When the EU’s General Data Protection Regulation (GDPR) took effect in 2018, it put Data Processing Agreements (DPAs) on the map. But before an organization can develop a Data Processing Agreement, it needs to put its data on a map.
Data mapping and data processing agreements are two distinct but closely related aspects of data protection and compliance. Data mapping refers to the process of identifying and documenting the flow of personal data within an organization, creating a comprehensive inventory of all the personal data collected, processed, stored or transmitted by an organization. Data mapping is a foundational requirement for compliance with data protection regulations.
Data processing agreements are legal contracts that govern the relationship between a data controller – the organization that collects and determines the purpose of personal data – and a data processor – an entity that processes personal data on behalf of the data controller. DPAs were first required under GDPR, and many privacy regulations introduced after the EU’s groundbreaking law also require them. Key provisions in a DPA include requirements for data security, confidentiality, data breach notification, and the rights and obligations of both organizations regarding personal data protection.
Data mapping serves as the foundation for creating effective data processing agreements. When an organization implements a comprehensive data mapping program, it gains a clear understanding of its data processing activities, which is necessary to draft DPAs. The information collected during data mapping, such as data processing activities and data storage locations, is used to define the scope and terms of DPAs, ensuring the agreements accurately reflect the organization’s data processing practices. Data mapping also can help identify potential risks and compliance gaps, which then can be addressed in the DPAs to ensure data processing is conducted in a compliant and secure manner. In addition, data processors often rely on the data mapping provided by data controllers to understand their roles and responsibilities in processing personal data, helping them meet their obligations under the DPAs.
In practice, the data mapping process typically comes before the data processing agreement is developed.
Data mapping is generally one of the first steps taken by an organization to achieve compliance with privacy and data protection regulations. This step is fundamental to gain a comprehensive understanding of an organization’s data processing activities. Only when an organization has a clear picture of its data processing activities can it then develop its data processing agreements.
The data controller needs the DPA to provide the data processor with instructions. The data processor should not process customer personal data without those instructions. Without these instructions, processing the data violates any privacy regulations requiring a DPA and both controller and processor would be accountable for the infractions.
While the GDPR is credited with introducing DPAs, many other data protection regulations require them in one form or another, including privacy laws in California, Colorado, Connecticut and Virginia in the United States, and the UK and Brazil overseas, among others. An organization’s DPAs should be reviewed and updated periodically to ensure they comply with all applicable laws, since non-compliance can result in penalties and fines.
The Relyance AI data privacy management platform includes a live data inventory and map module that quickly connects to code repositories, infrastructure tools and external vendor APIs. It automatically inventories data assets across internal APIs and third-party systems, and maps the topology of data flows and lineage. The platform’s Intelligent Insights module sends alerts about potential issues so privacy, data governance, and compliance teams can quickly prioritize action items. It detects missing vendor and data processing agreements, invalidated compliance frameworks in agreements, missing data categories in DPAs, broad DPA language and more. Through advanced machine learning technology, deep instrumentation, and the highest level of automation, customers can map their data in five hours, an effort that previously could take up to five months.
To learn more about the Relyance AI platform, book a demo here.
When the EU’s General Data Protection Regulation (GDPR) took effect in 2018, it put Data Processing Agreements (DPAs) on the map. But before an organization can develop a Data Processing Agreement, it needs to put its data on a map.
Data mapping and data processing agreements are two distinct but closely related aspects of data protection and compliance. Data mapping refers to the process of identifying and documenting the flow of personal data within an organization, creating a comprehensive inventory of all the personal data collected, processed, stored or transmitted by an organization. Data mapping is a foundational requirement for compliance with data protection regulations.
Data processing agreements are legal contracts that govern the relationship between a data controller – the organization that collects and determines the purpose of personal data – and a data processor – an entity that processes personal data on behalf of the data controller. DPAs were first required under GDPR, and many privacy regulations introduced after the EU’s groundbreaking law also require them. Key provisions in a DPA include requirements for data security, confidentiality, data breach notification, and the rights and obligations of both organizations regarding personal data protection.
Data mapping serves as the foundation for creating effective data processing agreements. When an organization implements a comprehensive data mapping program, it gains a clear understanding of its data processing activities, which is necessary to draft DPAs. The information collected during data mapping, such as data processing activities and data storage locations, is used to define the scope and terms of DPAs, ensuring the agreements accurately reflect the organization’s data processing practices. Data mapping also can help identify potential risks and compliance gaps, which then can be addressed in the DPAs to ensure data processing is conducted in a compliant and secure manner. In addition, data processors often rely on the data mapping provided by data controllers to understand their roles and responsibilities in processing personal data, helping them meet their obligations under the DPAs.
In practice, the data mapping process typically comes before the data processing agreement is developed.
Data mapping is generally one of the first steps taken by an organization to achieve compliance with privacy and data protection regulations. This step is fundamental to gain a comprehensive understanding of an organization’s data processing activities. Only when an organization has a clear picture of its data processing activities can it then develop its data processing agreements.
The data controller needs the DPA to provide the data processor with instructions. The data processor should not process customer personal data without those instructions. Without these instructions, processing the data violates any privacy regulations requiring a DPA and both controller and processor would be accountable for the infractions.
While the GDPR is credited with introducing DPAs, many other data protection regulations require them in one form or another, including privacy laws in California, Colorado, Connecticut and Virginia in the United States, and the UK and Brazil overseas, among others. An organization’s DPAs should be reviewed and updated periodically to ensure they comply with all applicable laws, since non-compliance can result in penalties and fines.
The Relyance AI data privacy management platform includes a live data inventory and map module that quickly connects to code repositories, infrastructure tools and external vendor APIs. It automatically inventories data assets across internal APIs and third-party systems, and maps the topology of data flows and lineage. The platform’s Intelligent Insights module sends alerts about potential issues so privacy, data governance, and compliance teams can quickly prioritize action items. It detects missing vendor and data processing agreements, invalidated compliance frameworks in agreements, missing data categories in DPAs, broad DPA language and more. Through advanced machine learning technology, deep instrumentation, and the highest level of automation, customers can map their data in five hours, an effort that previously could take up to five months.
To learn more about the Relyance AI platform, book a demo here.
Data mapping and data processing agreements are two distinct but closely related aspects of data protection and compliance. Data mapping refers to the process of identifying and documenting the flow of personal data within an organization, creating a comprehensive inventory of all the personal data collected, processed, stored or transmitted by an organization. Data mapping is a foundational requirement for compliance with data protection regulations.
Data processing agreements are legal contracts that govern the relationship between a data controller – the organization that collects and determines the purpose of personal data – and a data processor – an entity that processes personal data on behalf of the data controller. DPAs were first required under GDPR, and many privacy regulations introduced after the EU’s groundbreaking law also require them. Key provisions in a DPA include requirements for data security, confidentiality, data breach notification, and the rights and obligations of both organizations regarding personal data protection.
Data mapping serves as the foundation for creating effective data processing agreements. When an organization implements a comprehensive data mapping program, it gains a clear understanding of its data processing activities, which is necessary to draft DPAs. The information collected during data mapping, such as data processing activities and data storage locations, is used to define the scope and terms of DPAs, ensuring the agreements accurately reflect the organization’s data processing practices. Data mapping also can help identify potential risks and compliance gaps, which then can be addressed in the DPAs to ensure data processing is conducted in a compliant and secure manner. In addition, data processors often rely on the data mapping provided by data controllers to understand their roles and responsibilities in processing personal data, helping them meet their obligations under the DPAs.
In practice, the data mapping process typically comes before the data processing agreement is developed.
Data mapping is generally one of the first steps taken by an organization to achieve compliance with privacy and data protection regulations. This step is fundamental to gain a comprehensive understanding of an organization’s data processing activities. Only when an organization has a clear picture of its data processing activities can it then develop its data processing agreements.
The data controller needs the DPA to provide the data processor with instructions. The data processor should not process customer personal data without those instructions. Without these instructions, processing the data violates any privacy regulations requiring a DPA and both controller and processor would be accountable for the infractions.
While the GDPR is credited with introducing DPAs, many other data protection regulations require them in one form or another, including privacy laws in California, Colorado, Connecticut and Virginia in the United States, and the UK and Brazil overseas, among others. An organization’s DPAs should be reviewed and updated periodically to ensure they comply with all applicable laws, since non-compliance can result in penalties and fines.
The Relyance AI data privacy management platform includes a live data inventory and map module that quickly connects to code repositories, infrastructure tools and external vendor APIs. It automatically inventories data assets across internal APIs and third-party systems, and maps the topology of data flows and lineage. The platform’s Intelligent Insights module sends alerts about potential issues so privacy, data governance, and compliance teams can quickly prioritize action items. It detects missing vendor and data processing agreements, invalidated compliance frameworks in agreements, missing data categories in DPAs, broad DPA language and more. Through advanced machine learning technology, deep instrumentation, and the highest level of automation, customers can map their data in five hours, an effort that previously could take up to five months.
To learn more about the Relyance AI platform, book a demo here.
When the EU’s General Data Protection Regulation (GDPR) took effect in 2018, it put Data Processing Agreements (DPAs) on the map. But before an organization can develop a Data Processing Agreement, it needs to put its data on a map.
Data mapping and data processing agreements are two distinct but closely related aspects of data protection and compliance. Data mapping refers to the process of identifying and documenting the flow of personal data within an organization, creating a comprehensive inventory of all the personal data collected, processed, stored or transmitted by an organization. Data mapping is a foundational requirement for compliance with data protection regulations.
Data processing agreements are legal contracts that govern the relationship between a data controller – the organization that collects and determines the purpose of personal data – and a data processor – an entity that processes personal data on behalf of the data controller. DPAs were first required under GDPR, and many privacy regulations introduced after the EU’s groundbreaking law also require them. Key provisions in a DPA include requirements for data security, confidentiality, data breach notification, and the rights and obligations of both organizations regarding personal data protection.
Data mapping serves as the foundation for creating effective data processing agreements. When an organization implements a comprehensive data mapping program, it gains a clear understanding of its data processing activities, which is necessary to draft DPAs. The information collected during data mapping, such as data processing activities and data storage locations, is used to define the scope and terms of DPAs, ensuring the agreements accurately reflect the organization’s data processing practices. Data mapping also can help identify potential risks and compliance gaps, which then can be addressed in the DPAs to ensure data processing is conducted in a compliant and secure manner. In addition, data processors often rely on the data mapping provided by data controllers to understand their roles and responsibilities in processing personal data, helping them meet their obligations under the DPAs.
In practice, the data mapping process typically comes before the data processing agreement is developed.
Data mapping is generally one of the first steps taken by an organization to achieve compliance with privacy and data protection regulations. This step is fundamental to gain a comprehensive understanding of an organization’s data processing activities. Only when an organization has a clear picture of its data processing activities can it then develop its data processing agreements.
The data controller needs the DPA to provide the data processor with instructions. The data processor should not process customer personal data without those instructions. Without these instructions, processing the data violates any privacy regulations requiring a DPA and both controller and processor would be accountable for the infractions.
While the GDPR is credited with introducing DPAs, many other data protection regulations require them in one form or another, including privacy laws in California, Colorado, Connecticut and Virginia in the United States, and the UK and Brazil overseas, among others. An organization’s DPAs should be reviewed and updated periodically to ensure they comply with all applicable laws, since non-compliance can result in penalties and fines.
The Relyance AI data privacy management platform includes a live data inventory and map module that quickly connects to code repositories, infrastructure tools and external vendor APIs. It automatically inventories data assets across internal APIs and third-party systems, and maps the topology of data flows and lineage. The platform’s Intelligent Insights module sends alerts about potential issues so privacy, data governance, and compliance teams can quickly prioritize action items. It detects missing vendor and data processing agreements, invalidated compliance frameworks in agreements, missing data categories in DPAs, broad DPA language and more. Through advanced machine learning technology, deep instrumentation, and the highest level of automation, customers can map their data in five hours, an effort that previously could take up to five months.
To learn more about the Relyance AI platform, book a demo here.
When the EU’s General Data Protection Regulation (GDPR) took effect in 2018, it put Data Processing Agreements (DPAs) on the map. But before an organization can develop a Data Processing Agreement, it needs to put its data on a map.
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