How can you ensure data security and privacy when practicing data engineering?

Data engineering is the process of designing, building, and maintaining data pipelines, systems, and platforms that enable data analysis, processing, and visualization. As a data engineer, you are responsible for ensuring that the data you work with is secure and private, especially when it involves sensitive or personal information. In this article, we will discuss some of the best practices and tools that can help you achieve data security and privacy when practicing data engineering.

Find expert answers in this collaborative article

Selected by the community from 8 contributions. Learn more

1 Understand the data lifecycle

One of the first steps to ensure data security and privacy is to understand the data lifecycle, which consists of the stages of data creation, collection, storage, processing, analysis, dissemination, and disposal. Each stage poses different risks and challenges for data security and privacy, and requires different measures and controls to protect the data from unauthorized access, modification, or disclosure. For example, you may need to encrypt the data at rest and in transit, implement access control policies and roles, audit the data activities and logs, anonymize or mask the data before sharing or publishing, and delete the data securely when no longer needed.

Add your perspective

Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Report contribution
Get document all lifecycle for all data in data systems and software system: From raw data through standardized data to memory in data system components and data product system if can. This document will be a guidance to implement technical process to manage it when data pass each steps in lifecycle related with each data products, as well use in testing and technical privacy review process.

Like

2 Follow the data protection laws and regulations

Another important step to ensure data security and privacy is to follow the data protection laws and regulations that apply to your data sources, destinations, and domains. Depending on the nature and location of your data, you may need to comply with various frameworks and standards, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI DSS). These laws and regulations define the rights and obligations of data subjects, data controllers, and data processors, and specify the principles and requirements for data security and privacy, such as data minimization, consent, purpose limitation, transparency, accountability, and breach notification.

Add your perspective

Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Report contribution
Remember that compliance regulations can define context that your data engineering team need to know: - How ensure your resources are secures in data pipelines, - How modify your data loading steps based in processed data - View need to apply privacy methods in what kind of data. - Can apply DPIA in data pipeline or does not required? Talk with legal team, privacy enginering team and security team to know how acchive these topics.

Like

Load more contributions

3 Use data security and privacy tools and frameworks

A third step to ensure data security and privacy is to use data security and privacy tools and frameworks that can help you implement and automate the data protection measures and controls. There are many tools and frameworks available for different aspects and stages of data security and privacy, such as encryption, authentication, authorization, auditing, anonymization, masking, and deletion. Some examples are Apache Ranger, Apache Knox, Apache Atlas, Apache NiFi, Apache Kafka, Apache Spark, Apache Airflow, AWS KMS, AWS IAM, AWS CloudTrail, AWS Glue DataBrew, AWS Lake Formation, Google Cloud KMS, Google Cloud IAM, Google Cloud Audit Logs, Google Cloud Data Loss Prevention, Google Cloud Dataflow, Azure Key Vault, Azure Active Directory, Azure Monitor, Azure Purview, Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.

Add your perspective

Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Report contribution
Some tools need to integrate or use: - Data inventory: use to tag your ingested data to use in privacy processes based in guideline of data classification. - Identity and access management: define who can do what in group of authorized resources; and validate who can access to resources. - Vulnerability scanning: to detect possible vulnerabilities in your software.

Like

Load more contributions

4 Train and educate yourself and your team

A fourth step to ensure data security and privacy is to train and educate yourself and your team on the best practices and tools for data security and privacy. Data security and privacy is not only a technical issue, but also a human and organizational one. You and your team need to be aware of the data security and privacy risks and challenges, the data protection laws and regulations, the data security and privacy tools and frameworks, and the data security and privacy policies and procedures. You and your team also need to adopt a data security and privacy culture, which means that you value and respect the data security and privacy of yourself and others, and that you act accordingly and responsibly.

Add your perspective

Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Report contribution
If your organization have, works closely with the teams of privacy, legal and security teams: they will give knowledge and guideline about how your data pipelines need to work based in privacy and security standards and cases. Include them in your development processes, testing and reviews!

Like

5 Review and update your data security and privacy practices

A fifth step to ensure data security and privacy is to review and update your data security and privacy practices regularly and continuously. Data security and privacy is not a one-time or static task, but a dynamic and ongoing one. You need to monitor and evaluate your data security and privacy performance, identify and address any gaps or issues, and improve and optimize your data security and privacy processes and solutions. You also need to keep up with the changing data security and privacy landscape, such as the evolving data security and privacy threats, the emerging data security and privacy technologies, and the new data security and privacy regulations.

Add your perspective

Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Report contribution
In privacy, you need some documentations: - PIA or Privacy impact assessment: help to detect and minimise risk in new feature or project. - TIA or Transfer impact assessment: help to detect risk in transfer data one location to other location.

Like

Load more contributions

6 Seek feedback and collaboration

A sixth step to ensure data security and privacy is to seek feedback and collaboration from other data professionals, stakeholders, and experts. Data security and privacy is not a solo or isolated endeavor, but a collaborative and collective one. You need to communicate and coordinate with other data engineers, data analysts, data scientists, data managers, data owners, data users, data customers, and data regulators, and share your data security and privacy goals, challenges, solutions, and results. You also need to learn from and leverage the data security and privacy best practices, experiences, and insights of other data professionals, stakeholders, and experts, and contribute to the data security and privacy community and knowledge base.

Add your perspective

7 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?

Add your perspective

How can you ensure data security and privacy when practicing data engineering?

1

2

3

4

5

6

7

1 Understand the data lifecycle

2 Follow the data protection laws and regulations

3 Use data security and privacy tools and frameworks

4 Train and educate yourself and your team

5 Review and update your data security and privacy practices

6 Seek feedback and collaboration

7 Here’s what else to consider

Data Engineering

Rate this article

Thanks for your feedback

More articles on Data Engineering

More relevant reading

How can you ensure data security and privacy when practicing data engineering?

1

2

3

4

5

6

7

1 Understand the data lifecycle

2 Follow the data protection laws and regulations

3 Use data security and privacy tools and frameworks

4 Train and educate yourself and your team

5 Review and update your data security and privacy practices

6 Seek feedback and collaboration

7 Here’s what else to consider

Data Engineering

Rate this article

Thanks for your feedback

Explore Other Skills