How to Prevent Generative AI Data Leakage

Strategies to Prevent AI Driven Data Leakage

To prevent leakage of data as a result of AI changes or misuse, organizations should put in place the most holistic approach: 

1. Data Classification & Continuous Monitoring: All sensitive data is catalogued and classified as PII, intellectual property, or any financial or legal documents. Automated discovery tools can be employed or procured to create a map of where the data is stored, how it flows, and who has access to see it  in order to continue to stay situationally aware. To increase protection, one must revisit the data inventories periodically to refresh them with new sensitive data content as it occurs. 
2. Strong Access controls: Implement role-based access control (RBAC) in conjunction with the zero trust framework so that users can only access data necessary to perform their job functions. Strong authentication should be in place for all generative AI tools, including multifactor authentication (MFA) and single sign-on (SSO). A micro-segmentation model, layered with tight network policies to minimize attack surfaces to be followed.
3. Approved and Secured AI Tool Management: The criteria for data privacy, vendor conditions, encryption and audit logging should be established.  A central and updated repository of approved generative AI tools to be maintained, prioritizing deployment on private cloud or on-premises for sensitive data. 
4. Shadow AI and Detection & Policy Enforcement: As these sorts of tools become more ubiquitous it is essential to have policies that not only change the situations and environments but also specify the tasks for which generative AI tools of any kind are permitted, especially sensitive ones. In addition, at the network level, regulations banning access to dangerous AI areas and maybe tracking usage and history would be quite helpful in overseeing activities related to the tools. 
5. Enhanced DLP for GenAI: To raise GenAI DLP to a higher level, the standard DLP devices currently used need to be replaced by more advanced DLP devices capable of detecting GenAI threats, such as vector storage leaks and/or predictive risk exposure. The improved real-time content inspection function will allow to quickly discover the sensitive materials communicated by AI interactions and systems, thus a mere reliance on metadata analysis will be outdated. 
6. Continuous Monitoring, Auditing, and Risk Assessment: Every single time a generative AI tool is used the instance must be logged so as to produce the detailed audit trails that are imperative to compliance and peace of mind to a forensic investigation team. Implement real-time monitoring of users’ activities through analytics and anomaly detection to detect any illicit use or materials extraction attempts by a generative AI tool. 
7. Employees Training and Cultural Changes: The employees must be made aware of the potential data violations through generative AI and at the same time be able to recognize the confidential nature of the data. Quite a few examples of ethical AI applications can be given and the types of data that must not be disclosed can be listed. 

In 2023, the private code that belonged to Samsung’s semiconductor company was hacked to infiltrate a publicly available AI application online. What is interesting is that the AI application in question was open to the public. This shows the importance of having policies, governance, and education enforcement.

How Lepide Helps

The Lepide Data Security Platform  provides organizations with the ability to use generative AI in a secure manner via insightful data and user identity governance. Our platform scans continuously across Active Directory, Microsoft 365, and file servers to provide visibility to where sensitive data resides and who has access to that data. Lepide also has an integrated permissions auditing feature that is straightforward and takes seconds to revoke excessive permissions that would allow unintended access to sensitive data, if applicable, to a least privilege access policy prior to launching an AI tool such as Copilot. 

Once the AI tool is implemented, Lepide continues its governance with generative AI to ensure a safe implementation. We monitor and alert on user behaviors that are anomalous, track changes to permission in Active Directory, and produce detailed audit reports, in addition to many other features that provide assurance that governance is being maintained. This provides organizations with the ability to use these AI assistants with confidence by preventing sensitive data from being potentially exposed as well as leveraging valuable tools that will create ongoing compliance needs. 

Lepide will provide a secured framework for allowing the utilization of AI through data discovery, access management, and continual audit trails. Working with Lepide will allow organizations to adopt generative AI capabilities knowing sensitive data is protected, privileges have been restricted or assigned and any risks of adopting generative AI via plugins or other methods are able to be assessed and mitigated before that risk becomes consequential.

Start a free trial now or arrange a demo with one of our engineers to discuss proven methods to prevent AI-enabled data breaches. 

Frequently Asked Questions( FAQs)

Q1. Why is it crucial to prevent data leaks from generative AI?

Financial loss, regulatory risk (GDPR, HIPAA, etc.), damage to the company’s reputation, and the disclosure of possible trade secrets and intellectual property are all possible outcomes of data leaks. So, its crucial to prevent data leaks from generative AI.

Q2. How could employees unknowingly facilitate data leakage using generative AI tools?

Using AI-generated work products without testing against compliance criteria, asking an AI device to analyse or help with private emails, sharing unreduced customer or individual data, and copying sensitive and/or private documents into an AI discussion are the first three.

Q3. What if you believe data leakage is taking place?

Notify your security or compliance teams immediately, act according to your company’s incident response policy, and document what data was posted to the AI and using what tool.