Artificial intelligence (AI) and machine learning (ML) are all around us these days. Just check your email inbox. AI is likely filtering out junk and spam emails while making suggestions about what entertainment you might want to consume or products you might want to buy. And your favorite businesses probably use ML to monitor and streamline their operations to help lower costs.
The competition for more data and convenience
Both AI and ML run on data. A lot of data, including data about people. That data creates incredible convenience for both people and businesses. But demands for data also bring heightened concerns around data privacy and security.
Convenient and insightful uses of AI and ML may be outpacing privacy and security protections. This puts companies and industries at risk and could lead to loss of trust, litigation, and enforcement actions if there are misuses of data. Just as other technologies eventually find detractors, new uses and purveyors of AI tech can create greater potential for data to be accessed by bad actors with bad intentions.
Cooperation for more security: The role of technology in protecting privacy
A big change in in data privacy is the growing dual role of technology. This tech used to be singularly focused – it either used the data, or it protected that data—now it must do both and make it convenient along the way.
Here’s where AI-based automation can complement data privacy and help organizations improve their privacy protections. Other technical innovations—such as homomorphic encryption, differential privacy, and federated learning—also have the potential to make headway as new privacy enhancements.
Technology can allow privacy and convenience to coexist
Your personal data has value. Many organizations would pay top dollar for your data (and they don’t all have your best interest in mind). Deciding what and how much data to share has turned us into chess masters playing the long game.
As data-fueled technologies like AI and ML enter the mainstream, stewards of customer data now have dual responsibilities. They must build privacy and security into products and processes, while keeping that same data accessible and useful.
Act on this data privacy trifecta
I recommend organizations that want to improve their data privacy take three actions:
- First, build a resilient, principles-based framework for privacy compliance.
- Second, know your data and how that data is used.
- Third, set a goal through privacy engineering to build privacy and security into the products and services your organization uses and sells in the marketplace.
It can be as straightforward as one, two, three. To help, we encourage you to take advantage of the following resources for more information: