While marketing, sales, and customer support teams use generative AI to boost productivity, there are data security implications.
ChatGPT and other generative AI solutions continue to grow in popularity as companies find new ways to use these tools to improve productivity. In fact, ChatGPT has had the fastest adoption rate out of the major Internet services launched in the past few decades, reaching one million users in just five days.
Generative AI is a type of AI for generating text, images, video, or other forms of content. After being trained on vast amounts of data, these AI models learn enough patterns and contextual insights to be capable of creating high-quality and relevant content based on prompts. This is enabling marketing and sales teams to automate content creation, lead generation, customer service, and many other aspects of their work.
Despite the potential benefits, however, companies like JPMorgan Chase, Amazon, Verizon, and Accenture have already banned ChatGPT for work over security and privacy concerns. But we believe marketing and sales teams can use ChatGPT securely if they put the right processes in place.
Let’s look at four ways marketing and sales teams are using generative AI today, and the potential data security implications organizations face. We’ll also discuss some ways to mitigate the security risks associated with using generative AI.
1. Content Creation
Marketing teams can use ChatGPT to generate blog posts, social media posts, product descriptions, and other written content. By automating content creation with generative AI, marketing teams can produce more content within less time and ensure they have a consistent flow of new content to publish. This also frees up resources to focus on other marketing efforts, such as building new campaigns and analyzing the results of previous ones.
The Data Security Implication:
Although content creation with generative AI has come a long way, there’s still a risk that the AI will generate inappropriate or biased content. AI models like GPT-4 are trained on large amounts of text that might contain offensive, discriminatory, or harmful language. These biases during training could impact the content it produces.
The content an AI generates could also be plagiarized or violate copyright laws. Since most of today’s large language models are trained on public data sources, there’s a chance the text it generates could be too similar to existing content.
Mitigating these risks requires human editors and moderators to verify that the content is safe, unbiased, and original. This ensures the AI generated content aligns with ethical standards, brand guidelines, and company values.
2. Lead Generation
Marketing and sales teams can use chatbots and automated conversations to collect information from website visitors and potential leads. During a conversation with site visitors, the chatbot can ask targeted questions and guide them through the initial stages of the sales process. The information collected by the chatbot can also be used to optimize future sales strategies.
The Data Security Implication:
The information gathered from customers using a chatbot could contain personally identifiable information (PII) like names, email addresses, phone numbers, or even more sensitive data like social security numbers or financial information. Collecting this data can pose security and privacy risks because it could be misused or accessed by unauthorized individuals.
These data risks can be mitigated by implementing encryption, access controls, and other security measures. Understanding where sensitive data might be stored can also help organizations safeguard customer data and maintain compliance with privacy regulations.
3. Customer Service
Sales and customer representatives can use ChatGPT to automatically generate email or instant messaging responses to customers. By streamlining customer interactions, generative AI can help these teams provide more timely and consistent responses. This also allows businesses to handle a larger volume of customer requests with fewer human representatives.
Customers could also interact with the chatbot directly to get quick and personalized answers to their questions. This would allow customers to get immediate assistance at any time without waiting for a human representative. In addition, the chatbot could handle repetitive or low-level inquiries and free up time for human representatives to focus on more complex or high-priority issues.
The Data Security Implication:
Generative AI can automate many aspects of customer service, but it also introduces data security risks. Sales and customer representatives might accidentally input sensitive customer data into ChatGPT. That data could later be exposed to employees who don’t need access to customer data.
These risks can be largely mitigated by implementing safer processes for input data into chatbots as well as accessing it. Training on data security best practices and tools to automatically anonymize data can help sales and customer service representatives use ChatGPT safely. Robust access controls and visibility into who has access to what can ensure that only certain employees who handle customer interactions have access to customer data.
4. Personalized Recommendations
A generative AI model can analyze customer data to provide hyper-personalized recommendations for products, services, or content. Using information like browsing behavior and purchase history, AI can make suggestions that closely align with the preferences of an individual customer. This personalized approach can help businesses improve customer satisfaction and increase conversions.
The Data Security Implication:
Large amounts of customer data is the key to hyper-personalizations, but collecting this information could violate data privacy laws. For example, GDPR and CCPA are two well-known regulations that typically require consent before collecting personal data and have strict guidelines for its usage. Information about previous purchases might also contain financial data or other sensitive data that pose a security risk.
To address these data security implications, organizations should follow appropriate regulatory guidelines around handling of data. This usually includes clearly communicating what data is collected and how it’s being used. It’s also important to provide customers with control over their data through opt-out mechanisms or preference settings.
Learn more about data security for generative AI
As you can see, data security is an important concern when using generative AI. Many of the latest generative AI solutions like ChatGPT have privacy policies that allow them to collect PI from chat sessions and share this data with other organizations. This introduces privacy risks that can lead to data breaches and fraud.
SafeType is our new extension for Chrome and Edge browsers that lets users know when they’re about to input sensitive data during a ChatGPT session and enables them to automatically anonymize the information. This is one of many ways to mitigate privacy risks associated with using ChatGPT.
We looking for input to make the tool more valuable, so please join our public Slack community #cyeralabs and share your thoughts with us. And if you don't have SafeType yet, download it here!
