Data privacy has been a growing requirement ever since the internet age began. So much personal information is stored in and transferred through computer networks that protecting it has become an official mandate. Most companies must follow HIPAA, GDPR, or another industry or locality-based privacy rule. By the end of 2024, 75% of the world's population will have their personal data protected. You don't need to be a large enterprise organization to have data privacy compliance at the top of your mind; it goes hand in hand with cybersecurity, and privacy requirements hit all sized companies. Between July 2020 and July 2021, GDPR violations rose by 113.5%, and associated fines jumped by 124.92%. When it comes to HIPAA violations, each incident can carry a penalty between $100 to $25,000. It should go without saying that when companies collect, send, or store personally identifiable information (PII), it needs protection. To stay on top of your privacy compliance obligations, you should keep up with trends in this area. We've documented the most significant data privacy trends developing in 2023 that you should be aware of.
What's Happening in Data Privacy Compliance?
AI Governance
Approximately 40% of privacy compliance technology requires artificial intelligence (AI) to operate. AI has undoubtedly made its way into many of the applications we use on a daily basis, so it's no surprise that AI is running many of the algorithms responsible for keeping data protected. But what happens when there is a problem with the AI? This is the question that AI governance is working to address. This is a new trend in data privacy because AI has never been so prevalent throughout the data journey as it is now. Whenever AI is used in the data protection area, organizations need to govern it properly. This helps ensure that automated processes aren't accidentally exposing sensitive data.
Consumer Privacy UX
A trend that we've seen over the last several months is putting more privacy power into consumers' hands. Many privacy regulations require that apps and websites provide data transparency by telling people what data they're collecting, how they're collecting it, and what they do with it. People also need an "out" to get their data back. These needs have led to the development and widespread adoption of consumer privacy UX. You can think of this as a centralized privacy portal where people can access privacy-related settings in various apps. This gives better visibility into how their data is being collected and used.
Increased Scrutiny of Remote Employee Monitoring
The pandemic has forever changed the global workforce. Many organizations now run completely remote offices or use a mix of remote and in-office staff. The dramatic increase in people working from home has led to data collection changes. Companies are ramping up their monitoring of those employees working off-site, but this type of monitoring opens a can of worms regarding data privacy. Organizations need to ensure that they aren't encroaching on the rights of their staff. This is most pertinent when putting monitoring software on employee devices, as approximately 49% of remote employees use their personal computers for work. Companies often put endpoint device monitoring in place for security reasons. They need to ensure they are not gathering or backing up any personal data owned by the employee, not the company.
Data Localization
One of the concerns when the social app TikTok became popular relates to location. With the firm being a China-based company, people worried about the privacy of their data. The data was initially stored on servers governed by the Chinese government, a country with very different data privacy rules than the US and other countries. Where a server resides governs the privacy rules and regulations that it may fall under. Thus, companies and governments are now asking a question of cloud providers, "Where is my data stored?" Many want their data to be as close to home as possible.
Privacy-Enhancing Computation (PEC)
Using privacy-enhancing computation is a way that AI is helping cybersecurity. Using PEC as a built-in component of software and apps, developers address privacy concerns by making data protection more automated. Look for PEC components in data analytics when shopping for business tools.