The focus on data privacy started to quickly shift beyond compliance in recent years and is expected to move even faster in the near future. Not surprisingly, the Thomson Reuters Risk & Compliance Survey Report found that 82% of respondents cited data and cybersecurity concerns as their organization’s greatest risk. However, the majority of organizations noticed a recent shift: that their organization has been moving from compliance as a “check the box” task to a strategic function.

With this evolution in data privacy, many organizations find that they need to proactively make changes to their approach to set themselves up for the future. Here are five key considerations to get ready for the future of data privacy.

1. Create a process for staying up to date on new and evolving regulations

While data privacy is more than simply compliance, your organization must comply with all regulations first and foremost — or else risk fines and reputational damage. However, regulations are constantly being passed and changed, making it exceptionally challenging to stay up to date. As of September 2024, 20 states had consumer data privacy laws, with legislation pending in numerous other states. While the U.S. does not currently have a federal data privacy law, the American Privacy Rights Act is in the first stage of legislation.

As the data privacy regulation landscape continues to change, organizations must create a process to manage all pertinent regulations, which can be challenging for global companies. Because organizations must comply with the regulations of their customer locations, not the company’s locations, global businesses often find themselves bound by many different regulations. Organizations are increasingly turning to artificial intelligence (AI) with tools that monitor all relevant regulations and ensure compliance, which saves time and reduces fines.

2. Focus on balancing data privacy with analytics and AI goals

AI at the University of Pennsylvania’s Wharton School found that the percentage of employees who used AI weekly increased from 37% in 2023 to 73% in 2024. However, this significant and rapid increase in AI adoption has created significant data privacy issues. Top concerns include a lack of data transparency, new endpoints for vulnerabilities, third-party vendors and potential regulatory gaps. At the same time, businesses not using AI will likely quickly fall behind competitors in productivity and personalization.

Because not using AI is rarely the right business decision, organizations must take a strategic approach to creating a balance between business value and data security. While technology is part of the solution, platforms and systems cannot solve the challenges without a balanced approach. By creating processes and a framework that helps organizations evaluate risks and benefits, businesses can make smart business decisions with regard to data privacy. For example, a company may adopt automation throughout their organization using AI except in use cases that involve sensitive customer and employee data.

3. Consider privacy-preserving machine learning (PPML)

By using specific techniques in AI and analytics, organizations can reduce data privacy risks. Many organizations are turning to PPML, which is an initiative started by Microsoft to protect data privacy when training large-capacity language models. Here are the three components of PPML defined by Microsoft:

1. Understand: Organizations should conduct threat modeling and attack research while also identifying properties and guarantees. Additionally, leaders need to understand regulatory requirements.
2. Measure: To determine the current status of data privacy, leaders should capture vulnerabilities quantitatively. Next, teams should develop and apply frameworks to monitor risks and mitigation success.
3. Mitigate: After gaining a full picture of data privacy, teams must develop and apply techniques to reduce privacy risks. Lastly, leaders must meet all legal and compliance regulations.

4. Focus on data minimization

In the past, many businesses defaulted to keeping all — or at least most of — their data for a lengthy period of time. However, all data stored and saved must follow compliance regulations, causing many organizations to use a strategy referred to as data minimization.

Deloitte defines data minimization as taking steps to determine what information is needed, how it’s protected and used and how long to keep it. By taking this measured approach and determining which data to keep, organizations can reduce costs, make it easier to find the right data and improve compliance. Additionally, it’s easier and takes fewer resources to secure a smaller volume of data.

5. Create a culture of data privacy

Just like cybersecurity, data privacy is not simply the job of specific employees. Instead, organizations need to instill the mindset that every employee is responsible for data privacy. Creating a data privacy culture doesn’t happen overnight or with a single meeting. Instead, leaders must work to instill the values and focus over time. The first step is for leaders to become champions, express the shift in responsibility and “walk the walk” in terms of data privacy.

Because data privacy depends on team members following the processes and requirements specified, organizations must not simply dictate the rules but instead must explain the importance of data privacy. When employees understand the risks of not following the processes as well as the consequences to the organization and its consumers, they are more likely to comply.

Additionally, leaders should measure compliance with the processes to determine the current state and then the goal. By then offering incentives, organizations can help encourage compliance as well as stress its overall importance.

Start crafting your data privacy approach now

As your team focuses on planning for 2025 and beyond, now is the time to pause to make sure that your approach and goals align with where the industry is moving. Organizations that understand where data privacy is likely headed and take the steps needed to align their goals with the future of data privacy can be better prepared to more effectively gain business value from their data while still ensuring compliance.