Data Privacy For Chat

What is conversational chat data?

Customer interactions on communication channels like social media, SMS messaging, and chatbots generate large amounts of conversational text. This unstructured data contains valuable information about product or service issues, sentiment, feedback, and more but it may also contain personally identifiable information that is subject to regulatory control. Therefore, access to this conversational text for analysis is often limited or completely restricted in its raw form.

Read the solution brief to discover how you can leverage conversational chat data and still mitigate privacy risks.

Get The Brief

Derive value of customer interactions for analysis
Reduce regulatory risk by de-identifying sensitive data attributes
Deploy into existing conversational channels and workflows with minimal disruption of operations
Improve the performance and utility of common NLP analytics on conversational data

Solve for privacy—in context and at scale

Privitar Data Privacy for Chat uses natural language processing techniques to recognize and classify text. It then applies policies to de-identify sensitive data at speed and scale. Data Privacy for Chat uses policies to manage the use of conversational chat data. Policies define how sensitive data is masked, how it can be used, and who can access the data in raw or masked form, ensuring safe and compliant analysis. De-identification permits analysis without exposing sensitive, protected information.

How does it work?

Privitar combines a set of de-identification transformations with deep learning models for natural language processing (NLP) to allow users to apply privacy policies on conversational text data.

Classify sensitive text data with natural language processing

The NLP model classifies free-form text, character-by-character. The Privitar transformation engine uses the classifications to apply a privacy policy. A policy consists of rules determining how to transform the text, allowing for consistent tokenization of protected information. Privitar trains the NLP model using a proprietary active learning process that enables higher accuracy than competing offerings from cloud vendors. You can tailor the model to your organization’s data.

Integrate with your existing system landscape

You can integrate the Data Privacy for Chat service into your existing data pipelines like Databricks, NiFi, Kafka, and many more. Kubernetes deployment of the NLP model allows you to scale demanding text classification workloads. By deploying the model to CUDA accelerated hardware you can support real-time de-identification.

Uses for Conversational Chat Text in Analytical Scenarios

De-identifying this sensitive data allows you to provision it to a wider group of analysts while upholding customer privacy and ensuring regulatory compliance. Responsible analysis using this valuable unstructured (free text) data source starts with protecting sensitive data that can reveal an identity—such as name, address or location, organization, phone number, email address, credit card number, date of birth, and so on.

Review SMS exchanges between customers and service agents to measure effectiveness and efficiency.

Analyze chatbot and live chat interactions for new product and service ideas.

Gauge customer, citizen, supplier, and employee sentiment from conversations on messaging platforms.

Privitar Data Privacy for Chat

Using deep learning to mitigate privacy risks when analyzing conversational chat data