Artificial intelligence has revolutionized the way businesses are operating today. Although, such a way of working comes with inherent risks, especially when it comes to the protection of confidential data. Recent incident of Samsung’s software developers putting proprietary data on ChatGPT is the most talked about example. With generative AI systems like ChatGPT, which are designed to generate content based on input data using machine learning algorithms, the risks of unauthorized access to sensitive data, including proprietary data and company secrets, have increased significantly. So, let us explore the risks associated with generative AI and provide a holistic approach to safeguarding confidential data.
Generative AI systems like ChatGPT are not inherently equipped to distinguish between what is confidential and what is not. They are designed to generate content based on the input data provided, making them more of a derivative medium. As a result, there is always a risk of these systems generating content that could compromise the confidentiality of proprietary data or company secrets. This risk is even higher when generative AI systems are used outside of a company’s firewall, such as in the cloud or on third-party servers and more severly over the internet as free PaaS.
Generative AI systems, like ChatGPT, can be trained on massive amounts of data, making it easier for hackers to exploit vulnerabilities in these systems. For example, attackers can manipulate input data to generate malicious output that can compromise the confidentiality of sensitive information. As generative AI continues to advance, so too do the techniques used by attackers to exploit vulnerabilities. This highlights the importance of implementing a comprehensive security strategy that protects against a wide range of threats.
To safeguard confidential data from generative AI, businesses need to adopt a holistic approach that encompasses people, processes, and technology. Here are some solutions to consider:
People: Employees are the first line of defense against data breaches. Just like social engineering, It’s crucial to educate them on the importance of data security and confidentiality, as well as the risks associated with generative AI. Regular training, workshops, and clear guidelines on what data is confidential and how to handle it can help employees understand the importance of safeguarding confidential data. Remember, social engineering and generative AI, both lead to external logical people or system having access to the data a human poses.
Processes: Well-defined processes surrounding data management and access must be in place to ensure that sensitive data is protected. Regular reviews of these processes should be conducted to ensure that they align with industry best practices and changing business needs. This can include regular audits of user access to ensure that only authorized personnel can access sensitive data.
Technology: Technology plays a critical role in securing confidential data from generative AI. Companies should implement access controls, encryption, and monitoring tools to ensure that sensitive data is only accessible by authorized personnel and is being used appropriately. For example, access to confidential data can be restricted to specific IP addresses, and encryption can be used to protect data in transit and at rest. The DLP systems have to grow beyond file scanning and look at realtime clipboard access and analysis.
Lastly and conclusively, as generative AI becomes increasingly widespread, businesses must take steps to protect their sensitive data from unauthorized access or misuse. Failure to do so can result in significant financial and reputational damage. For example, a breach of proprietary data can lead to lost competitive advantage or even lawsuits from customers or partners. In addition, a data breach can damage a company’s reputation and erode customer trust.