Leveraging AI and Analytics in Your Data Privacy Program

Headshot for blog author Roy Horgan

Roy Horgan

3 min read

In an age of rapid technological transformation, governments are playing regulatory catch-up as they try to keep pace with technological developments and the increasing amount of personal identifiable information (“PII”) generated by our every-day lives. Privacy laws regulating the use of PII continue to strengthen (Gartner estimates that while 10% of the world’s population was covered by comprehensive privacy laws in 2020, by year-end 2024 it will be 75%). However just when it seemed that they were catching up, then along came AI.

While AI laws are in their comparative infancy, governments are racing to put in place regulations to maximize opportunities but limit the risks. As more and more draft AI laws emerge, and with the flagship EU AI Act having just been approved by the EU Parliament, we can already see tremendous thematic overlap between AI and privacy regulatory principles: transparency, lawful (and unlawful) uses cases, accountability, documented risk assessments, human oversight of automated processing, data quality, security, user data control - the list goes on.

While new privacy laws and their AI law siblings will mean a lot of work for privacy professionals, it’s important to remember that AI, as well as Analytics, can help a privacy program scale to meet the increased challenges.

Using Analytics and AI To Strengthen Your Privacy Program

  1. Understand the landscape: The first step in building a robust data privacy (as well as AI compliance) program is gaining, and keeping up to date, a comprehensive understanding of the PII and AI landscape of your organization. For example, it’s critical to know how much PII you have, what type of PII you have and who it’s about (the “Data Subjects”). Qlik Sense apps can be a key tool to look holistically at the PII across your entire organization, using Data Lineage to ensure PII locations are understood, deduping to ensure a correct tally, and enabling you to drill down by certain characteristics, e.g., region, PII type, etc. Qlik AutoML can then be applied, to predict growth curves in certain geographies, helping you estimate as to when you are likely to reach certain legal thresholds. Qlik Alerting can be used to alert you to any triggers important to your program management. Qlik Sense apps can also be built on your supply chain management to identify AI tools in your vendor ecosystem, leveraging AutoML to predict trends and Qlik Alerting to prompt you about noteworthy developments.

  2. Govern data: Analytics with Qlik Sense has in-built data governance, with features enabling anonymization or pseudoanonymization of PII fields as well as user-controlled access. Qlik Sense apps can also be built to help you understand the governance of underlying databases, such as access rules and data retention of these datasets, enabling you to identify and close any compliance gaps.

  3. Automate workflows: Mature privacy programs will have sophisticated workflows to manage high-volume privacy tasks, such as Data Subject requests, privacy impact assessments and records of processing activities. By using Qlik Sense to understand where your PII resides you can execute these effectively against predetermined rules and leverage features such as Application Automation to ensure your privacy program can scale by automating workflows, where possible.

  4. Understand the effectiveness of your program: By layering Qlik Sense dashboards over your privacy program, you can better understand organizational risks and volume stress points at a glance, as well as automate reporting and KPIs to continuously evaluate your program.

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