5 Key Steps to Securing Data with Full GenAI Security

Since ChatGPT was introduced to the world less than two years ago, it’s clear artificial intelligence will continue to transform work as we know it. As these AI systems evolve, so do the potential data risks associated with their deployment. As a result, organizations need to adopt a comprehensive approach to securing data that goes into and comes out of GenAI tools. 

Beyond data security, a key aspect of securing generative AI also involves robust access management. Chances are good that you organization already implemented basics like Single Sign-On (SSO) solutions and multi-factor authentication (MFA). A modern CASB tool goes beyond securing access to SaaS and GenAI applications to provide a level of data protection. 

Any effort to secure data needs to account for private customer information like PII, PHI, data controlled by regulations like GDPR, HIPAA and soon-to-be-active NIS2 and ultimately your organization’s proprietary information. A Data Loss Prevention (DLP) solution is a key piece of the puzzle, but there’s more to it than that. 

Here are 5 key considerations for securing Generative AI tools:

Commiting to Employee Training and Awareness

Using AI tools requires new ways of thinking. They operate in a more interactive way than traditional SaaS applications. That’s why employee training and awareness are critical for securing data generative AI systems. Ensuring all users understand potential risks and data best practices is essential. By fostering a culture of security awareness as it relates to data in AI tools, organizations can reduce human error and data exfiltration. That’s a great place to start. 

Achieving Compliance with Regulations

Adherence to regulations like GDPR, CCPA, and HIPAA ensures that AI systems handle data both legally and ethically. Compliance is crucial in protecting user privacy and building stakeholder trust. By regularly reviewing and aligning data practices with evolving regulations, organizations can prevent legal repercussions and maintain a robust security posture, thus ensuring the integrity and safety of their AI systems.

Securing Access to AI Tools and SaaS Applications

 A modern CASB tool plays a vital role in managing access to generative AI tools. By offering visibility and control over user activities, CASBs ensure only authorized employees access these AI systems. Regularly updating access permissions and conducting thorough reviews maintain data integrity and privacy within AI environments. A modern tool like Forcepoint ONE CASB integrates inline inspection of files uploaded to or downloaded from a GenAI tool.

Implementing a Data Loss Prevention (DLP) Solution

Data Loss Prevention (DLP) solutions also play a huge role in protecting sensitive information within AI datasets. Forcepoint DLP enables businesses to discover, classify, monitor, and protect data intuitively. Using the 1,700 classifiers provides out-of-the-box compliance for data in over 80 countries. And it also helps extend data security across multiple channels beyond SaaS and GenAI tools. Other related solutions like our Forcepoint ONE Data Security offers the fastest endpoint provision and policy deployments speeds in the industry, with real-time incident remediation.

Tackling Ongoing Monitoring Capability

Continuous monitoring detects anomalous behavior or potential security incidents in real-time. Automated monitoring tools and a clear incident response plan ensure swift resolution of security issues. Using policy controls to protect regulated and sensitive data is a good first step. Implementing solutions like our Risk-Adaptive Protection go further by providing  real-time risk response through policy automation.  It works in conjunction with access controls through advanced behavioral analysis to identify risky user behavior and dynamically adjust data policies to match that potential risk.

No matter where your company is on the road to securing GenAI, we can help. Talk to one of our experts today.