How to balance the needs of privacy and customer self-service in the age of AI

Artificial Intelligence (AI) has been around for decades, but with the release of ChatGPT by OpenAI in late 2022, it achieved mainstream attention and people started to realize its potentially mind-blowing capabilities. This has created a spark of AI innovation across companies, who now leverage AI to reduce workload and boost various business functions—particularly customer self-service.

Customer self-service refers to a knowledge base/help center or chatbot that customers can use instead of contacting someone for help. To create an effective self-service solution, organizations must train their AI systems with relevant data for the resulting system to be as good as it can be.

A problem that stems from this are the privacy concerns that could come with the data the bot is exposed to. This creates a paradox. On one hand, there is a need for the convenience of providing self-service options; on the other hand, creating those options creates the risk of publicly exposing the organization's intellectual property. Finding the balance between privacy concerns and providing efficient customer self-service options is crucial.

What to do in this situation? This article aims to tackle the issue and provide a solution to balancing privacy and customer self-service in the age of Artificial Intelligence.

While the benefits of AI in customer self-service are clear, it's crucial to understand the complex privacy landscape that comes with it.

Privacy Concerns/Paradox of AI Self-Service

AI self-service tools have many benefits, including reducing workload for human support agents, saving costs, and increasing customer satisfaction through accurate and quick responses.

Artificial Intelligence also helps companies with more than self-service tooling. It can organize data and learn patterns, reduce human error, conduct analysis, and even forecast trends. Documentation teams who have AI trained on their internal docs can leverage AI to assist in auditing documentation, noticing broken/missing content, identifying weak spots, and preparing for future additions. These AI assists can help teams make better strategic decisions.

However, along with these benefits come significant privacy considerations that organizations must address.

Privacy Risks of Third-Party AI Integrations

Many companies want to streamline their support operations with the help of an AI integration, but these AI integrations are often 3rd party, which could be a privacy concern. Organizations that make use of 3rd party AI integrations often have to train the AI on private customer data such as past conversations, a proprietary code base, personally identifying customer information, and IP-related information, which creates a risk of sensitive data being exposed.

AI can also reduce the risk of privacy breaches by reducing human error and minimizing the number of people who have access to raw data. It can help to accelerate business efforts, but it can also create problems around privacy and security.

The Data Dilemma in AI

The foundation of AI is data, and improper handling of that data can lead to breaches and break the trust between company and customer. Data on its own can be difficult to exploit, while AI is a tool that can be built using that data to exploit it and garner value from it.

Many customers want their experiences personalized and powered by AI, often preferring not to have human interaction and instead interact with tools that will lead them to the answers they're looking for. However, getting the tools up to the standard required to be useful often involves access to data and information that an organization might not want to release. At the end of the day, many people would much rather have their information safe than have access to AI tools that may jeopardize the safety of their information.

Companies also want to gather data on their users to be able to customize their AI services to better serve and retain users. "Cookies" are a significant way to gather data. Navigating this fine line of data and AI is very important for companies because the worries about data privacy go both ways.

Given these privacy challenges, how can organizations leverage AI while maintaining customer trust? The answer lies in transparency.

Transparent AI Builds Trust

Many users are worried about how their data is used, especially when there are frequent data breach news reports. Reports such as this one from Salesforce say that the majority of customers feel like they do not have control over their personal information and want to know more about how their data is being used. Data privacy is more than a legal requirement; it also builds trust between companies and their users.

The best organizations find a balance between using AI to develop better customer experiences and protect customers' privacy rights.

Some ways you can do that include:

• Transparency: Clearly communicate to customers how their data is processed, stored, used, and potentially shared, in a manner that is easily understandable by the average user.
• Customer Control: Make sure customers have the power to opt in or out of data collection and processing. Control could also include giving customers the ability to access the data collected, correct it, or request it be deleted. Outdated or incomplete data could lead to disastrous results in AI systems, so giving customers control over their data builds trust.
• Security: Implement the latest security protocols to ensure that customer data is always safe, as cybercriminals may also use AI tools for malicious purposes.

By implementing these practices, organizations can reap numerous benefits while safeguarding customer privacy.

Other best practices you can perform to ensure trust between you and your customers and also enhance your data privacy are:

• Anonymization of customer data used for training AI models, reducing the risk of exposing personally identifying information.
• Model Interpretability: This refers to how easily humans can understand why an AI model makes certain decisions. Offer customers clear explanations of how their data contributes to improved services.

By implementing these practices, organizations demonstrate their commitment to data privacy, which can yield multiple benefits:

• Increased customer trust
• Organic growth through positive word-of-mouth
• A compelling selling point for marketing

Transparently communicating these data protection measures to users not only reinforces trust in your product but also encourages recommendations, potentially becoming a key differentiator in your marketing strategy.

While transparency is key, it's not the only consideration. Organizations must also rethink how they structure and protect their knowledge bases to ensure privacy in AI-driven self-service systems.

Rethinking Knowledge Base Architecture

As knowledge bases grow to include sensitive information, controlling access to specific documents becomes crucial for data privacy. Traditional approaches of designing knowledge bases to be entirely public or private may no longer be enough. Instead, a hybrid model with access control mechanisms can strike the right balance.

Role-Based and Attribute-Based Access Control

A great approach is Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC). With RBAC/ABAC, access is granted based on the individual's role within the organization or their attributes. For example, in internal documentation, executives may have access to all documentation, while entry-level employees can only see relevant sections. Externally, paid customers could have access to specialized content, while free public documentation contains limited information and a call-to-action to gain access to the specialized content.

A key advantage of this approach is the ability to define these access rules at the start and apply them consistently with multiple users. It simplifies administration and reduces the risk of manual errors and wrong access to sensitive or proprietary information. A tradeoff is that these systems can become complex as the number of roles grows.

While RBAC and ABAC offer powerful access control capabilities, they're not the only options available to organizations.

Access Control Lists (ACLs)

Another approach is Access Control Lists (ACLs) where each document has a list of authorized users or groups. However, maintaining and updating these lists can be difficult, especially in large organizations with a lot of personnel movement.

Choosing the Right Access Control Approach

In essence, the best approach to access control depends on the organization's specific needs, resources, and the sensitivity of the knowledge base content. It's possible to combine different methods, using RBAC/ABAC for broad access rules while supplementing with ACLs for exceptions or highly sensitive materials. Robust access control rules are essential to help protect information and follow appropriate privacy and security policies.

With a clear understanding of privacy concerns, the importance of transparency, and strategies for secure knowledge base architecture, we can now explore how to strike the right balance in AI-powered customer self-service.

Finding the Balance

The AI industry grapples with what's known as the "Black Box problem" - the challenge of understanding and explaining how AI algorithms arrive at their decisions. While ongoing innovation aims to address this issue, it's crucial to recognize that organizations don't have to sacrifice either privacy or capability when implementing AI tools for customer self-service.

The key lies in a multi-faceted approach:

• Optimize knowledge base architecture
• Implement robust access control protocols
• Embrace transparency in AI operations
• Provide customers with control over their data

By adopting these strategies, organizations can not only meet their legal responsibilities but also deliver superior self-service solutions. This approach builds a foundation of trust with customers, potentially leading to:

• Increased adoption of AI-powered services
• Enhanced customer loyalty
• A competitive edge in the rapidly evolving AI landscape

In conclusion, the future belongs to companies that can effectively balance the power of AI with respect for customer privacy. By prioritizing transparency in data management and usage, while leveraging capable AI self-service tools, businesses can position themselves favorably in the AI-driven future. The goal is not just to use AI, but to use it responsibly, simultaneously fostering trust while also driving innovation.