AI Data Security Governance is Here

Balaji Ganesan, CEO, Privacera

Generative AI and large language models (LLMs) have already had an incredible impact on the world, beginning with the launch of ChatGPT in November 2022. This innovative technology has captured the attention of millions, surpassing even the growth rates of society’s most popular platforms like Netflix and Instagram. 

With over 100 million users, which have been gained in metaphorical milliseconds when compared to other applications, it's safe to say that generative AI is here to stay.

Transforming Enterprise Operations

Generative AI and LLMs have the potential to transform business operations. By upgrading communication, automating tasks, enhancing decision-making processes, and providing personalized experiences, these advancements empower enterprises to gain a competitive edge, streamline workflows, and offer exceptional services to customers and stakeholders. The power of language processing is truly remarkable. 

Addressing Privacy and Security Concerns

While the benefits of generative AI are tremendous, it's critical to address the unprecedented concerns surrounding privacy and security that have arrived with this technology. After all, generative AI is quite different from the machine learning models we have seen thus far, and requires new thinking as a result. 

As we delve deeper into training these AI models, we must be more than mindful of the deeply serious risks associated with sensitive data exposure. Some companies have already taken fairly aggressive precautions by banning ChatGPT access on corporate devices and networks to mitigate potential privacy breaches.

Data Security Governance for AI

To ensure the safety of sensitive information, it's crucial to establish robust data security measures when it comes to generative AI. Here are a few key considerations:

Protecting Training Data

Training these models requires vast amounts of data, and it's essential to safeguard it. While internet data itself poses privacy challenges, fine-tuning the AI models using your own company's data provides tailored value. If this data contains sensitive, classified, or private information, precautions must be taken. 

Continuous scanning, classifying, and tagging of sensitive data before loading it into the models for training is crucial. Implementing controls such as masking, encryption, or removal of sensitive data elements ensures privacy.

Privacy and Access Control for Model Responses

Applications accessing the model for generating responses should have appropriate identity authentication and data access controls. It's important to enforce fine-grained data access controls to prevent the disclosure of sensitive or private information, even if that data exists in the model. 

For instance, even if the model has been trained with employee salary information, it should only display salary details to individuals with the relevant security access permissions and role. Implementing role-based, attribute and tag-based masking, encryption, or redaction at the data item level ensures data privacy.

Filtered Questioning

Another layer of protection is pre-filtering questions based on the security and privacy settings of users. This approach ensures that sensitive data aspects are not exposed inadvertently. For example, if a question contains sensitive information, the system should immediately respond with an error message indicating that the question is not allowed.

By adopting these safeguards and best practices, we can harness the power of generative AI while upholding privacy and security standards. Together, we can unlock the true value of this technology in an ethical, responsible manner.

Privacera AI Governance

At Privacera, we’ve recently announced our own AI data security governance solution that brings together comprehensive data security governance for relational data, non-structured data as well as AI model training and access. 

Privacara AI Governance (PAIG) is powered by Privacera’s Unified Data Security Platform, which has set the standard for data in the traditional big data ecosystem as well as the modern cloud data estate. With PAIG, organizations tap into Privacera’s history of building massively scalable data and access security on diverse data estates.

Remember, the future is bright, and generative AI has the power to reshape how we work and communicate. Let's embrace its transformational potential while properly protecting our data and privacy in a holistic, sustainable way.