What are the most effective ways to audit data privacy risks in computer engineering systems?

2 Data privacy framework

A data privacy framework is a set of principles, guidelines, and best practices that help define and implement data privacy policies and procedures. A data privacy framework can help structure and streamline the data privacy audit process, by providing a common language and a consistent approach. Some examples of data privacy frameworks are the ISO/IEC 27001, the NIST Privacy Framework, and the GDPR.

3 Data privacy risk assessment

A data privacy risk assessment is a core component of a data privacy audit, as it helps identify and evaluate the potential threats and impacts to data privacy in a computer engineering system. A data privacy risk assessment can be conducted using various methods and tools, such as data mapping, data inventory, data flow diagrams, privacy impact assessment, threat modeling, and risk matrices. A data privacy risk assessment should consider both technical and organizational factors, such as data types, data sources, data processing, data sharing, data retention, data security, data governance, and data rights.

4 Data privacy controls

Data privacy controls are the measures and mechanisms that are implemented to mitigate or prevent data privacy risks in a computer engineering system. Data privacy controls can be classified into three categories: preventive, detective, and corrective. Preventive controls aim to avoid or reduce data privacy risks before they occur, such as encryption, anonymization, access control, and consent management. Detective controls aim to monitor and detect data privacy risks when they occur, such as logging, auditing, alerting, and reporting. Corrective controls aim to respond and recover from data privacy risks after they occur, such as backup, restore, breach notification, and remediation.

5 Data privacy testing

Data privacy testing is the process of verifying and validating the effectiveness and performance of data privacy controls in a computer engineering system. Data privacy testing can be performed at different stages of the system development life cycle, such as design, implementation, deployment, and maintenance. Data privacy testing can involve various techniques and tools, such as code review, penetration testing, vulnerability scanning, compliance checking, and user feedback.

6 Data privacy review

A data privacy review is the process of evaluating and reporting the results and outcomes of a data privacy audit in a computer engineering system. A data privacy review can help communicate and document the findings and recommendations of the data privacy audit, as well as the actions and responsibilities for improvement. A data privacy review can also help monitor and measure the progress and performance of data privacy controls, and identify any new or emerging data privacy risks.

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?