Abstract

This paper examines the critical role of privacy policies in data management, focusing on the obligations of data controllers under legal frameworks such as the KSA PDPL. It highlights the necessity for controllers to provide clear and accessible privacy policies to data subjects, outlining the collection, processing, storage, and destruction of personal data. The paper discusses the essential components of a privacy policy, including the rights of data subjects and how they can exercise these rights, general activation steps, and the challenges faced in ensuring transparency and compliance.

Introduction

In an era of increasing data privacy concerns, organizations must prioritize transparency in their data handling practices. Article 12 mandates that data controllers create and public privacy policies that inform data subjects about their data collection practices. This requirement is crucial for building trust and ensuring compliance with data protection regulations.

Key Words

Privacy Policy; Data Collection; Data Protection; Data Controller; Data Subject Rights; Data Management; Compliance; KSA PDPL

KSA PDPL Article 12 Explanation

Article 12 requires data controllers to draft and share a privacy policy with individuals before collecting their personal data. This policy must explain why the data is being collected, what data will be collected, how it will be collected, processed, stored, and eventually destroyed. It should also inform data subjects about their rights and how to exercise them.

Key Points

• Purpose of Collection: Explain why the data is being collected.
• Personal Data: Specify what personal data will be collected.
• Means of Collection: Describe how the data will be collected.
• Processing and Storage: Outline how data will be processed and stored.
• Destruction: Detail how and when data will be destroyed.
• Data Subject Rights: Provide information on the rights of the data subjects and how they can exercise these rights.

Data Subject Rights and How to Exercise Them

Data subjects have several rights concerning their personal data, which should be clearly outlined in the privacy policy.

• Right to Access
  • Description: Data subjects can request access to their personal data held by the data controller.
  • How to Exercise: Submit a formal request to the data controller specifying the data to be accessed. The controller must respond within a specified timeframe (e.g., 30 days).

• Right to Rectification
  • Description: Data subjects can request corrections to inaccurate or incomplete data.
  • How to Exercise: Contact the data controller with details of the required corrections. The controller must update the data and inform the data subject.

• Right to Erasure (Right to be Forgotten)
  • Description: Data subjects can request the deletion of their personal data under certain circumstances.
  • How to Exercise: Submit a request for erasure to the data controller, specifying the reasons for the request. The controller must assess the request and delete the data if appropriate.

• Right to Restrict Processing
  • Description: Data subjects can request that their data be restricted from processing in certain situations.
  • How to Exercise: Submit a request to the data controller to restrict processing, explaining the grounds for the restriction. The controller must comply if the grounds are valid.

• Right to Data Portability
  • Description: Data subjects can request and/or allow their data to be transferred to another organization or directly to themselves in a structured, commonly used format.
  • How to Exercise: Make a request to the data controller specifying the data to be transferred and the preferred format. The controller must provide the data without undue delay.

• Right to Object
  • Description: Data subjects can object to the processing of their data for certain purposes, such as direct marketing.
  • How to Exercise: Submit an objection to the data controller, indicating the reasons for the objection. The controller must cease processing for the specified purposes.

• Rights Related to Automated Decision-Making
  • Description: Data subjects can contest decisions made solely based on automated processing that significantly affects them.
  • How to Exercise: Contact the data controller to challenge the automated decision and request human intervention or reconsideration.

Article 12 – General Activation Steps

• Policy Drafting: Develop a comprehensive privacy policy that includes all required elements.
• Review and Approval: Have the policy reviewed and approved by legal and compliance teams.
• Publication: Make the privacy policy accessible to data subjects through appropriate channels.
• Communication: Inform data subjects about the availability of the privacy policy and their rights.
• Process Implementation: Establish procedures to handle data subject requests and ensure compliance.
• Periodic Updates: Regularly review and update the privacy policy to reflect changes in data practices or legal requirements.

Use Cases

• E-commerce: An online retailer provides a privacy policy on its website detailing data collection practices related to customer purchases and outlines how customers can exercise their rights.
• Healthcare: A medical clinic shares a privacy policy explaining how patient information is collected, used, and protected, along with instructions for accessing and correcting their data.
• Social Media: A social media platform publishes a privacy policy outlining data collection for user profiles and interactions and provides a mechanism for users to manage their data preferences and rights.

Dependencies

• Legal Requirements: Compliance with data protection laws such as KSA PDPL or GDPR.
• Internal Policies: Alignment with organizational data management and privacy policies.
• Technology Infrastructure: Systems for data collection, processing, and storage must support policy implementation.

Tools/Technologies

• Document Management Systems: For storing and managing privacy policies.
• Compliance Software: To ensure adherence to data protection laws and manage data subject requests.
• Content Management Systems (CMS): For publishing and updating privacy policies on websites.

Challenges

• Complexity of Regulations: Navigating various data protection regulations can be complex and time-consuming.
• Communication Barriers: Ensuring that the privacy policy is accessible and understandable to all data subjects.
• Policy Maintenance: Regularly updating the privacy policy to reflect changes in data practices or legal requirements.
• Managing Requests: Efficiently handling data subject requests and ensuring timely responses can be challenging.

Conclusion

Privacy policies play a crucial role in ensuring transparency and trust between data controllers and data subjects. By clearly outlining data collection, processing, storage, and destruction practices, and informing data subjects of their rights and how to exercise them, organizations can enhance their compliance with data protection regulations and boost greater trust with their customers.

References

• Kingdom of Saudi Arabia Personal Data Protection Law (KSA PDPL).
• General Data Protection Regulation (GDPR).
• Privacy and Electronic Communications Regulations (PECR).
• Relevant academic literature and industry guidelines on data protection and privacy policies.

For Your Further Reading:

• Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond
• Data Strategy vs. Data Platform Strategy
• ABAC – Attribute-Based Access Control 
• Consequences of Personal Data Breaches
• KSA PDPL (Personal Data Protection Law) – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL Article 4, Article 5, Article 6, Article 7, Article 8, Article 9, & Article 10
• KSA PDPL Article 11
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• KSA NDMO – Classification Process, & Data Breach Management
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA PDPL – Article 12 – Data Collection Transparency – The Role of Privacy Policies in Data Management appeared first on MDM Team.

As organizations evolve and expand their IT infrastructure, especially within cloud environments and hybrid systems, traditional access control models like Role-Based Access Control (RBAC) often fall short in addressing modern security requirements. This paper delves into Attribute-Based Access Control (ABAC), a dynamic and flexible access control mechanism that makes decisions based on a combination of user, resource, and environmental attributes. ABAC provides a more granular and context-aware security model, allowing organizations to effectively manage access to sensitive data and systems in a dynamic environment. Through the integration of attributes, ABAC enhances security, compliance, and operational efficiency.

Keywords

Data Security; System Access, Attribute Based Access Control (ABAC); Discretionary Access Control (DAC); Mandatory Access Control (MAC); Role-Based Access Control (RBAC); Compliance; GDPR

Introduction

In the era of cloud computing, mobile devices, and remote workforces, protecting sensitive information and controlling access to resources are critical challenges for organizations. Traditional access control models such as Discretionary Access Control (DAC), Mandatory Access Control (MAC) and RBAC are often too rigid to meet the demands of modern IT ecosystems, which require more dynamic and scalable solutions.

To tackle these challenges, we will first define and compare Discretionary Access Control (DAC), Mandatory Access Control (MAC), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC). Following this, we will highlight how Attribute-Based Access Control (ABAC) offers a more flexible and sophisticated approach to access management by evaluating multiple attributes to make access control decisions. This paper outlines the key principles of ABAC, its advantages over traditional models, and its applicability in today’s complex IT environments. We will also explore real-world use cases that demonstrate how ABAC improves security while meeting compliance requirements like GDPR, PDPL, and NDMO guidelines.

Definition and Comparison of DAC, MAC, RBAC, and ABAC

Definitions

• Discretionary Access Control (DAC)
  • Definition: In DAC, the owner of a resource (like a file or a database) has the authority to determine who can access or modify that resource. Access permissions are granted based on the discretion of the resource owner.
  • Example: A user who owns a file can grant read or write permissions to other users at their discretion.

• Mandatory Access Control (MAC)
  • Definition: MAC is a strict access control model where access decisions are based on policies set by a central authority. Users cannot alter access permissions; they are determined by the system based on predefined policies.
  • Example: In a military environment, access to classified documents is controlled by security levels, and users with lower security clearances cannot access higher-level documents.

• Role-Based Access Control (RBAC)
  • Definition: RBAC assigns permissions based on roles within an organization. Each role has specific access rights, and users are assigned to roles based on their job responsibilities.
  • Example: In a company, a “Manager” role might have access to financial reports, while a “Staff” role has access only to their own work files.

• Attribute-Based Access Control (ABAC)
  • Definition: ABAC makes access decisions based on a combination of attributes related to the user, resource, and environment. Attributes might include user roles, resource types, time of access, and location.
  • Example: A user can access a document only if they are in the office, during business hours, and have the appropriate job role, as determined by the attributes set for the document and the user.

Comparison

• Flexibility
  • DAC: Highly flexible; resource owners set permissions as they see fit.
  • MAC: Less flexible; access is strictly governed by centralized policies.
  • RBAC: Moderately flexible; permissions are tied to roles, which can be adjusted as needed.
  • ABAC: Highly flexible; access decisions are based on a range of attributes and conditions.
• Complexity
  • DAC: Simpler to implement but can become complex with many users and resources.
  • MAC: More complex due to centralized policy management and rigid enforcement.
  • RBAC: Moderately complex; easier to manage with well-defined roles.
  • ABAC: Most complex; requires managing and evaluating multiple attributes and conditions.
• Security
  • DAC: Security can be compromised if users grant excessive permissions.
  • MAC: High security due to strict control and centralized policy enforcement.
  • RBAC: Good security within defined roles, but can be limiting if roles are not well-defined.
  • ABAC: High security with granular control based on a wide range of attributes.
• Use Cases
  • DAC: Suitable for environments where resource owners need control over access, such as personal or small business systems.
  • MAC: Ideal for highly secure environments like military or government where strict access control is required.
  • RBAC: Effective for organizations with well-defined roles and responsibilities, such as corporate or institutional settings.
  • ABAC: Best for dynamic and complex environments where access decisions need to account for various conditions, such as cloud services and large enterprises.

Core Concepts of ABAC

• Attributes as the Foundation: In ABAC, access control decisions are made based on the evaluation of attributes associated with users, resources, and environments:
  • User Attributes: These can include user roles, job functions, security clearances, or specific characteristics like location or department.
  • Resource Attributes: These define characteristics of the data or system being accessed, such as classification level (e.g., confidential, public), ownership, or file type.
  • Environmental Attributes: Dynamic factors such as the time of day, network security, user device, or geographical location during access.
• Policy-Based Decision Making: ABAC policies evaluate attributes to determine whether access should be granted. These policies follow conditional logic structures, such as:
  • If the user has the role of a ‘Manager,’ the data is labeled ‘Confidential,’ and the access request is made during working hours, then allow access.
• Real-Time Contextual Evaluation: ABAC enables real-time decisions based on changing attributes. For instance, access can be restricted if a user attempts to access sensitive data from an untrusted network or outside of working hours, even if they otherwise have the correct user privileges.

Why ABAC is Superior to Traditional Models

• Granularity of Control: Unlike RBAC, which makes access decisions based solely on predefined roles, ABAC enables fine-grained control by evaluating multiple factors. This allows organizations to define more specific policies that better align with their security and compliance needs.
• Scalability for Complex Environments: As organizations grow, so do the complexities of managing access controls. ABAC eliminates the need to create and manage an overwhelming number of roles by leveraging dynamic attributes, making it ideal for large enterprises and cloud environments.
• Context-Aware Access: ABAC’s ability to incorporate environmental and situational attributes into access decisions provides a more adaptive and secure approach, making it especially useful in hybrid and cloud-based systems.
• Enhanced Regulatory Compliance: Industries bound by data protection laws, such as the GDPR, PDPL, and NDMO, benefit from ABAC’s ability to enforce specific compliance-driven policies. Access can be restricted based on location (e.g., within the KSA) or device security status, ensuring adherence to local data privacy laws.

Implementing ABAC in a Modern IT Environment

• Identify Relevant Attributes: Start by determining the key attributes that will drive access decisions in your organization. These include user-specific data (e.g., department, security level), resource attributes (e.g., classification, ownership), and environmental data (e.g., time, location).
• Develop Granular Access Policies: Create detailed policies that dictate how attributes should be evaluated. These policies should be aligned with organizational security standards and compliance regulations. For example:
  • If a user has a security clearance of ‘Top Secret’ and is accessing data classified as ‘Confidential’ from a secure device, grant access.
• Deploy the ABAC Infrastructure: Implement Policy Decision Points (PDPs) that evaluate policies and Policy Enforcement Points (PEPs) that enforce access decisions. PDPs review attributes and decide on access rights, while PEPs apply those decisions in real-time.
• Real-Time Attribute Management: Ensure that attributes are consistently updated and accurate. User attributes should be sourced from identity management systems, while resource and environmental data can be dynamically updated based on data classification systems or contextual sensors (e.g., network security).
• Monitoring and Auditing: Implement comprehensive monitoring to track access decisions and identify potential issues or policy violations. Regular auditing ensures that ABAC policies remain effective and compliant with regulatory standards.

Use Cases for ABAC

• Healthcare: In healthcare, ABAC can be used to restrict access to patient records based on a combination of user roles, patient consent, and location. For example, only doctors with the correct credentials and who are within the hospital premises can access sensitive medical data.
• Financial Services: ABAC enables financial institutions to enforce fine-grained access to sensitive financial data. For example, only employees from specific departments can access confidential data during business hours, and access may be further restricted based on the security of the employee’s device.
• Government Agencies: Government institutions dealing with classified information can use ABAC to control access based on user clearance levels, data classification, and current location (e.g., inside a secure facility).
• Cloud and Hybrid Systems: Cloud providers can use ABAC to control access to resources based on attributes like IP address, user authentication level, or device status, improving security in hybrid environments where users may be accessing systems remotely.

Challenges in ABAC Implementation

• Complex Policy Management: While ABAC offers significant flexibility, the complexity of defining and maintaining attribute-based policies can become overwhelming in large organizations. To manage this complexity, organizations need to adopt clear governance processes.
• Performance Concerns: Evaluating multiple attributes and policies in real-time can introduce performance overhead. Optimizing the ABAC system to balance security with system performance is crucial for smooth operations.
• Attribute Integrity: For ABAC to function effectively, it is critical that all attributes (user, resource, and environmental) are accurate and up-to-date. An outdated or incorrect attribute could result in unauthorized access or prevent valid access attempts.

Conclusion

Attribute-Based Access Control (ABAC) is a modern, flexible approach to access control that enables organizations to meet the challenges of increasingly complex and dynamic IT environments. By evaluating multiple attributes in real time, ABAC allows for more granular and context-aware access control decisions, making it especially useful in cloud environments, regulated industries, and distributed systems.

As regulatory requirements and security threats continue to evolve, ABAC provides the adaptability needed to protect sensitive information while ensuring compliance with frameworks such as GDPR and PDPL. Organizations implementing ABAC can benefit from enhanced security, scalability, and regulatory compliance, ensuring that only the right individuals can access the right resources under the right conditions.

References

• NIST Special Publication 800-162: Guide to Attribute-Based Access Control (ABAC).
• European Union General Data Protection Regulation (GDPR).
• Personal Data Protection Law (PDPL).
• National Data Management Office (NDMO) Controls

For Your Further Reading:

• Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond
• Data Strategy vs. Data Platform Strategy
• Consequences of Personal Data Breaches
• KSA PDPL (Personal Data Protection Law) – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL Article 4, Article 5, Article 6, Article 7, Article 8, Article 9, & Article 10
• KSA PDPL Article 11
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• KSA NDMO – Classification Process, & Data Breach Management
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post Attribute-Based Access Control (ABAC) – A Modern Approach to Dynamic and Granular Security appeared first on MDM Team.

Abstract

In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), cloud computing, artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, and operational efficiency.

Introduction

The exponential growth of data generated by modern businesses, devices, and internet platforms has driven the need for scalable, efficient data management solutions. Big Data, characterized by large-scale, high-velocity, and diverse datasets, has emerged as the key driver of innovation across industries. This paper compares Big Data with several foundational technologies, highlighting how it differs in terms of scale, complexity, and application, and explores how these differences impact modern data-driven initiatives.

Big Data vs Traditional Data

Volume

• Big Data: Involves vast amounts of data, often measured in terabytes, petabytes, or even exabytes.
• Traditional Data: Limited to gigabytes or smaller, typically manageable within conventional databases like relational databases (RDBMS).

Variety

• Big Data: Includes structured, semi-structured, and unstructured data (e.g., social media posts, sensor data, logs).
• Traditional Data: Primarily structured data, like tabular data stored in rows and columns.

Velocity

• Big Data: Data generated at high speed, requiring real-time or near-real-time processing.
• Traditional Data: Slower data generation, often processed in batches or periodic updates.

Processing

• Big Data: Requires specialized frameworks (e.g., Hadoop, Spark) for distributed storage and processing.
• Traditional Data: Managed using SQL-based systems and relational database management systems (RDBMS).

Big Data vs Data Warehousing

Data Source

• Big Data: Can ingest and process all types of data from various sources, including social media, IoT devices, sensors, etc.
• Data Warehousing: Primarily handles structured data consolidated from various internal systems for reporting and analysis.

Storage

• Big Data: Utilizes distributed storage systems like Hadoop’s HDFS or cloud-based storage like AWS S3.
• Data Warehousing: Centralized storage, typically using specialized database systems (e.g., Oracle, SQL Server) optimized for reporting.

Processing Model

• Big Data: Batch and real-time processing (e.g., MapReduce, Spark Streaming).
• Data Warehousing: Primarily batch processing, optimized for querying and reporting.

Tools

• Big Data: Apache Hadoop, Apache Spark, NoSQL databases (e.g., Cassandra, MongoDB).
• Data Warehousing: ETL tools (e.g., Informatica, Talend), and OLAP systems.

Big Data vs Business Intelligence (BI)

Focus

• Big Data: Focused on handling vast amounts of raw data and discovering insights through advanced analytics.
• Business Intelligence (BI): Focused on querying, reporting, and analyzing historical business data for decision-making.

Processing Methods

• Big Data: Utilizes advanced algorithms, machine learning models, and real-time processing.
• BI: Relies on structured data, traditional reporting, and dashboard generation.

Use Case

• Big Data: Suited for exploratory data analysis, predictive analytics, and machine learning applications.
• BI: Suited for descriptive analysis, generating reports, and supporting strategic decisions.

Big Data vs Cloud Computing

Purpose

• Big Data: Focused on managing and processing large data sets to derive insights and patterns.
• Cloud Computing: Refers to the on-demand availability of computing resources (servers, storage, applications) via the internet.

Relationship

• Big Data can leverage Cloud Computing for scalable storage and processing capabilities (e.g., AWS Big Data services, Google BigQuery).

Scalability

• Big Data: Requires highly scalable storage and processing frameworks.
• Cloud Computing: Provides flexible infrastructure and resources to host and process Big Data solutions.

Big Data vs Artificial Intelligence (AI)

Data Role

• Big Data: Provides vast amounts of data that can be used to train AI models.
• AI: Uses data from Big Data to develop intelligent systems capable of learning, decision-making, and problem-solving.

Objective

• Big Data: Focuses on storing, managing, and processing large volumes of data.
• AI: Focuses on using algorithms and models to make data-driven decisions or predictions.

Tools & Techniques

• Big Data: Hadoop, Spark, NoSQL databases.
• AI: Machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn.

Big Data vs Data Science

Goal

• Big Data: The technology and methods used to handle and process vast quantities of data.
• Data Science: The field that applies statistical methods, algorithms, and data analysis techniques to extract insights from data.

Skills

• Big Data: Requires skills in distributed systems, database management, and processing frameworks.
• Data Science: Requires skills in statistics, programming (e.g., Python, R), machine learning, and data visualization.

Tools

• Big Data: Hadoop, HDFS, Spark, Kafka.
• Data Science: Jupyter Notebooks, Python libraries (e.g., Pandas, NumPy), RStudio.

Big Data vs NoSQL

Data Structure

• Big Data: Refers to the broader concept of handling large-scale, diverse data.
• NoSQL: Refers to non-relational databases designed for horizontal scaling, often used in Big Data environments (e.g., MongoDB, Cassandra).

Purpose

• Big Data: Encompasses both storage and processing techniques.
• NoSQL: Primarily focused on storage, supporting unstructured and semi-structured data.

Conclusion

This comparative analysis demonstrates that Big Data is essential due to its vast scale, complex nature, and broad applications across various sectors. Its capacity to manage diverse data types and deliver real-time insights makes it crucial for contemporary businesses aiming to leverage data for strategic advantage. By integrating Big Data with technologies such as data warehousing, business intelligence, cloud computing, AI, and data science, organizations can significantly enhance their analytical capabilities and operational efficiency.

References

• DAMA International. (2017). Data Management Body of Knowledge (DMBoK).
• General information on Big Data technologies was obtained through various Google searches.
• Research was conducted through the study of various technical articles and whitepapers relevant to Big Data and its applications.

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• Consequences of Personal Data Breaches
• KSA PDPL (Personal Data Protection Law) – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL Article 4, Article 5, Article 6, Article 7, Article 8, Article 9, & Article 10
• KSA PDPL Article 11
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• KSA NDMO – Classification Process, & Data Breach Management
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond appeared first on MDM Team.

Key risks associated with personal data breaches

• Identity Theft
  • Personal Information Exposure: When personal data such as Social Security numbers, credit card details, or other sensitive information is leaked, it can be used to impersonate the data subject, leading to identity theft.
  • Financial Loss: Identity theft can result in fraudulent transactions, credit damage, and significant financial loss for the data subject.

• Privacy Violation
  • Personal and Sensitive Information: The exposure of personal data (e.g., medical records, private correspondence) compromises the data subject’s privacy. This can lead to unwanted attention, harassment, or psychological distress.
  • Reputational Damage: Public exposure of private information can harm the data subject’s personal and professional reputation.

• Fraud and Scams
  • Target for Scams: Data subjects whose information is leaked may become targets for phishing or other scams, which can lead to further financial loss or personal distress.
  • Unwanted Solicitations: Contact information like phone numbers or email addresses being leaked can result in unwanted marketing, spam, or other forms of tease.

• Emotional Distress
  • Stress and Anxiety: The knowledge that personal information has been exposed can cause significant emotional distress, anxiety, and fear about future misuse.
  • Loss of Control: The data subject may feel a loss of control over their personal information, which can be a distressing experience.

• Potential for Discrimination
  • Sensitive Data: Leaked information about health, sexual orientation, or other sensitive topics can lead to discrimination or stigmatization, affecting the data subject’s personal and professional life.
  • Employment and Insurance Issues: Leaked data might influence hiring decisions or affect insurance premiums, leading to potential adverse outcomes for the data subject.

• Legal and Financial Consequences
  • Legal Costs: Data subjects might incur legal costs if they need to take action to address the misuse of their information or to clear up any resultant issues.
  • Compensation Claims: They may face the burden of pursuing compensation or remediation for damages caused by the breach.

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, Article 8, & Article 9
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post The Far-Reaching Consequences of Personal Data Breaches appeared first on MDM Team.

Explanation

This article ensures that the collection and use of personal data are strictly related to the purpose for which the data is gathered. It emphasizes lawful methods, limits the amount of data collected to what is necessary, and mandates the destruction of data once it’s no longer required.

Key Points

• Purpose Alignment: Personal data must be collected for purposes that directly align with the Controller’s legitimate objectives and comply with the law.
• Lawful Methods: Collection methods must be clear, lawful, direct, and secure. Deception, misleading tactics, or extortion are prohibited.
• Data Minimization: Only collect the minimal amount of data necessary for the specific purpose. Avoid collecting data that could specifically identify a person unless absolutely required.
• Data Deletion: When the collected personal data is no longer necessary for the original purpose, the Controller must stop collecting it and destroy the data without undue delay.

General Activation Steps

• Define Purpose: Clearly outline the purpose for collecting personal data in alignment with business and legal needs.
• Choose Lawful Collection Methods: Ensure the means of data collection are appropriate, transparent, secure, and comply with legal provisions.
• Implement Data Minimization: Review the data being collected to ensure it is limited to what is essential for the defined purpose.
• Monitor Data Usage: Regularly assess whether collected data is still necessary. When the data has served its purpose, take immediate steps to securely destroy it.
• Establish Controls: Set up procedures and controls for ongoing monitoring and review of data collection practices to ensure compliance with the article.

Use Cases

• E-commerce: An online store collects customer data for order fulfillment. The store ensures the data is only used for this purpose, and unnecessary or expired customer information is securely deleted after the order is completed.
• Healthcare: A hospital collects patient data for treatment. Once treatment is completed, and the data is no longer necessary for ongoing care, the hospital must delete it to comply with privacy laws.

Dependencies

• Legal Compliance: Controllers need to ensure alignment with KSA PDPL and other necessary data protection laws.
• Data Governance Framework: Strong data governance policies must be in place to manage the collection, retention, and deletion of personal data.
• Security Practices: Secure methods for data collection, storage, and destruction are crucial for compliance.

Tools and Technologies

• Data Discovery Tools: For identifying personal data across systems (e.g., OneTrust, Varonis).
• Data Minimization Solutions: Implement software to ensure data collection practices align with purpose limitation (e.g., TrustArc, BigID).
• Encryption and Secure Deletion Tools: Use tools for securely storing and deleting data (e.g., VeraCrypt, Blancco).
• Audit and Monitoring Systems: Deploy continuous monitoring to ensure data collection and retention are compliant (e.g., AuditBoard, Splunk).

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL (Personal Data Protection Law) – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, Article 8, Article 9, & Article 10
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• KSA NDMO – Classification Process, & Data Breach Management
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA PDPL – Article 11 – Purpose Limitation and Data Minimization appeared first on MDM Team.

Explanation

The Data Breach Management and Response Process outlines how an organization should handle and address data breaches. It details the steps for reviewing, responding to, and correcting breaches while ensuring compliance with regulatory requirements. It ensures a structured and compliant response to data breaches, mitigating potential risks and safeguarding personal data.

Key Points

• Incident Review: The Data Controller must review the breach with the Regulatory Authority.
• Immediate Response: The Data Controller and/or Data Processor should quickly address the breach.
• Corrective Actions: Implement permanent fixes as directed by the Regulatory Authority.
• Testing: Verify the effectiveness of corrective actions to ensure data protection.

General Activation Steps

• Incident Detection: Identify and report the breach to the Data Controller.
• Initial Assessment: Assess the breach’s impact and notify the Regulatory Authority if required.
• Immediate Response: Implement short-term measures to contain and mitigate the breach.
• Corrective Actions: Develop and apply long-term solutions as specified by the Regulatory Authority.
• Testing: Conduct tests to ensure the implemented actions effectively protect personal data.
• Documentation: Maintain detailed records of the breach, response actions, and tests.

Use Cases

• Unauthorized Access: An employee’s credentials are used to access sensitive data without authorization.
• Data Exfiltration: Personal data is stolen or leaked due to a security vulnerability.
• System Compromise: A breach occurs due to a compromised system or software.

Dependencies

• Regulatory Guidelines: Compliance with regulations from the National Data Management Office (NDMO) and other relevant authorities.
• Incident Detection Tools: Systems for monitoring and detecting breaches (e.g., SIEM, IDS/IPS).
• Communication Channels: Methods for promptly notifying affected individuals and authorities.
• Corrective Action Tools: Solutions for addressing vulnerabilities and testing effectiveness.

Tools/Technologies

• Incident Response Software: For tracking and managing the breach response (e.g., Splunk, ServiceNow).
• Data Encryption: Tools for securing data and preventing unauthorized access (e.g., Symantec, McAfee).
• Vulnerability Assessment Tools: For identifying and addressing system vulnerabilities (e.g., Nessus, Qualys).
• Testing Frameworks: Tools for validating the effectiveness of corrective actions (e.g., penetration testing tools).

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL (Personal Data Protection Law) – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, Article 8, Article 9, & Article 10
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA NDMO – Personal Data Protection – Data Breach Management Process- PDP.3.2 P1 appeared first on MDM Team.

Explanation

Article 10 outlines the circumstances in which a Data Controller may collect or process personal data without direct consent or for purposes other than the originally stated ones. While the general rule is to collect personal data directly from the individual and process it only for specific purposes, certain exceptions allow flexibility. These include cases involving public interest, health, security, or legal obligations. Approach ensures compliance while allowing the Controller flexibility when handling personal data, particularly when consent isn’t feasible.

Key Points

• Direct Collection as a General Rule: Personal data should primarily be collected from the individual (Data Subject).
• Exceptions for Alternative Collection or Processing:
  • With Consent: The Data Subject agrees to alternate collection or processing.
  • Publicly Available Data: Data from public sources can be used.
  • Public Entities: Government bodies can collect or process data for public interest, security, or legal reasons.
  • Vital Interests: If not following this rule would harm the individual or impact their well-being.
  • Health and Safety: Necessary collection or processing for public health and safety or individual life protection.
  • Anonymized Data: Data that cannot directly or indirectly identify a person can be processed.
  • Legitimate Interests: Controllers may collect or process data to achieve legitimate goals, provided they do not violate the rights or interests of individuals.

General Activation Steps

• Assess Data Collection Source: Ensure data is collected directly from the individual unless one of the exceptions applies.
• Obtain Consent Where Necessary: If collecting from other sources or for new purposes, secure clear consent unless one of the exceptions allows bypassing this.
• Verify Legitimate Interests: In cases of legitimate interest, confirm that the processing doesn’t infringe on the rights or interests of the Data Subject, and ensure sensitive data isn’t processed.
• Ensure Public Health or Safety Justification: When processing for health or safety, document the necessity.
• Public Entity Actions: For government bodies, confirm that the processing aligns with public interest, legal, or security needs.
• Ensure Anonymization: If using anonymized data, verify that it cannot be traced back to an individual.
• Comply with Regulations: Adhere to additional regulatory procedures set by governing bodies regarding paragraphs (2) to (7).

Use Cases

• Public Health Campaigns: A government agency collects anonymized data from hospitals to monitor disease outbreaks without directly obtaining patient consent.
• Legal or Judicial Requirements: A law enforcement agency collects data from third-party sources for an ongoing investigation without needing direct consent.
• Legitimate Business Interests: A company uses publicly available customer data to tailor marketing campaigns, ensuring no sensitive data is processed and rights are respected.
• Public Safety Operations: Emergency services collect personal data in crisis situations, like natural disasters, to protect citizens’ health and safety.

Dependencies

• Data Subject’s Consent: Direct collection and processing may require clear consent unless falling under the exceptions.
• Public Availability of Data: Data must be verifiably public or from public sources in relevant cases.
• Legal and Regulatory Framework: Exceptions, especially for public entities, must align with legal mandates and judicial requirements.
• Legitimate Interest Balancing: Controllers must ensure the balance between legitimate interests and the data subject’s rights and interests.

Tools & Technologies

• Consent Management Platforms: Tools that track and manage consent from individuals (e.g., OneTrust, TrustArc).
• Data Anonymization Tools: Technologies that anonymize personal data, ensuring it cannot be traced back (e.g., IBM Data Privacy Passports, Oracle Data Masking).
• Public Data Collection Tools: Tools for gathering and processing publicly available information, including web scraping tools.
• Compliance Management Systems: Systems that ensure adherence to legal requirements and automate data processing policies (e.g., SAP GRC, RSA Archer).
• Data Protection Impact Assessment (DPIA) Tools: Helps assess the impact of data processing activities on data subject rights (e.g., DPIA template tools).

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, & Article 8, Article 9
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA PDPL – Article 10 – Purpose Limitation and Permissible Exceptions for Data Collection & Processing appeared first on MDM Team.

Explanation

In case an organization’s personal data is compromised (i.e., exposed, stolen, or leaked), the responsible party—either the Data Controller or Data Processor—must inform the Regulatory Authority. This notification must happen within a strict 72-hour window, in line with the requirements laid out by the National Data Management Office (NDMO) under their Personal Data Protection Regulations. This approach aligns with NDMO regulations and ensures compliance while safeguarding personal data.

Key Points

• Data Breach Definition: A breach occurs when unauthorized access, disclosure, or destruction of personal data happens.
• Data Controller/Processor Responsibility: The Data Controller or Processor managing personal data is responsible for notifying the appropriate authorities.
• 72-Hour Notification Window: Once the breach is discovered, the responsible entity has 72 hours to notify the Regulatory Authority.
• Contents of Notification: The breach notification should include details of the breach, the scope, the impact on individuals, and the corrective actions taken.
• Regulatory Compliance: The notification must meet the National Data Management Office (NDMO) Personal Data Protection Regulations’ specific requirements.
• Penalties for Non-Compliance: Failing to notify within the required timeframe may lead to fines and penalties under the regulatory framework.

General Activation Steps

• Identify the Breach: Detect and confirm the breach, evaluate its severity, and gather relevant details.
• Assess the Impact: Determine the scope and individuals affected by the breach (what data was compromised and how many data subjects are involved).
• Prepare the Notification:
  • Description of the breach (what happened, how it was detected).
  • Data Impacted: Detail of personal data affected.
  • Mitigation Measures: Steps being taken to address and prevent further impact.
  • Contact Details: Include details of the individual or team responsible for managing the breach.
• Submit the Notification to the Regulatory Authority within 72 hours.
• Notify Impacted Individuals (if applicable): After notifying the authority, inform the affected individuals, especially if the breach can cause harm (e.g., identity theft).
• Update Internal Documentation: Record the incident, corrective actions, and results for auditing and future references.

Use Cases

• Financial Institutions: A bank detects unauthorized access to customer accounts, compromising personal and financial information.
• Healthcare Providers: A hospital experiences a ransomware attack that exposes patient medical records.
• E-Commerce Companies: A data breach of customer profiles, including email and payment information, occurs due to a vulnerability in the system.

Dependencies

• Incident Response Team: Ensure an active and well-trained team to handle breaches swiftly.
• Data Governance Policies: An established data governance framework ensures the breach process is well-managed.
• Technology Monitoring Tools: Implement monitoring and detection tools to discover breaches early.
• Legal and Regulatory Advisors: Have access to legal guidance for compliance with specific regulations.
• Employee Training: Regular security awareness training is essential for early detection and proper handling of breaches.

Tools and Technologies

• Security Information and Event Management (SIEM): Tools like Splunk, IBM QRadar, or LogRhythm to detect and monitor breaches.
• Incident Management Platforms: JIRA, ServiceNow, or similar systems to track the breach and manage the response workflow.
• Encryption and Data Masking Tools: Tools such as Vormetric, Talend, or Oracle Advanced Security for mitigating the impact of data breaches.
• Endpoint Detection and Response (EDR): Tools like CrowdStrike, Microsoft Defender for Endpoint to detect and isolate breaches at endpoint levels.
• Notification Tools: Use compliance management platforms to assist with breach notifications, like OneTrust or TrustArc.

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, & Article 8, Article 9
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA NDMO – Personal Data Protection – Data Breach Notification – PDP.3.1 P2 appeared first on MDM Team.

Explanation

Article 9 of the KSA PDPL allows the Controller (the entity handling personal data) to set timeframes for when individuals (Data Subjects) can access their personal data. It also provides conditions under which the Controller may limit access, particularly when it’s necessary to prevent harm or when the Controller is a public entity dealing with security, legal, or judicial matters.

By defining clear timeframes and conditions for data access, the Controller ensures a balance between the Data Subject’s rights and legal, security, or harm-prevention concerns.

Key Points

• Timeframes for Access: The Controller can establish deadlines or schedules for responding to requests to access personal data.
• Access Limitations: The Controller can restrict access if:
  • It’s necessary to protect the Data Subject or others from harm.
  • The Controller is a public entity and access limitations are required for security, legal, or judicial reasons.
• Prevention of Access: The Controller can entirely prevent access under specific circumstances outlined in the regulations.

General Activation Steps

• Define Timeframes: Establish clear time limits for data access requests, ensuring these are compliant with regulatory guidelines.
• Set Criteria for Limitation: Identify situations where access should be limited to prevent harm or comply with security and legal requirements.
• Notify Data Subjects: Ensure Data Subjects are informed about timeframes and any potential limitations when requesting access to their personal data.
• Document Policies: Create a policy outlining how and when access may be restricted, with clear guidelines for employees handling these requests.

Use Cases

• Protection from Harm: If accessing personal data could lead to physical or emotional harm, the Controller can restrict access. For example, in cases involving domestic abuse, accessing certain data might endanger someone.
• Public Entity & Security: A government agency may deny access to certain personal data if releasing it poses a national security risk or violates other laws.
• Judicial Requirements: In an ongoing legal investigation, certain personal data might be restricted to preserve the integrity of the investigation.

Dependencies

• Regulatory Guidelines: The Controller must adhere to the detailed regulations provided under the law, which will clarify the conditions and timeframes for limiting access.
• Legal Compliance: Ensure that any access limitations comply with other national laws, especially when dealing with public security or judicial matters.
• Risk Assessment: A solid process must be in place to evaluate the risk of harm before restricting access to personal data.

Tools/Technologies

• Data Management Platforms: Implement systems to manage access requests efficiently (e.g., access tracking tools).
• Audit and Monitoring Tools: Use tools that log access to personal data, including restrictions, to ensure compliance.
• Security and Risk Management Tools: Deploy risk assessment and data classification tools to evaluate the potential harm before allowing or restricting data access.
• Legal and Compliance Solutions: Utilize tools that provide real-time updates on legal changes or security requirements that may affect data access.

For Your Further Reading:

• Data Strategy vs. Data Platform Strategy
• KSA PDPL – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA PDPL – Article 5, Article 6, Article 7, & Article 8
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post KSA PDPL – Article 9 – Data Access Timeframes and Limitations appeared first on MDM Team.

• Data Strategy: A comprehensive approach that aligns data management with business goals, focusing on governance, analytics, culture, and overall data management.
• Data Platform Strategy: A focused approach that deals with the technical aspects of data infrastructure, ensuring the right platforms, tools, and technologies are in place to support the data strategy.

The two strategies work together, with the Data Platform Strategy serving as the technological backbone to execute the broader Data Strategy.

Breakdown of their differences

Data Strategy

Data Strategy is a high-level plan that outlines how an organization will use data to achieve its business objectives. It encompasses the following key areas:

• Vision and Goals
  • Defines how data will support the organization’s overall mission and goals.
  • Establishes data as a strategic asset.

• Data Governance
  • Ensures data quality, security, compliance, and proper management across the organization.
  • Involves policies, roles, responsibilities, and standards.

• Data Management
  • Covers the processes of collecting, storing, processing, and maintaining data.
  • Focuses on data lifecycle management, including data integration, quality, and stewardship.

• Analytics and Insights
  • Defines how data will be used to generate insights, inform decision-making, and create value.
  • Includes data analytics, reporting, and data-driven innovation.

• Culture and Skills
• Promotes a data-driven culture within the organization.
• Addresses the need for data literacy and relevant skills development.

• Technology and Tools
  • Identifies the technologies and tools necessary to manage and analyze data.
  • Includes considerations for data architecture, storage solutions, and analytics platforms.

Data Platform Strategy

Data Platform Strategy is more specific and focuses on the technological infrastructure and tools needed to support the broader data strategy. It includes the following:

• Technology Architecture
  • Defines the technical foundation for data management, including databases, data lakes, data warehouses, and data integration tools.
  • Ensures the infrastructure supports scalability, performance, and security requirements.

• Platform Selection and Deployment
  • Involves choosing and implementing the right data platforms (e.g., cloud-based, on-premises, hybrid).
  • Considers factors like cost, performance, compatibility, and vendor support.

• Data Storage and Processing
  • Focuses on how data will be stored, accessed, and processed efficiently.
  • Includes decisions on data models, storage formats, and processing frameworks (e.g., batch vs. real-time).

• Integration and Interoperability
  • Ensures that different data systems and platforms can work together seamlessly.
  • Includes APIs, ETL processes, and data integration tools.

• Security and Compliance
  • Addresses the security measures and compliance requirements specific to the data platforms.
  • Involves encryption, access controls, and adherence to regulations like GDPR, KSA PDPL, etc.

• Monitoring and Optimization
  • Establishes monitoring tools and practices to ensure platform performance, reliability, and cost-effectiveness.
  • Includes continuous optimization to meet evolving business needs.

For Your Further Reading:

• KSA PDPL – Initial Framework
• KSA PDPL – Consent Not Mandatory
• KSA NDMO – Data Catalog and Metadata
• KSA NDMO – Personal Data Protection – Initial Assessment
• KSA NDMO – Classification Process – Data Classification Metadata
• KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
• KSA NDMO – Personal Data Protection – PDP Plan
• Enterprise Architecture Governance & TOGAF – Components
• Enterprise Architecture & Architecture Framework
• TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
• TOGAF – Architecture Content Framework
• TOGAF – ADM Features & Phases
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post Data Strategy vs. Data Platform Strategy appeared first on MDM Team.