Privacy has always been an issue ever since companies started collecting consumer data. Now that AI is in the picture, its use is growing. Businesses across industries use it to enhance their operations and improve customer experiences.
However, many of the AI solutions that companies use require data comprising sensitive customer information. As AI integration continues to increase in daily operations, businesses must safeguard customer privacy.
What are the privacy risks with AI?
Privacy is a concern to many as more companies use AI systems to collect personal information and enhance their performance. Due to their advanced algorithms, AI can gather and interpret vast amounts of data. It can uncover various insights with the information but tends to reveal much more than needed.
As such, this amount of information increases the risk of data breaches. AI systems often require extensive datasets for training. Businesses may become a target for cyber-attacks, leading to a privacy breach. When this instance occurs, it reveals sensitive information that cybercriminals can use for malicious purposes.
Another issue is that AI can drive targeted advertising that can feel invasive. Companies use personal data to tailor ads so specifically that it raises ethical concerns about the extent to which businesses are monitoring online behaviour. With the risks involved with AI, small businesses must take action to protect consumer privacy through the following steps.
1. Give users control over their data
Giving users control over their data is essential to building better consumer relationships. Customers are willing to share their information with businesses they trust. Therefore, taking this step is fundamental to giving consumers the upper hand in how companies use their data.
Small businesses can achieve this by implementing clear privacy settings. This allows customers to opt in or out of data collection and use. When users have this option, companies should also link to a privacy policy that is easy to understand. The privacy policy should avoid jargon and educate audiences on how businesses use their information.
Offering tools for users to view their data and have the option to download or delete it helps. Doing so will gain further trust while it also guarantees compliance with privacy laws.
2. Use a zero-trust approach
Zero trust is a security strategy where organisations choose not to trust anything inside or outside their perimeters without verification.
Small businesses should adopt this approach because it prevents unauthorised data access. A zero-trust model ensures each user and device is authenticated and authorised to gain access to reduce the risk of breaches. This involves implementing strong authentication methods like multi-factor authentication. Users and devices should have minimum access to perform their tasks. This limits the potential of damage from a security breach.
3. Assess AI service providers for security
Small businesses can guarantee data protection by assessing AI services. This process involves evaluating AI services like a company would with cloud providers.
AI service providers often handle sensitive information. It is essential to ensure they have strong security measures to protect data against breaches. Companies can evaluate AI service providers by reviewing their security policies and certifications to help them understand their commitment to security.
Additionally, they should check for regular audits of the AI services they use. Doing so reassures the business that the provider is undergoing regular third-party security audits.
4. Employ data governance
Small businesses should employ data governance before applying data privacy to data-containing areas. This ensures they use data strategically, maintaining accuracy and consistency when utilising AI.
This tactic involves creating policies and procedures for data management. How a company collects, stores and processes data with AI should fall within a framework. That way, it defines who is responsible for all data-related tasks. They should also classify data based on sensitivity and establish protocols for handling different data types to ensure all sensitive data is handled with extra care.
5. Remove unused sensitive data
Before companies use AI algorithms, a key step for them is to remove unused or unnecessary data. Data minimisation helps because the less sensitive data a business holds, the less it risks exposing it.
The first step to removing unused data is conducting an audit. This enables businesses to identify anything obsolete. Once they determine what is no longer needed for operations, they can use tools to automate data purging. Tools like ManageEngine DataSecurity Plus provide file analysis, real-time monitoring and reporting. It is useful for identifying, classifying and purging sensitive business data.
6. Use privacy-preserving AI techniques and tools
Companies can use privacy-preserving techniques to train and utilise AI models without exposing underlying data. One of these tactics includes differential privacy, which adds noise to data so the output avoids compromising data privacy. Tools like Google’s TensorFlow Privacy and Apple’s Core ML use this technique to help businesses deploy machine learning models safely.
Another tactic involves federated learning. This enables AI models to be trained across several decentralised devices that contain local data without exchanging it. TensorFlow Federated can implement this technique for businesses. It is an open-source tool for machine learning on decentralised data.
7. Limit use of generative AI
Limiting the use of generative AI is an important consideration for small businesses. Technologies like ChatGPT and DALL-E often need large datasets for training. Applying these tools to only closed datasets is crucial. Doing so eliminates the risk of data misuse or exposure.
That is where companies must define appropriate use cases for generative AI. Avoiding deploying it for tasks for accuracy is critical. If they do use generative AI, there should be human oversight to ensure employees stay in the loop. Simultaneously, they should analyse the data sensitivity involved when using generative AI and avoid using it in scenarios where it might process highly sensitive data.
Instilling privacy protection in the age of AI
Safeguarding privacy in the age of AI requires various tactics and strategic approaches. While implementing privacy protection practices takes time, businesses can ensure they use AI responsibly to guarantee safety for their customers and businesses.
Eleanor Hecks is the managing editor at Designerly. She’s also a mobile app designer with a focus on UI. Connect with her about digital marketing, UX and/or tea on LinkedIn.
