Understand the risks of generative AI for better business outcomes

New technology, when used properly, can be a great asset to improve or transform your business environment. And if misused, it can pose a significant risk to your company. In this respect, ChatGPT is no different than any other generative AI model. Generative AI models are poised to transform a variety of business areas, improving their ability to engage customers and internal processes and drive cost savings. However, it can also pose serious privacy and security risks if used improperly.

ChatGPT is best known for the current generation of generative AI, but there are several others, including VALL-E, DALL-E 2, Stable Diffusion, and Codex. They are created by feeding “training data” that includes a variety of data sources, such as queries generated by companies and their customers. The resulting data lake is the “magic source” of generative AI.

In enterprise environments, generative AI has the potential to revolutionize work processes while building ever-closer relationships with target users. Still, companies should know what they’re going to do before they start. Like any new technology adoption, generative AI increases organizational risk. Proper implementation means understanding and controlling the risks associated with the use of tools that feed, transmit, and store information that primarily originates outside the company walls.

A chatbot for customer service that effectively uses generative AI

One of the biggest areas of potential material improvement is customer service. Program generative AI-based chatbots to answer frequently asked questions, provide product information, or help troubleshoot customer issues. This allows us to improve our customer service in several ways. That means delivering 24/7 “staffing” faster, cheaper and at scale.

Unlike human customer service representatives, AI chatbots can provide 24/7 help and support without taking breaks or vacations. They can also process customer inquiries and requests much faster than human agents, reducing wait times and improving the overall customer experience. The cost effectiveness of using chatbots for this business purpose is obvious as less manpower is required and a high volume of inquiries can be handled at a lower cost.

Chatbots use well-defined data and machine learning algorithms to personalize customer interactions and tailor recommendations and solutions based on individual preferences and needs. All of these response types are scalable. AI chatbots can handle a large number of customer inquiries simultaneously, allowing businesses to easily handle spikes in customer demand and high volume of inquiries during peak hours.

To use AI chatbots effectively, companies must have clear goals in mind, use AI models appropriately, and have the necessary resources and expertise to effectively implement AI chatbots. you need to make sure that Alternatively, you should consider partnering with a third party. A provider specializing in AI chatbots.

It is also important to design these tools with a customer-centric approach, such as being easy to use, providing clear and accurate information, and being able to respond quickly 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 so, businesses can improve customer service, increase customer satisfaction, and drive long-term growth and success.

We need to visualize the risks of generative AI

To enable transformation while preventing increased risk, companies need to be aware of the risks posed by the use of generative AI systems. This depends on your business and proposed use. Regardless of intent, there are a number of universal risks. The main risks are information leakage or theft, lack of control over output, and lack of compliance with existing regulations.

Companies using generative AI risk sensitive or confidential data being accessed or stolen by unauthorized third parties. This can occur through hacking, phishing, or other means. Similarly, misuse of data is also possible. Generative AI can collect and store large amounts of data about users, including personally identifiable information. If misused, this data can be used for malicious purposes such as identity theft and fraud.

All AI models generate text based on their training data and the input they receive. Companies may not have full control over their output, and sensitive or inappropriate content may be exposed during conversations. Information accidentally included in conversations with generated AI risks being disclosed to unauthorized parties.

Generative AI can also generate inappropriate or objectionable content, which can damage a company’s reputation or cause legal problems if shared publicly. This can happen when AI models are trained with inappropriate data or programmed to generate content that violates laws and regulations. To this end, businesses must ensure that they comply with regulations and standards related to data security and privacy, such as GDPR and HIPAA.

In extreme cases, a generative AI can be maliciously or incorrectly manipulated with the intent that a malicious person manipulates the underlying data used to train the generative AI to produce harmful or undesirable results. may become. This is a practice known as “data poisoning”. Attacks against the machine learning models that underpin AI-driven cybersecurity systems can lead to data breaches, information disclosure, and widespread brand risk.

Controls help reduce risk

To mitigate these risks, companies can limit the types of data that enter generative AI, implement access controls to both AI and training data (i.e. restrict who can access it), Some steps can be taken, such as implementing a continuous monitoring system. content output. Cybersecurity teams should consider using strong security protocols, such as encryption to protect data and additional training for employees on data privacy and security best practices.

Emerging technologies enable you to achieve your business goals while improving the customer experience. Generative AI is poised to transform many customer-facing business lines of companies around the world and should be adopted for its cost-effective benefits. However, business owners should be aware of the risks AI poses to their organization’s operations and reputation, and the potential investments associated with proper risk management. If the risks are properly managed, there is a huge opportunity to successfully implement these AI models into daily operations.

Eric Schmitt is Sedgwick’s Global Chief Information Security Officer.