Data Security & Privacy¶

Overview¶

Data Security & Privacy ensures that Nissan North America’s (NNA) data is protected from unauthorized access, breaches, and misuse, while complying with relevant regulations and internal policies.

This section establishes policies, controls, and governance practices to safeguard enterprise data, particularly sensitive and regulated information.

Purpose¶

Protect enterprise data against unauthorized access, loss, or corruption.
Ensure compliance with privacy regulations (e.g., GDPR, CCPA, HIPAA) and contractual obligations.
Safeguard sensitive, confidential, and regulated data across its lifecycle.
Provide guidance for role-based access, encryption, monitoring, and incident response.

Key Principles¶

Principle	Description
Confidentiality	Only authorized users have access to data based on classification and role.
Integrity	Ensure data is accurate, complete, and unaltered unless approved changes are logged.
Availability	Data is accessible to authorized users when needed.
Privacy by Design	Embed privacy controls and minimization practices into systems and processes.
Auditability	Maintain logs and monitoring to support audits and compliance reporting.

Security Controls & Practices¶

Control Area	Guidelines / Examples
Access Management	Role-based access control (RBAC), least privilege principle, multi-factor authentication
Encryption	Encrypt sensitive data at rest and in transit using enterprise-standard algorithms
Monitoring & Logging	Capture access logs, changes, and anomalies for audit and forensic analysis
Data Masking / Tokenization	Mask sensitive fields in non-production environments; tokenize PII/PHI
Network & System Security	Firewalls, intrusion detection/prevention systems, secure API endpoints
Incident Response	Defined processes for detecting, reporting, and mitigating security breaches

Privacy Practices¶

Data Minimization: Only collect and store data necessary for business operations.
Consent & Regulatory Compliance: Ensure proper consent is captured and maintained where required.
Anonymization / Pseudonymization: Apply to sensitive or regulated datasets for analytics or testing.
Cross-border Data Transfers: Follow legal guidelines for international data flows.
Privacy Impact Assessments (PIAs): Conduct assessments for new systems or processes handling sensitive data.

Integration with Classification & MDM¶

Classification Alignment: Security controls vary by data classification (Public, Internal, Confidential, Sensitive/Restricted).
MDM Integration: Apply consistent access controls, masking, and auditing for master data domains.
Lineage & Provenance: Monitor who accesses or modifies sensitive data and track its flow across systems.

Roles & Responsibilities¶

Role	Responsibility
Data Owner	Approves security and privacy policies for their domain; authorizes access requests
Data Steward	Ensures compliance with security rules, monitors access, and implements controls
IT / Security Teams	Enforce technical controls, encryption, monitoring, and incident response
Governance Council	Reviews security policies, approves exceptions, oversees compliance audits

Tools & Technologies¶

Identity & Access Management (IAM): Okta, Azure AD, or equivalent
Encryption & Tokenization Tools: Enterprise encryption, HSMs, field-level masking
Security Monitoring & SIEM: Splunk, QRadar, or equivalent for logging and alerts
Privacy Compliance Tools: Consent management platforms, GDPR/CCPA compliance frameworks

Visual Representation¶

flowchart TD
    A[Data Assets] --> B[Classification Levels]
    B --> C[Access Controls]
    B --> D[Encryption & Masking]
    C --> E[Monitoring & Logging]
    D --> E
    E --> F[Incident Response & Audit]