The utilization of generative AI tools has become increasingly prevalent, with almost half of individuals having used these tools and a significant portion using them on a daily basis. While generative AI offers numerous benefits, the unsanctioned use of these tools within organizations, known as shadow AI, poses considerable risks and challenges.
Tech giants like Amazon have eagerly embraced AI tools such as ChatGPT for business purposes, but incidents like Samsung’s accidental data leak have led to certain companies banning the use of these tools. Concerns around sensitive information sharing have prompted banks like Goldman and Citigroup to restrict AI use as well.
Shadow AI presents a new threat vector in the realm of shadow IT, posing unknown threats for security and compliance. While shadow IT is generally manageable once identified, shadow AI carries more elusive risks that are difficult to quantify and control. It not only involves the unauthorized use of AI tools but also encompasses the unsanctioned utilization of company data in unregulated spaces.
Moreover, the accessibility and potential productivity gains associated with AI tools make it tempting for employees across various roles to feed company data into unauthorized tools. This not only jeopardizes sensitive information but also exposes valuable intellectual property to potential risks.
Addressing the challenges posed by shadow AI requires a multi-faceted approach. While traditional methods of managing and monitoring data activities are crucial, they are insufficient for defending against shadow AI outside the boundaries of data centers. Implementing an outright ban on AI use is also impractical, as employees may find ways to discreetly employ these tools for their benefit.
To mitigate the threats of shadow AI, IT leaders can take several additional steps:
1. Educating employees about the risks: A critical initial step is to educate the workforce about the threats and implications of unsanctioned AI use. By being specific about the risks involved, such as feeding sensitive information into a black box AI and the lack of transparency regarding AI failures, organizations can create awareness and demonstrate the reality of these risks.
2. Updating AI policies and processes: Instead of a blanket ban, organizations can update their policies to include specific AI restrictions and guidance for acquiring approval for legitimate business needs. This allows for a regulated route for AI use, enabling use cases to be reviewed, risk-assessed, and approved or denied accordingly.
3. Implementing endpoint security tools: Organizations should adopt endpoint security tools to enhance visibility into all AI use and reduce risks at the user level. This includes technologies like Cloud Access Security Brokers (CASB) to address the endpoint and remote workers, thereby providing control and visibility over shadow AI usage.
4. Establishing end-user agreements with AI vendors: Similar to traditional end-user license agreements (EULAs), companies can implement agreements with AI vendors to set parameters on the use of data within AI models and platforms. This helps establish clear boundaries and guidelines while fostering collaborative and transparent communication with AI vendors.
Looking ahead, the challenges associated with shadow AI are anticipated to worsen as the implementation of AI tools outpaces organizations’ ability to secure them. However, with time, appropriate policies and training can be implemented to ensure the correct and secure utilization of data within AI models. As awareness grows, more solutions and companies are expected to emerge, addressing the concerns posed by shadow AI.
FAQ Section:
Q: What is shadow AI?
A: Shadow AI refers to the unsanctioned use of artificial intelligence tools within organizations, including the unauthorized use of AI tools and the utilization of company data in unregulated spaces.
Q: What are the risks of shadow AI?
A: Shadow AI poses risks such as data breaches, unauthorized access to sensitive information, and exposure of valuable intellectual property to potential threats.
Q: How can organizations address the challenges of shadow AI?
A: Organizations can take several steps to mitigate the threats of shadow AI, including educating employees about the risks, updating AI policies and processes, implementing endpoint security tools, and establishing end-user agreements with AI vendors.
Q: How can organizations educate employees about the risks of unsanctioned AI use?
A: Organizations should be specific about the risks involved, such as feeding sensitive information into a black box AI and the lack of transparency regarding AI failures. This can create awareness and demonstrate the reality of these risks.
Q: What can organizations do to update AI policies and processes?
A: Instead of a blanket ban, organizations can include specific AI restrictions and guidance for acquiring approval for legitimate business needs. This allows for regulated AI use, where use cases can be reviewed, risk-assessed, and approved or denied accordingly.
Q: How can organizations enhance visibility and reduce risks at the user level?
A: Organizations should adopt endpoint security tools, such as Cloud Access Security Brokers (CASB), to provide control and visibility over shadow AI usage, especially for endpoint and remote workers.
Q: How can organizations establish clear boundaries and guidelines with AI vendors?
A: Companies can implement end-user agreements with AI vendors, similar to traditional end-user license agreements (EULAs), to set parameters on the use of data within AI models and platforms. This helps establish clear boundaries and guidelines while fostering communication and transparency with AI vendors.
Key Terms:
– Generative AI: Refers to AI tools that can generate new content, such as text or images, based on patterns and examples provided to them.
– Shadow AI: The unsanctioned use of AI tools within organizations, including the unauthorized use of AI tools and the utilization of company data in unregulated spaces.
– Shadow IT: The use of unauthorized software or hardware within organizations without the knowledge or approval of IT departments.
– Endpoint Security: The practice of securing endpoints, such as computers, mobile devices, and IoT devices, to prevent unauthorized access and protect against cybersecurity threats.
Related Links:
Amazon – Information on the AI tools used by tech giants like Amazon for business purposes.
Goldman Sachs – Goldman Sachs website for information on their approach to AI and technology.
Citigroup – Citigroup website for information on their stance regarding AI use and security.
The source of the article is from the blog hashtagsroom.com