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How can you design data privacy frameworks for emerging technologies?

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Data privacy is a crucial aspect of data architecture, especially when dealing with emerging technologies such as cloud computing, artificial intelligence, and the internet of things. These technologies pose new challenges and opportunities for data protection, as they involve collecting, processing, and sharing large volumes of personal and sensitive data across different platforms, devices, and networks. How can you design data privacy frameworks that address these challenges and ensure compliance with relevant laws and regulations? Here are some key steps to consider.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3

1 Assess the data landscape

The first step is to understand the data landscape of your organization and the emerging technologies you are using or planning to use. This includes identifying the types, sources, locations, and flows of data, as well as the stakeholders, roles, and responsibilities involved in data governance and management. You should also map out the data lifecycle, from collection to deletion, and the potential risks and impacts of data breaches, misuse, or loss. This will help you define the scope and objectives of your data privacy framework, as well as the legal and ethical requirements you need to comply with.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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in my knowledge, All data privacy framework must get basic privacy knowledge about your data from process, business goals as well users needs and rights (internal, external and thrid entities) to work and can be to use in your tools. Define a process to get these information about your data , document it, get available for using to integrate it in emerging technology tool as possible: you will get agile and efficient work in data privacy work at scale. So you will need to know about data classification, privacy review, data lifecycle and consent management as minimum

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2 Establish the data principles

The next step is to establish the data principles that will guide your data privacy framework. These are the high-level values and goals that reflect your organization's vision, mission, and culture, as well as the expectations and rights of your data subjects. Data principles can cover topics such as data minimization, data quality, data security, data transparency, data accountability, and data consent. You should align your data principles with the best practices and standards of your industry, as well as the applicable data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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in my knowledge; some basic data privacy principles can help are: - Always prepare your process and tools to manage consents users, data deletion and data subject access request. - Retain only data will you need to use. - Always prepared for technical privacy reviews. - Register all activities related with privacy from your tool in detailed. - All data need to be secure. The emerging technologies can be use to implement some of said principles from technical; like Homomorphic encryption; or define new technical patterns, practices and approches in data architecture; edge computing case

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3 Design the data policies

The third step is to design the data policies that will implement your data principles in practice. These are the specific rules and procedures that define how your organization will collect, process, store, share, and delete data, as well as how you will handle data requests, complaints, and incidents. Data policies should be clear, concise, and consistent, and cover all aspects of your data landscape and emerging technologies. You should also document the roles and responsibilities of your data privacy team, such as the data protection officer (DPO), the data stewards, and the data owners.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
(已编辑)
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A data architecture need to support following topics from privacy view: - Manage user consents process in storage and processing - Data pipeline for data subject access request and data deletion - Data modeling with privacy strategy - Monitoring for privacy review activities - Integration with Data inventory and sharing - Processing for encryptied data - Mechanism for secury data transfering from architecture to user - Set up a secure level in your resources you need to understand how emerging technologies affect to data architecture in some said topics; Homomorphic encryption for data pipeline; or how data architecture approachs this topics to emerging technologies cases; consent management in IoT case.

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4 Deploy the data controls

The fourth step is to deploy the data controls that will enforce your data policies and ensure data quality and security. These are the technical and organizational measures that prevent, detect, and respond to data breaches, misuse, or loss, as well as ensure data accuracy, completeness, and timeliness. Data controls can include encryption, anonymization, pseudonymization, access management, audit logs, backup and recovery, monitoring and alerting, and testing and validation. You should also use data privacy by design and by default approaches, which integrate data protection into every stage of your data architecture and emerging technologies development.

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5 Monitor and review the data performance

The final step is to monitor and review the data performance of your data privacy framework. This involves measuring and evaluating the effectiveness and efficiency of your data policies and controls, as well as the satisfaction and trust of your data subjects and stakeholders. You should use data privacy indicators and metrics, such as data breach frequency, data quality score, data request response time, and data subject feedback, to track and report your data privacy performance. You should also conduct regular data privacy audits and assessments, as well as update and improve your data privacy framework according to the changing data landscape and emerging technologies.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you design data privacy frameworks for emerging technologies?

1

2

3

4

5

6

1 Assess the data landscape

2 Establish the data principles

3 Design the data policies

4 Deploy the data controls

5 Monitor and review the data performance

6 Here’s what else to consider

Data Architecture

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How can you design data privacy frameworks for emerging technologies?

1

2

3

4

5

6

1 Assess the data landscape

2 Establish the data principles

3 Design the data policies

4 Deploy the data controls

5 Monitor and review the data performance

6 Here’s what else to consider

Data Architecture

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