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

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.

In addition to complying with data protection laws, understanding the intricacies of these regulations enhances your ability to create a robust security framework. Dive into the specifics of GDPR, CCPA, HIPAA, or PCI DSS, not just as regulatory hurdles but as guiding principles. These regulations outline principles such as data minimization, consent, transparency, and breach notification. Implementing data security measures becomes more meaningful when seen as an ethical responsibility and a commitment to safeguarding individual rights, fostering a more profound connection with both legal and ethical aspects. This collaborative approach ensures compliance and enriches your data engineering strategy.

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.

But before you implement Data protection tools and frameworks, it is important to have a data classification and labeling system in place that categorizes the data based on its sensitivity, confidentiality, and criticality. This includes defining the data classification levels, assigning the data labels, and implementing the data access and handling policies. By having a data classification and labeling system in place, you can ensure that the data is managed and protected according to its level of sensitivity and confidentiality. Moreover, it fosters a culture of awareness within the organization, empowering employees to understand and prioritize data sensitivity.

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.

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!

Recognize that data security is not a static achievement but a dynamic, ever-evolving process. Privacy policies should be reviewed at least once a year to ensure that they reflect current data security and privacy practices. Special attention should be given when launching new products, updating services, or adopting novel data usage methods. Additionally, user testing proves invaluable in maintaining up-to-date practices. Gathering feedback through user testing aids in refining privacy information, pinpointing potential issues or gaps in data security, and enhancing the overall delivery of privacy information. Combining policy reviews and user testing, this dual approach ensures a robust and adaptable data security and privacy framework.

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.