How to protect data privacy when using AI

Every day, companies are taking the plunge and diving into the world of AI. After all, AI could revolutionize your customer service operations and bring exciting new opportunities to your business. But one big question remains: How will you address the issues surrounding data privacy and trust?

Companies are keen to tap into AI’s potential while navigating the complex terrain of ethical and secure data usage. Rest assured that these aren’t insurmountable challenges—they just require thought and consideration.

In this post, we'll explore what AI data privacy entails. We’ll look at some of the associated risks, then propose some best practices that will give you a leg up when integrating AI into your operations.

Let’s start with a brief primer on AI data privacy.

What is AI data privacy?

AI data privacy concerns how you handle personal and sensitive data when using AI algorithms. Are your practices secure? Ethical?

Ensuring that AI systems respect individual privacy rights means adopting practices like data anonymization, secure data storage, and transparent data usage policies. Many companies overlook these details—or shoehorn them in as an afterthought—and this can come back to haunt them.

If we’re talking about customer operations, we know that AI commonly handles tasks like chatbot interactions, predictive analytics, and personalized recommendations. Certainly, these applications can radically enhance your customer experience. But to be effective, the applications also require you to collect and process massive amounts of data. Naturally, this raises some questions:

• How is this data stored?
• How is the data being used?
• Who has access to this data?

How organizations answer these questions has a legal bearing. Industries and regulators have requirements regarding sensitive data, and noncompliance can lead to legal and financial repercussions. For example, General Data Protection Regulation (GDPR) violations can lead to fines in the range of millions of dollars.

In addition, how you deal with AI data privacy has a huge effect on customer trust. According to Twilio’s 2023 State of Customer Engagement Report, 98% of consumers want brands to do more to guarantee the privacy of their data and be more transparent about how they use their data.

With that in mind, let’s look at some of the specific risks that using AI may introduce.

What are the privacy risks of using AI?

Integrating AI into your customer service operations offers many benefits and several privacy risks. Of particular concern are the risks when dealing with sensitive customer data.

Privacy

The collection, analysis, and storage of customers’ personal information may be essential to the effectiveness of your AI algorithms. However, dealing with sensitive data like this introduces significant risk.

After all, mishandling sensitive data may lead to unauthorized disclosures. For example, what if a bug in your code causes a chatbot to display a user’s order history to a different user during a live chat? This is a serious breach of privacy.

Bias

AI systems are trained on massive amounts of data and used to make predictions. The predictions may guide customer interactions or inform recommendations to customers. What would happen if the data used to train your AI algorithms contained biases? How might that skew or bias the resulting system?

Imagine a customer service AI chatbot designed to handle product inquiries and trained predominantly on data from customers who have purchased high-end, expensive products. When a customer inquires about a low-cost, budget-friendly product, the chatbot provides less-detailed responses or redirects them to a product page rather than offering personalized assistance.

In the above example, the chatbot shows a bias toward high-end product purchasers, giving them more attention and personalized service. This kind of bias negatively affects your customer experience. But more importantly, this chatbot behavior would also reveal that it treats customers better or worse based on their purchasing history.

Security

Finally, we have the most unmistakable data privacy risk: basic security. What if your stored customer data isn’t encrypted? Or you haven’t enforced access control to that data?

A compromised AI system can be a gold mine of customer data for hackers. We can only imagine the devastating consequences of having sensitive customer data stolen, then held for ransom or published online. It’s no wonder that 42% of brands say their top customer engagement challenge in 2023 is finding a balance between security and customer experience.

How might we navigate these risks? Let’s consider some best practices that would help us to build a more secure and trustworthy AI-driven customer service operation.

How do you ensure data privacy and transparency with AI?

Here are some best practices to significantly mitigate the data privacy risks associated with using AI in customer service.

1. Anonymize your data

Data anonymization involves removing or modifying personal identifiers in your datasets. This prohibits identifying or associating individuals with data. When you use this data AI model training, you have useful data without the risk of compromising customer privacy. Even if there’s a breach, there’s no way to trace the leaked data back to specific customers.

2. Involve human oversight

This means incorporating human judgment into your AI decision-making processes. A human supervisor should review and validate the decisions made by your AI systems, providing a crucial layer of oversight. Practicing human oversight helps you catch errors or biases that the AI might overlook.

3. Implement data retention policies

You probably don’t need to keep your customer data forever. In fact, you should keep it only for as long as you need it in your processes, then delete it once it’s no longer necessary. By limiting how long you keep your customer data, you reduce the risks of unauthorized access or data breaches.

Data retention policies dictate how long to store data and when to delete it. Establish these policies, then enforce them. This not only enhances privacy but also ensures compliance with various data protection laws—such as GDPR, HIPAA, CCPA, and more.

4. Be transparent with your customers

Transparency with your customers is essential to building their trust. Your customers have the right to know how you use their data.

One way to do this is by using Twilio’s AI Nutrition Facts labels. These labels offer a clear and concise overview of how an AI model uses data, along with its level of data privacy. Because so much of modern AI usage is opaque, providing a high level of transparency to your customers will be a welcomed change and foster a tremendous amount of trust.

Build AI you can trust with Twilio

Using AI in customer service applications will likely bring your organization some huge gains over the long term. However, the requisite handling of sensitive customer data means you also need a strong focus on data privacy. Mishandled data, model bias, or insecure practices can damage your business reputation—or worse, you might incur legal or financial repercussions.

By adopting the key best practices we’ve outlined, you’ll be well on your way as you navigate this tricky landscape.

Twilio's CustomerAI is a reliable and trustworthy AI solution to help you in your customer engagement processes. By combining your customer engagement data with Twilio’s predictive AI capabilities, you’ll gain AI-powered insights that help you understand and serve your customers better. Because of the robust privacy framework from CustomerAI, you can rest assured that your AI implementations are effective and ethical.

In addition, Twilio’s AI Nutrition Facts labels will help you make informed decisions about the AI capabilities your organization should adopt, plus help you be transparent with your customers about how you use their data.

When you’re ready to begin building AI into your customer operations—responsibly and securely, of course—contact us or sign up for free today.