Navigating the Cybersecurity Challenges of Generative AI in the Business Landscape

NNicholas September 5, 2023 9:22 PM

As businesses increasingly embrace generative AI tools like ChatGPT and DALL-E, they are facing complex cybersecurity and regulatory compliance concerns. While these tools offer exciting new capabilities such as automation and creative conceptualization, they also introduce significant data security risks and uncertainties around regulatory frameworks that are still in progress.

Rapid adoption of generative AI in businesses

Generative AI has moved from being a fringe technology to a highly sought-after tool in the corporate world. This is thanks to innovative applications like ChatGPT or DALL-E that have simplified its use, encouraging a broader acceptance across industries and age groups. Recent surveys show a growing number of Gen Z, Gen X, and Millennial employees are utilizing generative AI tools in their daily work. Predictions indicate that large-scale adoption of generative AI is set to double from 23% in 2022 to 46% by 2025.

Cybersecurity concerns around generative AI

Generative AI is a rapidly evolving technology that uses trained models and vast datasets to generate unique content, ranging from text and images to videos, music, and even software code. However, the swift pace of its adoption and the current lack of regulatory oversight are raising significant concerns regarding cybersecurity and regulatory compliance. Surveys reveal that a majority of people are concerned about the security risks posed by generative AI, with many advocating for a temporary halt in its development until regulations catch up.

Generative AI tools thrive on data, often drawing on external or freely available internet data. However, to maximize the efficacy of these tools, users often share sensitive business information. This practice introduces a risk of unauthorized access or unintentional disclosure of sensitive information, a risk that is inherently 'baked in' to the use of these freely available generative AI tools. These risks are yet to be thoroughly explored and understood, and legal and regulatory frameworks around generative AI use are still maturing.

While regulators are beginning to evaluate generative AI tools in terms of privacy, data security, and data integrity, the regulatory support for these emerging technologies is still several steps behind their widespread adoption. This regulatory lag creates a significant risk for businesses. As companies eagerly embrace the potential for automation and growth offered by these tools, risk managers face uncertainties regarding future regulations, potential legal implications, and the possibility of compromised or exposed data.

CISOs' role in governing generative AI

In the absence of comprehensive regulatory frameworks, Chief Information Security Officers (CISOs) must take a proactive role in managing the use of generative AI within their organizations. They need to understand who is using these tools and for what purpose, protect enterprise information during interactions with generative AI tools, manage the security risks of the underlying technology, and balance security trade-offs with the value these tools offer. This necessitates detailed risk assessments and thoughtful company policies regarding the use of freely available generative AI applications.

Data classification to mitigate generative AI risks

One approach to mitigating the security risks associated with generative AI is to focus on data classification and protection, rather than the tool itself. This involves assigning a level of sensitivity to data, which dictates how it should be treated: should it be encrypted, blocked, or notified of? Who should have access to it, and where should it be shared? By concentrating on the flow of data, CISOs and security officers can better manage some of the associated risks.

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