This post was originally published on this site.
Written by Sean Arakawa
As organizations worldwide integrate generative artificial intelligence (gen AI) into daily operations, it is crucial they ensure the security of gen AI to protect intellectual property (IP) and sensitive data. The rapid expansion and evolution of gen AI requires that organizations proactively identify and address the associated risks to sensitive company data, trade secrets, and user privacy. Failure to do so could result in data exposure to competitors, hackers, and other threats.
This article will cover the following topics on securely using gen AI for your organization:
- Preventing Shadow IT
- Enterprise Grade Tools
- Guardrails via Data Loss Prevention
- User Training
Preventing Shadow IT
Shadow IT describes when staff use software, hardware, or services that are banned from use by the organization’s IT department for security reasons. However, it is generally impossible to completely stop users from accessing and using gen AI tools because of their universal accessibility via web and mobile apps. Depending on an organization’s security posture, users can move company data to non-authorized, personal devices, which can increase the risk to your organization’s data.
Given this reality, organizations should provide staff with a company sanctioned and secure enterprise gen AI platform to perform job duties. This will help your organization minimize risk due to data loss, and users will appreciate that they have access to a sanctioned gen AI tool.
Enterprise Grade Tools
There are a slew of new gen AI tools on the market, and more are added every day. Some are geared towards enterprises, while some are better suited for consumer use. To maximize the safety of your data and systems, it is critical that your organization use enterprise-grade gen AI tools and platforms. Enterprise-grade platforms have security options that help limit access to corporate users and devices, and they can integrate with other existing organizational security technologies and platforms like single sign-on (SSO). Consumer-grade tools do not provide this level of customization and safety, leaving your organization exposed to excess risk.
Likewise, enterprise-grade tools protect sensitive company data much more effectively than consumer-grade tools. Some tools do not provide the ability to opt out of using your organization’s data for AI training purposes. Consider a scenario where your organization’s essential IP is entered into an AI platform, which is then used to train the AI’s answers to future prompts. A competitor could receive an answer to an AI prompt that leverages this essential IP, and they can incorporate this IP into their own business strategy. Some gen AI programs anonymize inputted IP but considering how crucial that data might be to a business, it is best to opt out of data sharing in the first place.
Lastly, organizations can configure enterprise-grade gen AI tools to ensure data is for your organization’s use only. This provides better assurance that your data is kept confidential, but it is always better to add as many security layers as possible to protect your organization’s data, such as the guardrails mentioned in the next section.
Guardrails Via Data Loss Prevention
As previously mentioned, using and configuring enterprise-grade gen AI tools can help protect your organization’s data, but adding and configuring additional “guardrail” tools can add further protection. One such tool is data loss prevention (DLP) platforms, which control what data can be entered or exported into gen AI. Without a DLP platform, an organization lacks control over data inputs and relies on their users’ good will to follow organizational policies.
It is important that your DLP platform can monitor and control the data used with the gen AI tool, whether that is achieved through direct integration into the gen AI tool or an agent-based solution installed on corporate endpoints. Organizations should configure DLP platforms to label and identify corporate data and restricted data, which is blocked from upload or export. The DLP policies should align with the overall organization’s gen AI policy and strategy.
User Training
Upon providing a gen AI tool or platform for your organization, train users on how to use the tool(s) in conjunction with the organization’s policies and strategy. Training requirements are best created before providing access to AI tools, and requirements should include acknowledgement of the organization’s policies and guidelines for using gen AI. At a minimum, users should understand how to access the platform, what data is permitted to be uploaded, and how to use basic AI prompts. Training your staff on gen AI best practices is a primary safeguard to keep sensitive information from leaking outside your organization.
Conclusions
If your organization has integrated gen AI into the enterprise environment, it is crucial to secure your data with the strategies mentioned above. Organizations need to stay vigilant with data security and privacy concerns, both with legacy systems and new technologies such as gen AI to stay competitive in the ever-evolving business landscape.
If you or your organization have questions regarding AI tools and securing their use for your organization, please reach out to start a conversation.
Sean Arakawa is an Information Technology senior manager at Clark Nuber.
© Clark Nuber P.S., 2025. All rights reserved.