Customer service excellence has always been one of the primary factors for customer retention and growth. However, the recent paradigm shift in consumer preference towards messaging in business communication, and the ever-increasing expectation of an immediate response, started driving organizations to rethink their online digital experience.

In a research conducted by Forrester, messaging itself ranked as the No.1 customer service channel preferred by consumers in South Korea, Singapore, India, and the US, and among the top three preferred channels across the world.

Chatbots or conversational services today are either rule-based or AI-powered computer programs that interpret customer requests via natural language to predefined Intents and Actions.

There is a number of AI conversational service platforms in the market today, however; choosing the right platform for your use-case or your organization as a whole can sometimes be very challenging; factors such as data privacy and security can quickly become a barrier for leveraging chatbots or implementing meaningful use-cases.

For instance, in healthcare, using a cloud-based platform to build a Health Advisor that provides general health recommendations based on a member’s activity, diet, and medical history will legitimately raise data privacy concerns since sensitive PHI data will be going through a third party (i.e cloud service provider).

When implementing chatbot solutions in these highly regulated industries (such as Healthcare) where data privacy and security are primary concerns, it’s imperative to look for chatbot solutions that can reside within enterprise firewalls (on-premise). This is critical because these organizations need to have complete control over all aspects of their data and they need the flexibility to enforce data privacy, security, and compliance policies.

The Cognitive Business Automation team has formulated a thorough analysis of our recommended approach and best practices.

In this whitepaper, we discuss how an open source / on-premise conversational service platform can leverage AI and protect sensitive data. For the purpose of this analysis, we have chosen RASA as an example and covered the following aspects:

  • Usability: What is the ease of use and learning curve?
  • Configuration: How to properly configure bots to meet the requirements?
  • Data Security: Where & how is sensitive data being stored?
  • Compliance: Is RASA HIPAA certified? Is it HIPAA compliant?
  • Integrations: Which messaging platforms can be integrated with RASA-based chatbots?
  • Languages: Which languages are supported by RASA?
  • Pricing: What premium features are available? Support options?

To get access to the analysis, please click here.