIGO updates policies or procedures if a systemic issue is discovered.
6. Metrics
Data Access Violations: Number of unauthorized access attempts or misuse.
Retention Compliance: Percentage of data sets archived or destroyed on schedule.
Incident Response Time: Average time to investigate and resolve data privacy incidents.
Audit Finding Closure: Percentage of data protection audit findings closed within the target timeframe.
7. Records and Documentation
Data Collection Checklists
AI-IRB Approval Documentation
Encryption and Anonymization Logs
Data Retention and Destruction Logs
Incident/Non-Compliance Reports
8. Training Requirements
All personnel must complete annual data protection training.
Specialized training for handling sensitive datasets.
New hires or new roles that require data access must be trained before access is provisioned.
9. References
GDPR (General Data Protection Regulation)
HIPAA (Health Insurance Portability and Accountability Act)
ISO 27001 (Information Security Management)
NIST Privacy Framework
10. Revision History
| Version | Date | Description | Author | Approval |
|---|---|---|---|---|
| 1.0 | (Enter Date) | Initial release | (Name, Title) | (Name, Title) |
Approval Signatures
Owner (Data Privacy Office): Signature/Date
Approver (AI Governance Office): Signature/Date
AI-IRB Chair: Signature/Date
@startuml
title SOP-1303-01-AI: AI Data Protection & Privacy
participant “Data Privacy Office” as DPO
participant “AI Governance Office” as AIGO
participant “AI-IRB” as IRB
participant “Project Sponsor” as PS
participant “Data Custodian” as DC
participant “Data Scientist/Engineer” as DS
participant “Quality Assurance” as QA
participant “Operations” as OPS
participant “Technical Support” as TS
’ 1. Project scope and data usage plan
PS -> DPO: Provide project scope and data usage plan
DPO -> AIGO: Verify alignment with AI data policies
AIGO -> DPO: Confirms policy alignment
’ 2. Data classification and IRB check
DPO -> DC: Request data classification (personal, sensitive, etc.)
DC -> DPO: Provide data inventory and classification
alt Contains personal/sensitive data
DPO -> IRB: Submit data usage request
IRB -> DPO: [Approval or Conditional Approval]
else No personal/sensitive data
DPO -> DS: Inform that standard data controls apply
end
’ 3. Environment setup and data ingestion
DPO -> OPS: Ensure environment is secure (encryption, access control)
OPS -> DC: Confirm that environment meets requirements
DC -> DS: Provide approved data subsets for ingestion
’ 4. Anonymization/Pseudonymization
DS -> DPO: Request guidance on anonymization technique
DPO -> DS: Provide approved anonymization/pseudonymization method
DS -> DS: Apply anonymization and log transformations
’ 5. QA checks
DS -> QA: Submit evidence of data controls
QA -> DS: Verify data usage logs, encryption, anonymization
QA -> DPO: Audit data protection compliance result
’ 6. Data usage and retention
DS -> DS: Execute AI training and analysis
TS -> DC: Request data access (if needed)
DC -> TS: Approve/deny data requests based on policy
DS -> DC: Conclude project, request data archival or destruction
’ 7. Retention or destruction
alt Data must be retained
DC -> OPS: Archive data securely (encrypted)
QA -> DC: Confirm retention logs
else End of retention period
DC -> OPS: Perform secure destruction of data
QA -> DC: Audit destruction logs
end
’ 8. Incident or non-compliance
participant “Any Stakeholder” as ANY
ANY -> DPO: Report suspected data mishandling
DPO -> DS: Request logs and investigation
DPO -> AIGO: Initiate corrective actions if needed
@enduml
Short textual explanation:
This sequence diagram outlines the steps involved in AI data protection and privacy under SOP-1303-01-AI. It begins with the Project Sponsor providing the data usage plan and the Data Privacy Office (DPO) checking policy alignment. If personal or sensitive data is identified, the AI-IRB is consulted for approval. Operations and the Data Custodian set up a secure environment, and anonymization/pseudonymization steps are applied. Quality Assurance (QA) verifies compliance, and data is either retained or destroyed per policy. Any stakeholder can report data mishandling, prompting DPO investigation and potential escalation to the AI Governance Office.