Discover how AI chatbots are transforming business by improving customer support, simplifying operations, and raising important security considerations to keep in mind.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
[Provide the input text here]
[Provide the input text here]
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
Title italic
A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
Lorem ipsum dolor sit amet, line first
line second
line third
Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
Title italic Title italicTitle italicTitle italicTitle italicTitle italicTitle italic
A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
High-quality customer service is now one of the most important goals for companies, and using AI chatbots in business is a cost-efficient and effective way to achieve this. These tools change how companies communicate with customers and address customer service issues. Gartner has forecasted that approximately 30% of all Fortune 500 companies will use single AI-enabled channels for customer service by 2028.
Moreover, using AI is no longer limited to customer support. It can now be used in different business areas, such as sales and marketing, generating leads, and providing human resource recruitment services.
But with opportunities come risks, like customer dissatisfaction, security breaches, and data breaches. This is why businesses must follow best practices like implementing proper security measures when adopting AI.
In this article, we will discuss how AI chatbots transform businesses, their evolution, and associated problems.
Chatbots have come a long way since their early days. The first chatbot, ELIZA, was created in the 1960s. It used simple rules and decision trees to simulate conversations. But these early bots had limitations. They could only respond to specific inputs and often failed to handle anything outside their programmed scripts.
The real change came with large language models (LLMs). These advanced AI systems transformed conversational chatbots. Modern chatbots can understand intent, process complex questions, and respond in a way that feels human. They can even follow the flow of a conversation and build on earlier exchanges. These improvements have made AI chatbots a perfect fit for today’s businesses.
Here’s a quick example: a chatbot assisting with online shopping can remember a customer’s preferences, like size or color. Based on those selections, it can suggest suitable products.
Conversational AI solutions have become valuable for businesses across industries. They help save time and money and improve customer experiences. Let’s look at some real-world success stories to see how companies benefit from the use of chatbots:
Sephora was among the first businesses to use chatbot technology. This began with the Kik messaging app and then expanded to Facebook Messenger. The chatbot helps customers book beauty consultations and make product recommendations. This innovation paid off, increasing customer bookings by 11%.
LambdaTest used Zoho SalesIQ to create a customer assistance chatbot and improve its support coverage. Their support team became 40% more efficient, and the average response time was reduced to less than sixty seconds. This significantly improved the provision of timely and dependable client service.
Erica is a virtual assistant launched by the Bank of America in 2018. It helps customers manage their finances. It can track subscriptions and notify users about deposits or refunds. By November 2023, Erica had reached 40 million users, and by April 2024, it had handled 2 billion interactions. This shows how chatbots can scale to meet massive customer demand.
AI chatbots offer various opportunities to enhance business operations and customer experiences. They make personalized recommendations and improve business efficiency. Below, we’ll go over the opportunities AI chatbots provide businesses.
Modern chatbots are capable of understanding users' preferences and purchase patterns. They can analyze past interactions to offer tailored recommendations based on chat and cart history. For example, an e-commerce chatbot might suggest items similar to ones a customer has previously purchased.
Virtual assistants can also access personal information, like medical history or finances, to provide relevant advice and recommendations. This level of personalization keeps customers engaged longer and increases satisfaction and sales chances.
Unlike human operators, chatbots can handle thousands of user queries simultaneously without needing breaks or shifts. They provide immediate support, so users don’t have to wait for a human to respond. This efficiency is valuable during busy times, such as product launches, where a chatbot can manage a high volume of inquiries.
For example, the Mytime Active AI assistant helped reduce the number of incoming calls to the contact team, increasing overall efficiency and improving customer experience (CX). The contact team was no longer overwhelmed, and customers consistently received a positive experience.
Human support agents typically have limited domain knowledge, which can result in customers having to repeat their questions or be transferred between agents. However, an LLM-based chatbot can learn and retain information across all domains. This helps provide consistent and accurate answers to a wide range of queries without transferring the customer.
This improves response times and reduces customer frustration by providing immediate, reliable answers in one place.
LLM-based chatbots are multilingual, allowing businesses to serve a global audience. They can remove language barriers and provide better customer experiences for diverse customers. Several multilingual chatbots are available to support global customer service.
For instance, chatbots like Intercom and LivePerson offer multilingual features to help businesses engage with customers in their preferred languages.
Modern chatbots are relatively inexpensive and can be deployed quickly. Many chatbot providers offer per-interaction pricing models, which makes them a cost-effective solution for businesses.
They can also be set up using no-code tools and integrated with existing systems. This saves time and money compared to hiring new staff or setting up traditional customer service channels.
Chatbots can be implemented on messaging applications like Facebook Messenger or WhatsApp. This makes it easy for customers to start interactions without leaving their preferred app.
For instance, a customer may engage with a brand’s chatbot on an instant messaging platform such as WhatsApp and then proceed to the company’s website. This enhances usability as it facilitates interaction across various platforms in a simplified and individualized manner.
AI chatbots have found multiple applications across various industries. Some use cases of AI chatbots in business include:
As businesses increasingly adopt AI-powered chatbots and agentic AI for business process automation, they gain efficiency but also face notable risks. From data breaches to misinformation, the potential downsides can harm a company’s reputation and operations if not carefully managed.
Here are the risks of chatbot implementation and the broader use of AI agents in business process automation:
AI systems often have access to sensitive customer and business data. This makes them prime targets for hackers. Attackers may exploit vulnerabilities to access confidential information. They use jail-breaking techniques to force the bot to leak sensitive information. This could include sensitive customer details or internal business data. Customers, too, may hesitate to trust these systems, fearing their data isn’t secure.
Recently, a data breach attack on an AI call center platform exposed over 10 million conversations, revealing interactions between operators, customers, and AI agents.
Automated systems can lead to customer frustration when poorly designed or insufficiently trained. Chatbots may fail to understand complex queries, leaving customers feeling ignored. Moreover, miscommunication caused by AI agents can erode trust and drive customers away.
For instance, a bot in a logistics company promising unrealistic timelines could result in delayed shipments and upset customers.
One significant risk of AI systems is their tendency to hallucinate or generate misinformation. Misinformation can be costly for businesses as it misguides customers. For instance, hallucinations in healthcare support systems can lead to incorrect advice, potentially harming patients.
In financial services, misinformation could cost businesses heavily if customers make poor decisions based on inaccurate data. An e-commerce company may also risk losing revenue if a chatbot promises a discounted price due to incorrect programming.
AI systems rely on training data, which can sometimes contain biases. AI agents used in hiring or customer support may unintentionally discriminate based on gender, ethnicity, or other factors.
For example, biased training data could cause a chatbot in an HR application to sift resumes unjustly. This could affect a company’s reputation and isolate certain groups.
Companies are competing to push the boundaries by adding better and more distinctive features to their chatbots. Gone are the days of frustrating, robotic answers that felt distant and impersonal. Instead, the focus is now on creating personalized experiences that genuinely improve customer interactions.
And this is just the beginning. As chatbots continue to advance and refine their communication skills, we see more meaningful and positive customer interactions.
Here’s a closer look at current trends:
AI chatbots in business are helping automate consumer interactions and increasing productivity. However, businesses adopting AI chatbots must remain mindful of potential security risks and strike the proper balance between maximizing the benefits and ensuring chatbot security.
Here’s a quick recap of what we've learned:
Need help enhancing your AI chatbot’s security? Lakera can help.
With Lakera’s advanced threat detection and real-time response capabilities, you can ensure your AI chatbots operate securely and as expected. Lakera’s platform offers:
Book a demo today to see how Lakera can protect your business-critical GenAI initiatives.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Get the first-of-its-kind report on how organizations are preparing for GenAI-specific threats.
Compare the EU AI Act and the White House’s AI Bill of Rights.
Get Lakera's AI Security Guide for an overview of threats and protection strategies.
Explore real-world LLM exploits, case studies, and mitigation strategies with Lakera.
Use our checklist to evaluate and select the best LLM security tools for your enterprise.
Discover risks and solutions with the Lakera LLM Security Playbook.
Discover risks and solutions with the Lakera LLM Security Playbook.
Subscribe to our newsletter to get the recent updates on Lakera product and other news in the AI LLM world. Be sure you’re on track!
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.
