ISO/IEC 29100:2024 - Information Technology — Privacy Techniques — Privacy Framework

ISO/IEC 29100 provides a privacy framework that establishes a common privacy terminology, defines the actors involved in processing personally identifiable information (PII), and describes privacy safeguarding considerations. It serves as a foundational standard for privacy engineering and management in information systems. The 2024 edition is the current version, replacing the original 2011 release.

Privacy Framework Components

The standard defines a comprehensive privacy framework consisting of key elements:

Component Description
Privacy Principles Fundamental privacy principles including consent, purpose limitation, collection limitation, data minimization, use limitation, data quality, openness, individual participation, and accountability.
Privacy Actors Identification of roles and responsibilities including data subject, PII controller, PII processor, and third parties in privacy-sensitive systems.
Privacy Safeguards Technical and organizational measures to protect PII throughout its lifecycle, including collection, processing, storage, and disposal.
Privacy Controls Systematic controls for privacy risk management, including preventive, detective, and corrective measures.
Privacy Risk Assessment Framework for identifying, analyzing, and evaluating privacy risks associated with PII processing activities.
Privacy Requirements Systematic approach to eliciting, analyzing, and specifying privacy requirements for information systems.

Quality Attributes Emphasized by the Standard

The standard directly addresses privacy protection through multiple quality dimensions:

Quality Attribute Relevance in ISO/IEC 29100
Privacy Core focus on protecting personal information and individual privacy rights through systematic framework and principles.
Data Protection Comprehensive approach to safeguarding personally identifiable information throughout its entire lifecycle.
Transparency Openness principle requiring clear communication about PII processing practices, purposes, and individual rights.
Accountability Organizations must demonstrate compliance with privacy principles and be accountable for PII protection measures.
Data Minimization Collection limitation and data minimization principles requiring only necessary PII to be processed.
Consent Management Framework for obtaining, managing, and respecting individual consent for PII processing activities.
Data Quality Ensuring PII accuracy, completeness, relevance, and currency throughout processing lifecycle.
Security Technical and organizational security measures to protect PII against unauthorized access, processing, and disclosure.
Auditability Systematic documentation and monitoring capabilities to demonstrate privacy compliance and accountability.
Interoperability Privacy framework compatibility across different systems, technologies, and jurisdictions for consistent PII protection.

Privacy Principles and Engineering

Core Privacy Principles

  • Consent: Obtaining appropriate consent for PII collection and processing
  • Purpose Limitation: Using PII only for specified, explicit, and legitimate purposes
  • Collection Limitation: Limiting PII collection to what is necessary and relevant
  • Data Minimization: Processing minimal PII necessary to achieve specified purposes
  • Use Limitation: Restricting PII use to authorized purposes and recipients

Individual Rights and Control

  • Individual Participation: Providing individuals with control over their PII
  • Openness: Transparency about PII processing practices and policies
  • Data Quality: Ensuring PII accuracy, completeness, and currency
  • Security Safeguards: Protecting PII through appropriate technical and organizational measures
  • Accountability: Demonstrating compliance with privacy principles and requirements

Privacy Risk Management

Risk Assessment Framework

  • Systematic identification of privacy risks and threats to PII
  • Analysis of potential privacy harm and impact on individuals
  • Evaluation of existing privacy controls and safeguards
  • Risk treatment strategies including prevention, mitigation, and response

Privacy Engineering Integration

  • Privacy by design principles embedded in system development lifecycle
  • Privacy impact assessment (PIA) processes for high-risk processing activities
  • Privacy-preserving technologies and techniques integration
  • Continuous monitoring and improvement of privacy controls

Implementation Considerations

Organizational Measures

  • Privacy governance structures and accountability frameworks
  • Privacy policy development and management processes
  • Staff training and awareness programs for privacy protection
  • Incident response procedures for privacy breaches and violations

Technical Measures

  • Privacy-enhancing technologies (PETs) implementation
  • Data anonymization and pseudonymization techniques
  • Access control and authentication systems for PII protection
  • Audit logging and monitoring systems for privacy compliance

References