Technology Understand the risks of generative AI for better business...

Understand the risks of generative AI for better business outcomes


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Any new technology can be a great asset to improve or transform business environments if used properly. If misused, it can also pose a material risk to your business. ChatGPT and other generative AI models are no different in this regard. Generative AI models are poised to transform many different areas of business and can improve our ability to engage with our customers and our internal processes and deliver cost savings. But they can also pose significant privacy and security risks if not used correctly.

ChatGPT is the best known of the current generation of generative AIs, but there are others such as VALL-E, DALL-E 2, Stable Diffusion and Codex. These are created by giving them “training data,” which can include various data sources, such as queries generated by companies and their customers. The data lake that emerges is the “magic sauce” of generative AI.

In an enterprise environment, generative AI has the potential to revolutionize work processes while creating a closer connection than ever with the target users. Still, companies need to know what they’re getting into before they start; as with any new technology, generative AI increases an organization’s risk exposure. Proper implementation means understanding and managing the risks associated with using a tool that feeds, transports, and stores information that usually comes from outside the company walls.

Customer service chatbots are effective applications of generative AI

One of the biggest areas for potential material improvement is customer service. Generative AI-based chatbots can be programmed to answer frequently asked questions, provide product information and help customers solve problems. This can improve customer service in several ways, namely by providing faster and cheaper 24-hour “staffing” at scale.


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Unlike human customer service representatives, AI chatbots can provide 24/7 assistance and support without taking any breaks or vacations. They can also process customer inquiries and requests much faster than human representatives, reducing wait times and improving the overall customer experience. Because they require less staff and can process a greater number of inquiries at a lower cost, the cost-effectiveness of using chatbots for this business purpose is obvious.

Chatbots use properly defined data and machine learning algorithms to personalize customer interactions and tailor recommendations and solutions based on individual preferences and needs. These response types are all scalable: AI chatbots can handle a large number of customer queries simultaneously, making it easier for companies to handle spikes in customer demand or large volumes of inquiries during peak periods.

To use AI chatbots effectively, companies need to ensure that they have a clear goal in mind, that they are using the AI ​​model correctly, and that they have the necessary resources and expertise to effectively implement the AI ​​chatbot – or consider partnering with a third-party provider that specializes in AI chatbots.

It is also important to design these tools with a customer-centric approach, such as ensuring they are easy to use, provide clear and accurate information, and respond to customer feedback and inquiries. Organizations should also continuously monitor AI chatbot performance using analytics and customer feedback to identify areas for improvement. By doing this, companies can improve customer service, increase customer satisfaction and drive long-term growth and success.

You have to visualize the risks of generative AI

To enable transformation while avoiding mounting risks, companies need to be aware of the risks of using generative AI systems. This depends on the company and the proposed use. Regardless of intent, a number of universal risks are present, the most important of which are information leaks or theft, lack of control over output, and non-compliance with existing regulations.

Companies using generative AI run the risk of unauthorized parties accessing or stealing sensitive or confidential data. This can happen through hacking, phishing or other means. Likewise, data misuse is possible: generative AIs can collect and store large amounts of data about users, including personally identifiable information; if this data falls into the wrong hands, it can be used for malicious purposes such as identity theft or fraud.

All AI models generate text based on training data and the input they receive. Companies may not have full control over the output, potentially exposing sensitive or inappropriate content during conversations. Information inadvertently included in a conversation with a generative AI poses a risk of disclosure to unauthorized parties.

Generative AIs can also generate inappropriate or offensive content, which can damage a company’s reputation or cause legal trouble if shared publicly. This can happen if the AI ​​model has been trained on inappropriate data or if it has been programmed to generate content that violates any law or regulation. To that end, companies must ensure they comply with regulations and standards related to data security and privacy, such as GDPR or HIPAA.

In extreme cases, generative AIs can become malicious or inaccurate if malicious parties manipulate the underlying data used to train the generative AI, with the intent to produce harmful or unwanted results – an act known as “data poisoning”. Attacks against the machine learning models that underpin AI-driven cybersecurity systems can lead to data breaches, information disclosure and broader brand risk.

Controls can help mitigate risks

To mitigate these risks, companies can take several steps, including limiting the type of data fed into the generative AI, implementing access controls to both the AI ​​and training data (i.e. limiting who can access it), and implementing a continuous monitoring system for content output. Cybersecurity teams will want to consider using strong security protocols, including encryption to protect data, and additional employee training on data privacy and security best practices.

Emerging technology makes it possible to achieve business objectives while improving the customer experience. Generative AIs are poised to transform many customer-facing industries in companies around the world and should be embraced for their cost-effective benefits. However, entrepreneurs should be aware of the risks AI poses to an organization’s operations and reputation – and the potential investment that comes with good risk management. If risks are properly managed, there are great opportunities for successful implementations of these AI models in day-to-day business operations.

Eric Schmitt is Global Chief Information Security Officer at Sedgwick.

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