Best Practices to Mitigate AI Data Privacy Concerns

Artificial intelligence (AI) and AI-powered tools have flooded the business market, from handy analytics programs to capable chatbots. While these resources can positively transform your business processes, they also come with unique security risks. If you already use or plan to use AI in any capacity, you must understand these considerations and mitigate them through appropriate data security practices.

Why You Need to Prioritize Data Security and Privacy

Casual users can easily overlook the vast amounts of data required to train AI models like ChatGPT and Bard. Every decision an AI makes is based on this information, making data integrity critical to the model’s behavior. Plus, this data is often sensitive, making it a potential target for hackers and accidental leaks. Robust data security in AI helps ensure models perform as intended and safeguards the data needed to do so.

As data privacy concerns have become more widespread, so have consumers’ awareness of how companies use their data. One Forbes Advisor survey found that 76% of consumers are concerned about misinformation from AI, and most consumers are concerned about businesses using AI. Prioritizing data privacy is crucial for using AI ethically and meeting various regulatory requirements.

Addressing privacy issues, decision-making behavior, and data security in AI calls for a multipronged approach.

A Deeper Look at Privacy Issues With AI

AI privacy concerns and security issues range widely and can significantly impact trust, AI efficacy, legal standing, and your bottom line.

1. Deliberate Attacks

Hacking concerns in AI are similar to those present in other types of data handling. As an AI model collects data for training and carrying out tasks — such as customer information or business documents — you must protect that information from being accessed through malicious attacks. A breach in your AI system could expose sensitive information.

Good data security helps defend against deliberate attacks with methods like intrusion detection, secure encryption, and robust access controls. Your needs may vary depending on the sensitivity of the data.

2. Privacy Regulations and User Consent

Data privacy laws and regulations vary widely by industry and region. The General Data Protection Regulation in the European Union is a sweeping example with stringent requirements. In the United States, you’ll find similar legislation in many states, with laws being proposed and passed each year. Some industries also have their own requirements or suggestions within regulations, like the Health Insurance Portability and Accountability Act in health care and the Sarbanes-Oxley Act in finance.

These laws and regulations include requirements such as:

• Allowing consumers to opt out of profiling and automated decision-making.
• Telling users what information you collect and how you use it.
• Establishing appropriate data protection measures, such as access controls and encryption.
• Providing human oversight for important decisions like employment, credit approvals, housing, and insurance.
• Meeting data sovereignty and residence guidelines, which determine where data must be processed and stored.

Even without applicable regulations, following these rules can help you stay prepared for future demands and keep your use of AI ethical.

3. Model Poisoning and Data Tampering

Model poisoning occurs when a malicious entity manipulates an AI’s learning to plant misleading information. Computer scientists researching model poisoning provide the example of uploading images that aren’t safe for work, labeling them as safe, and adding a small red square in the top right corner of each one. The model would equate this square with safe images, so users could upload unsafe images with that red square and get past the AI’s filter.

The model poisoning strategy is less overt but worth watching for, especially in areas like fraud, debugging, and decision-making. Malicious actors could also affect the behavior of an AI model by tampering with data already stored.

4. Insider Threats and Unintentional Breaches

Disgruntled employees are always a concern, and even the most well-intentioned worker can make mistakes. An AI data privacy plan must limit these risks by controlling employee access and capabilities.

How to Ensure Data Security With AI

Although AI privacy concerns are extensive, their solutions often overlap. A comprehensive solution with different tools can help you address these challenges and use AI securely and ethically.

Some technologies and policies to consider when achieving AI data privacy include:

• Encryption: Adding strong encryption to your AI data is crucial for preventing unauthorized access and meeting regulations.
• Data loss prevention (DLP) and classification: DLP can restrict access to sensitive information by classifying it and applying appropriate protection policies, like blocking an employee from modifying your AI’s data. Some examples include role-based access control and attribute-based access control. DLP is especially useful for cloud-based systems and prevents both malicious and unintended data leaks.
• Tokenization: Tokenization replaces sensitive information with tokens that unauthorized entities cannot use. Tokens limit the sensitivity of the information your AI processes for better compliance and security.
• Data masking: This method, also called data sanitation, works similarly by replacing sensitive information with unrecognizable information that’s meaningless to hackers. However, it retains the statistical characteristics that are valuable to the AI.
• Risk assessment: Regularly evaluating your security risk based on your unique system and organization helps you accurately and efficiently respond to threats and implement appropriate technologies.
• User consent and transparency: Meet compliance demands and build trust among users by providing clear, accessible information on your data policies. Tell them how you plan to use and protect their data and offer opt-out choices where appropriate.
• Data minimization: By following data minimization practices, you only collect the information required for the task. This approach is often required for compliance and helps you limit the amount of sensitive data you must protect.
• Human evaluations: Many regulations require human oversight for reviews, so incorporate these steps into your processes. Also, establish an ethical committee or evaluation to ensure your AI makes fair and equitable decisions free of bias.

These are just some potential technologies for achieving AI data privacy, but the right one for your system depends on factors such as your business architecture, industry, AI models used, and operating region.

Leverage AI and Maintain Data Privacy With Salient Process

Privacy and artificial intelligence go hand in hand. While achieving privacy and security might seem daunting, a skilled partner can help you implement the right technology and policies to suit your system.

At Salient Process, we keep your goals front and center during implementation with our North Star Methodology. Our experience spans diverse industries and organizations, and we can help implement AI even in highly regulated fields like we did for FCT, an insurance provider in Canada. We offer full-service digital support powered by industry-leading software from IBM. The experts at Salient Process bring it all together to support your needs for data security in AI.

Learn more about our capabilities, or reach out today to develop a plan for secure, compliant, and effective AI.