How do you ensure data privacy during data quality assessment?

Only authorized personnel should have access to the data being assessed to ensure data privacy during data quality assessment. If possible, anonymize or pseudonymize the data before performing DQA (data quality assessment). If data cannot be anonymized or pseudonymized, encrypt it at rest and in transit. If data needs to be shared with external parties for DQA purposes, use a secure data sharing platform (drop box is NOT a secure place). Implement data retention policies to ensure that data is only retained for as long as necessary. Train staff on data privacy best practices, including how to handle and protect sensitive data. Audit data access and usage to identify any suspicious activity. Finally, be transparent, no black box!

Also if you are using Cloud provider to store said data, or using the cloud as intermediary please encrypt the data prior to it going to the cloud.

Implementation of ITGC compliance, controls related to data access which if not looked into may have catastrophic implications: Access to Database & Systems: If all employees have permission to create new user accounts, anyone can create a covert user account, and use it to monitor sensitive data. Inefficient Patch Management: Ineffective patch management could expose systems to known vulnerabilities. Attackers can then exploit these vulnerabilities to break into ERP systems, steal data, or delete valuable intellectual property. Data Integrity: Verify the integrity of the data, by performing manual reconciliation i.e. manually adjusting the data can adequately cover the accuracy and completeness of the data.

1. RBAC - Role based access control 2. Authorised views 3. Row level security which allows the creation of access policies that filters the data based on indicators and attaching them to user groups 4. Column level security that anonymises or pseudo-anonymises the data during ingestion 5. Encryption at rest using self managed encryption keys The above are some of the efficient techniques to protect PII data Additionally it’s important to have a stringent process to manage Joiners-leavers-movers access policies within the organisation

Anonymization and pseudonymization are effective methods to protect sensitive information while still allowing meaningful analysis. Additionally, differential privacy techniques can be employed to add noise to individual data points, safeguarding individual privacy while maintaining aggregate statistical accuracy. When conducting data quality assessments, it's crucial to define and adhere to privacy policies. Clearly communicate how data will be handled, stored, and anonymized to ensure compliance with privacy regulations and build trust with data subjects.

Data Catalogs should clearly capture information about the sensitive content of the datasets to educate people across the organisation on the usability as well as restrictions that apply while using the datasets (often goes undermined, but extremely important)