Cybersecurity Risks of AI Language Models: How Companies Can Mitigate Data Leakage and Protect Sensitive Information
The widespread utilization of expansive language models (LLMs), including OpenAI’s ChatGPT, has ignited substantial debate and apprehension in the corporate landscape. While these AI-driven services can undoubtedly enhance efficiency and simplify tasks, there are growing concerns regarding the potential compromise of data privacy and security. With more employees embracing LLMs to accelerate their work, companies must weigh potential risks and adopt proactive measures to safeguard their sensitive data.
One of the foremost concerns tied to LLMs is the potential for data leakage. Security professionals and businesses fear that sensitive information fed into these systems might be integrated into their models and later accessed if data security measures are inadequate. A recent study by Cyberhaven, a data security provider, identified and blocked requests to input data into ChatGPT from 4.2% of the 1.6 million workers at its client companies. These actions stemmed from concerns about exposing confidential information, client data, source code, or regulated information to the LLM, highlighting a substantial risk for businesses.
Moreover, studies have demonstrated that “training data extraction attacks” can successfully retrieve verbatim text sequences, personally identifiable information (PII), and other sensitive data from LLMs. In a study conducted by researchers from Apple, Google, Harvard University, and Stanford University, it was discovered that LLMs could memorize verbatim data from a single document. The data leakage risks are not merely theoretical but pose real and potentially severe consequences for businesses.
These risks have prompted some companies to take action. JPMorgan has limited its employees’ use of ChatGPT, while other major corporations like Amazon, Microsoft, and Walmart have urged their staff to exercise caution when utilizing generative AI services. Additionally, law firms recommend that companies incorporate restrictions on employees referring to or inputting confidential, proprietary, or trade secret information into AI chatbots or language models in their confidentiality agreements and policies. Implementing such measures could help lower the risk of data leakage and protect sensitive information from unauthorized access.
Despite the risks, LLMs’ popularity continues to rise. OpenAI’s ChatGPT and its underlying AI model, the Generative Pre-trained Transformer or GPT-3, have gained immense popularity in recent months, with over 300 developers using GPT-3 to power their applications. Companies like Snap, Instacart, and Shopify are all employing ChatGPT via the API to incorporate chat functionality into their mobile applications. The growing popularity of these tools demands that businesses proactively manage the associated risks.
A significant challenge for companies is educating their employees about the risks associated with AI-based services. Many workers view LLMs as a convenient solution to expedite their tasks, often without comprehending the potential hazards of submitting sensitive data to such systems. Proper education is essential for risk mitigation, and businesses should invest in both classroom and in-context training to help employees understand the risks and take appropriate steps to protect sensitive information.
In addition to education, companies can implement proactive measures to minimize data leakage risks. For instance, incorporating prohibitions on employees inputting confidential, proprietary, or trade secret information into AI chatbots or language models in their confidentiality agreements and policies can be beneficial. Businesses should also establish robust policies and agreements to safeguard sensitive data and restrict access to personal information and other confidential data. OpenAI and similar companies could develop a “corporate” tier compliant with HIPAA/SOC2 or other standards and charge a premium to alleviate corporate customers’ concerns.
Another crucial aspect for companies to consider is using APIs to connect their applications to LLMs. While this can be a powerful tool, it is vital to ensure that the LLM does not collect more information than users or their companies are aware of, as this could expose them to legal risks. Karla Grossenbacher, a partner at law firm Seyfarth Shaw, has cautioned that AI-based services might pose risks by storing information in their cloud, raising concerns for journalists and putting companies at risk of legal action.
As LLM usage continues to expand, it is crucial for companies to establish a clear strategy for managing the risks associated with these powerful tools. This may involve implementing robust data security measures such as encryption, multi-factor authentication, and access controls to protect sensitive data from unauthorized access. Collaborating closely with legal and compliance teams is also essential to ensure full compliance with all relevant regulations and standards.
It is important to note that the risks associated with LLMs are not confined to data leakage. As LLMs become more advanced, there is a growing risk of bias and discrimination in their outputs. This could be particularly problematic in industries like healthcare and finance, where the stakes are high and the potential consequences of bias can be severe. Companies need to be aware of these risks and take proactive steps to mitigate them, such as incorporating ethical considerations into their AI development processes.
Concerns surrounding data security with LLMs like ChatGPT are valid and should not be underestimated. It is essential to find ways to mitigate these risks and protect sensitive information from being leaked. Companies must invest in employee education, implement strong policies and agreements, and work closely with legal and compliance teams to safeguard their data and maintain compliance with regulations. By taking these steps, businesses can harness the benefits of LLMs while minimizing potential risks and ensuring the protection of their sensitive data.
By fostering a culture of data security awareness, companies can encourage responsible use of AI tools among their employees. This involves promoting best practices for handling confidential information and providing guidelines on the appropriate use of LLMs. Additionally, businesses should conduct regular audits and reviews of their data security practices to identify potential vulnerabilities and address them proactively.
Collaboration with AI developers and vendors is another crucial aspect of mitigating risks associated with LLMs. By working together, companies and AI providers can develop solutions that prioritize data privacy and security while still offering the benefits of advanced language models. This could include the implementation of AI models with built-in privacy measures, such as federated learning or differential privacy, to ensure that sensitive data remains protected even during the model training process.
Successful integration of LLMs into the corporate environment requires a multifaceted approach that balances the benefits of these powerful tools with the need to protect sensitive information and maintain compliance with data protection regulations. By investing in employee education, implementing strong data security policies, collaborating with AI developers and vendors, and continuously monitoring their data security practices, businesses can leverage the power of LLMs like ChatGPT while safeguarding their most valuable assets.
Based on expert opinions, it’s clear that concerns about data security and potential exploits with LLMs like ChatGPT are legitimate. Some experts believe it’s only a matter of time before a major exploit occurs, with sensitive data spilling out from the training data when prompting the model.
Furthermore, OpenAI is openly storing all the data it collects, and has had several leaks already. This has left many wondering if an exploit of their systems is imminent, and whether it could leak a monumental amount of data from users. In the most innocent case, it could lead to the personal info of naive users being leaked. But let’s be real here, the business world is filled with people who genuinely believe that AI is free-thinking and better than their own employees. For every organization that restricts ChatGPT use, there are fifty others that don’t, most of which have at least one person who is ready to upload confidential data at a moment’s notice.
It’s not just businesses that should be concerned, though. There’s a very real possibility that military personnel could be putting sensitive information into ChatGPT. OpenAI needs to include more brazen warnings against providing this type of data if they want to keep up this facade of “we can’t release it because ethics.” Cybersecurity is a much more real liability than a supervised LM turning into the Terminator.
Some may argue that other companies, like Microsoft, also have access to large amounts of data, but the fear of OpenAI leaking data seems to be more widespread. We need to be intellectually honest with ourselves and address the risk of data leaks through bugs as an equal threat. OpenAI uses Azure behind the scenes, so it should be just as solid (or not solid) as most other cloud-based tools. If there’s a fear that OpenAI will train their model on your data submitted through their textbox toy, but not through training on the troves of private corporate data, then that fear is unwarranted too.
When it comes to cases like the executive who cut and pasted the firm’s strategy document into ChatGPT, there’s not much you can do other than shake your head in disbelief. That level of basic common sense is lacking, and having someone like that in your business, particularly at the executive level, is a liability regardless of ChatGPT. Companies need to proactively block both ChatGPT and any website serving as a wrapper over it to prevent unauthorized data input.
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