Generative AI has a transformative impact across nearly all industries and applications. Large Language Models (LLMs) have revolutionized natural language processing, enabled conversational agents, and automated content generation. In healthcare, LLMs promise to aid in drug discovery, as well as personalized physical and mental treatment recommendations. In the creative realm, generative AI can generate art, music, and design, pushing the boundaries of human creativity. In finance, it assists in risk assessment, fraud detection, and algorithmic trading. With versatility and innovation, generative AI will continue to redefine industries and drive new possibilities for the future.
First brought to market at the end of November 2022, ChatGPT had about 266 million visits by December and 1 million active users in the first 5 days — a record adoption rate for any application at that time. In April 2023, the site received about 1.76 billion visits, according to analytics company Similarweb. At no point in history had any software been so rapidly and enthusiastically embraced by individuals across all industries, departments, and professions.
However, enterprises across the globe find themselves unable to empower large-scale, safe, and controlled use of generative AI because they are unprepared to address the challenges it brings. The consequences of data leakage are tremendous, and heroic innovation for data protection to accelerate, foster, and ensure safe usage is now imperative.
Fortunately, technical solutions are the best path forward. Generative AI’s utility overrides employees’ security concerns, even when enterprises have clear policies guiding or preventing the use of the technology. Thus questions such as “How to prevent data leakage” are useless as employees continue to use Generative AI tools regardless of privacy concerns. For example, tech giant Samsung recently reported that personnel used ChatGPT to optimize operations and create presentations, resulting in Samsung’s trade secrets being stored on ChatGPT servers.
While these sorts of incidents are alarming to an enterprise, they have not stopped their employees from wanting to leverage the efficiencies offered by Generative AI. According to Fishbowl, 70% of employees leveraging ChatGPT for work haven’t disclosed their usage to management. A similar report by Cyberhaven shows that 11% of workers have put confidential company information into LLMs. Employees use alternate devices, VPNs, and alternate generative AI tools to circumvent corporate network bans blocking access to these productivity-enhancing tools. As such, privacy preservation in big data has become one big game of whack-a-mole.
Many generative AI and LLM providers have been relying solely on contractual legal guarantees (such as Terms of Service) to promise no misuse of the Generative AI data that gets exposed to the providers and their platforms. Litigation against these providers is proving expensive, uncertain, and slow. Many causes of action will likely go undiscovered, as the use of leaked information can be difficult to detect.
How to Leverage Generative AI Data Safely and Successfully
Safeguarding your data in the generative AI era will require ongoing vigilance, adaptation, and active solutions. By taking the steps outlined below today, you can prepare your organization for whatever this new era brings, seizing the opportunities while navigating the challenges with confidence and foresight.
1. Understand your AI landscape inventory.
Conduct a comprehensive assessment of current and potential generative AI usage for your organization. Include departments such as IT, HR, Legal, Operations, any other departments that may be utilizing AI, as well as your AI teams, privacy, and security experts.
Document all the ways AI is being (and could be) used, such as search, summarization, chatbots, internal data analysis, and any AI tools that are currently implemented — both approved and unapproved. Be sure to include any third party AI systems (or systems that are using embedded AI functionality) your company relies on.
For each application, identify the potential data risks. These include exposure of confidential information and trade secrets, security vulnerabilities, data privacy issues, potential for bias, possibilities of misinformation, or negative impacts on employees or customers. Evaluate and prioritize the risks, identify and prioritize mitigation strategies, and continually monitor their effectiveness.
2. Design solutions with a clear focus on data protection.
Despite everybody’s best security efforts, data breaches can and will happen. In addition to the data governance and access controls that prevent unnecessary data exposure inside your organization, it’s now essential to incorporate fail-safe solutions that prevent unprotected data from being exposed to the generative AI tools that live outside of your organization (unprotected data is data that’s in a human-understandable form like plain text or images). Partner with generative-AI companies that enable you to maintain the ownership of your plain-text data.
3. Educate and train your workforce.
Your employees are a crucial element when addressing how to prevent data leakage. Invest in their education and training and encourage them to familiarize themselves with the concepts, tools, and best practices related to generative AI, but do not rely on them to be foolproof. Foster a culture that embraces AI and is aware of its implications while safeguarding against its inherent risks.
As a16z’s Marc Andreesen recently wrote: “AI is quite possibly the most important — and best — thing our civilization has ever created, certainly on par with electricity and microchips.” It’s now clear that the future of business will be undeniably intertwined with generative AI.
You have the power to leverage the advantages offered by generative AI while proactively securing the future of your organization. By adopting forward-looking solutions, you can ensure data protection as you forge the path to this revolutionary future.