Understanding the issues and operations of specific business processes requires fluency in the discipline’s terminology. For example, for understanding financial tasks and workflows, having a conceptual understanding of accounts payable, accounts receivable, asset, balance sheet, and other terms are essential. For data privacy operations, understanding the terminology is absolutely critical.
Privacy is top of mind for most businesses across the globe. For Privacy Day 2020, we can reflect on the last several years as we have witnessed the acceleration and crystallization of privacy around the world. Companies want their current operations to proceed unfettered in the face of new regulations. The scope of GDPR, CCPA, and other privacy laws affects virtually all companies who collect and use the personal information of their customers. As the tenets of privacy have solidified, so has key terminology that helps privacy, security, and risk leaders communicate privacy issues, challenges, projects, and compliance efforts.
With this backdrop, the Integris Data Privacy Dictionary serves as a resource to enable a better understanding of global data privacy regulations and terms. This privacy dictionary contains the most prevalent privacy terms that represent common searches, headlines, and worldwide regulations.
In this first edition, you will find sixty privacy-terms with definitions derived and normalized from multiple resources including the Federal Trade Commission (FTC), the European Commission (EC), the UK Information Commissioner’s Office (ICO) and the International Association of Privacy Professionals (IAPP). Provided with each definition are concept examples and links to additional resources for further learning. Here is an extract from one term, “Toxic Combinations of Data”:
A key issue for organizations leveraging Big Data is the potential for toxic combinations of data. While many organizations will mask the identities of customers, consumers, or patients for analytic projects, combinations of other data elements may lead to unexpected toxic combinations that can allow for re-identifying individuals. This is often the case with data lakes that take in a diverse mix of data sources and data source types such as structured, unstructured, and semi-structured data. Data in-motion can also be a blind spot for many companies, given most organizations don’t know what data is entering and leaving their organization every day.
Whether you are a privacy professional, IT leader or security practitioner, this dictionary will help you understand the dozens of terms that are used in today’s privacy discussions across the globe. The Integris Data Privacy Dictionary provides a common lexicon for data privacy, data governance, and security teams to collaborate on implementing privacy policies and controls for optimal compliance readiness.