AI and Privacy: Navigating the Challenges of the Digital Age

As artificial intelligence (AI)-based tools and software make waves across the business world, one question keeps popping up: What’s happening to our data? 

Since AI processes the data we feed into it to improve its services and predictions, it’s no surprise that AI and privacy concerns are growing. Let’s explore how AI gathers personal data, what it does with it, and the privacy risks that result from sharing your information with AI tools. Then, we’ll look at some best practices for keeping yourself safe in the digital age. 

How does AI gather and use personal data?

AI needs data to function properly because it relies on patterns to make decisions and predictions. The more data it has, the smarter and more accurate it becomes. 

This is why AI tools gather data from users and other sources — to optimize itself. The more personalized that data is, the better AI can tailor its services, recommendations, and responses to fit individual needs. This helps AI tools create a seamless, customized experience that feels relevant and intuitive to each user.

So, how does AI collect data? Where does it get personal data from, and what exactly does it use this data for? Here are a few of the most common ways AI gathers and uses data:

1. The internet: AI systems track the world’s online activities — the websites we visit, the content we post, the things we search for, and the products we purchase. This is often called “web trawling,” and it helps AI learn how humans think and what we’re interested in. This also happens on a more personal basis. AI marketing tools collect data specific to each user, such as their browsing habits. Using this data, marketers can make more accurate recommendations (like which content or products a user should look at next).
2. Social media: AI monitors your social media activity and collects data on your likes, shares, comments, and which posts you tend to engage with. This data is used in marketing to create targeted ads or guide the algorithms that show you content that matches your preferences.
3. Mobile apps: Many mobile apps track various kinds of data, including your location. For example, navigation apps like Google Maps use your location to provide real-time traffic updates and suggest nearby restaurants.
4. Smart devices: Smart devices like wearable fitness trackers gather data on how you interact with them. For example, a smartwatch tracks your steps and sleep patterns to offer personalized health suggestions based on your habits.

4 major AI privacy concerns for businesses

With all of this data floating around in AI ecosystems, some businesses are concerned about data privacy. Here are four examples of AI privacy concerns businesses need to be aware of:

1. Lack of transparency

One major privacy concern with AI is the lack of visibility into how decisions are made. AI systems are based on complex algorithms, which are difficult for most businesses and consumers to understand fully. 

When AI tools make important decisions, like credit approvals or personalized medical advice, it can be unclear how they arrived at those conclusions. This lack of transparency can lead to customer mistrust, especially if outcomes feel biased or unfair. Both businesses and customers may also fear that their data is unsafe since they can’t see how their personal information is being protected in AI contexts.

2. Unauthorized data use

Another AI data privacy concern is the potential for unauthorized use of personal data. A company might collect data for one purpose — such as improving customer service — but later use it for something else without obtaining consent. This can lead to violations of privacy laws like the General Data Protection Regulation (GDPR), which requires businesses to clearly state how data will be used and give customers the ability to opt out.

3. Copyright and intellectual property challenges

AI’s ability to web trawl and generate content based on what it learns raises questions about copyright and intellectual property. As AI systems scrape the internet for data to create new works, they can recreate copyrighted material. This poses a problem for businesses whose content is replicated by AI, as they may not want their work to be used this way. 

This is also murky territory for businesses that use AI to generate content. It’s not always clear whether the AI is inadvertently reproducing someone else’s copyrighted work or creating something original. The line between inspiration and infringement is often hard to define, which has even led to some court cases.

4. Security risks and data breaches

As AI systems handle increasing amounts of personal data, they become attractive targets for cyberattacks. If AI systems are breached, the data they store can be exposed. This is especially concerning when it comes to highly sensitive data, like medical records and payment details. Even one breach can significantly damage a business’s reputation and financial standing.

Mitigating AI privacy risks: 6 best practices

When businesses use AI tools, they must address privacy risks head-on. This helps them protect sensitive information — both theirs and their customers’ — and build customer trust. Here are six best practices for mitigating AI privacy risks:

1. Encryption

Encryption is one of the most effective ways to protect personal data in AI systems. Encryption converts data into a coded format that can only be read or decoded by someone with the matching decryption key. This adds an extra layer of security. Even if someone accesses your datasets, they won’t be able to read or understand them, which makes it much harder for unauthorized parties to access or misuse sensitive information.

2. Data minimization

Data minimization means you only collect the data you need — no more, no less — and retain it only for as long as necessary. To use this strategy, ask yourself, “Is this data essential for the task at hand?” If the answer is no (or if it’s unclear), it’s better to avoid collecting that data altogether.

By limiting the amount of personal information your business gathers, you reduce the risk of exposing sensitive data in the event of a breach. This helps you comply with privacy regulations like GDPR. Plus, it makes managing and safeguarding your data easier. In other words, simplicity helps you maintain security.

3. Data anonymization

Anonymizing personal data involves stripping away identifying details. For example, you might store general information like age or location but delete names, addresses, and payment details. This makes it harder for data to be traced back to individual users, reducing the potential for data misuse. As a result, you can more safely use data for AI training and analysis without compromising user identities.

4. Ethical AI practices

Ethical AI practices give users greater control over their data. First, businesses must clearly explain what data they collect and how they use it. They must also let users easily access, correct, or delete information whenever they want. Finally, they have to make sure their AI systems are designed with fairness and transparency in mind. This involves checking the systems for biases and discriminatory outcomes, especially when dealing with sensitive information.

5. Regular privacy audits

Regular privacy audits help businesses spot potential risks and stay compliant with changing privacy laws. As part of any audit, companies should review how AI is using personal data and make sure it’s stored and shared safely.

Audits also help identify security gaps, allowing businesses to fix minor issues before they spiral into bigger problems. For example, by catching a small vulnerability in your data transmission process, you can prevent potential breaches down the line. This proactive approach to AI privacy issues helps businesses avoid costly consequences and maintain a solid reputation.

If needed, you can recruit third-party experts to conduct privacy audits. These experts can identify vulnerabilities that internal teams might miss and provide a fresh perspective on your privacy practices.

6. User education and awareness

Whether your business offers AI software or uses AI as part of its workflow, education is key. Clarify how AI collects and uses personal data so users understand the value of their information and how it’s protected (or how it’s not). 

Offering easy-to-understand resources like FAQs and tutorials can help users feel more in control of their data. It also equips them to take action to protect themselves — for example, by updating their privacy settings or recognizing phishing attempts. This empowers users to become active partners in safeguarding their personal information.

Otter: AI you can trust

Not all AI technologies are created equal. You need AI solutions that protect data through rigorous audits, best practice adherence, and compliance with global regulatory requirements. 

By selecting trustworthy tools like Otter, you can rest assured that your business is safeguarding both customer and company data with the utmost care. Learn more about our privacy policies and security commitments.