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Dig Security Shields Generative AI Deployments

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The widespread use of generative AI remains one of the more notable breakthroughs in the tech world. Every day, new headlines appear covering innovations in this technology. It’s reminiscent of the early days of smartphones. The reason for the constant spotlight is that the value and potential of LLMs and generative AI unlock unprecedented capabilities and efficiencies for enterprises.

However, these intelligent systems introduce novel security hazards.

Experts caution that the convergence of LLMs and enterprise data introduces new complexities, as organizations must navigate the delicate balance between harnessing the power of AI for customized solutions and the imperative to uphold security and compliance standards. Addressing these challenges requires the adoption of robust data protection measures, meticulous handling of sensitive data and rigorous scrutiny of the AI training and deployment processes.

A company helping with those challenges? Dig Security.

Dig now enables customers to train and deploy LLMs while upholding data security, privacy and compliance, maintaining visibility and control over the data being passed to relevant AI models and preventing inadvertent data exposure during model training or deployment. All of this is possible due to the enhancements to the Dig Data Security Platform, including new capabilities to secure LLM architectures.

With Dig, customers can monitor the data going into a model, detect data-related AI risk before a model is trained, map all AI actors with access to sensitive data and identify shadow data and shadow models running on unmanaged cloud infrastructure.

The platform provides comprehensive scanning of a company's cloud databases and buckets, efficiently detecting and classifying sensitive data. By revealing which users and roles have access to this data, the platform becomes an essential tool in identifying potential risks where sensitive information might be used for AI model training, fine-tuning or influencing responses. Security teams can swiftly identify and flag models that pose higher risks of leaking sensitive information, empowering organizations to take proactive measures to safeguard their data.

Once AI models are trained, they become opaque, making it challenging to retrieve data from their training corpus. However, Dig's real-time data detection and response feature offers a solution to this conundrum. By identifying data flows that could result in downstream model risks, such as moving personally identifiable information into a bucket utilized for model training, users can address potential issues promptly and prevent data leaks.

Dig's robust data access governance capabilities go beyond data detection and extend to AI models that have API access to organizational data stores. The platform provides clear visibility into the types of sensitive data accessible through these APIs, allowing businesses to ensure compliance and prevent unauthorized access to critical information.

An agentless solution, Dig's platform covers the entire cloud environment, including databases running on unmanaged virtual machines. By alerting security teams to instances of sensitive data stored or moved into these databases, Dig bolsters cloud security. It also can detect when a VM is utilized to deploy an AI model or a vector database, which plays a crucial role in storing embeddings, ensuring a comprehensive approach to data protection throughout an organization's cloud infrastructure.

"We are providing capabilities that allow enterprises to innovate securely – to train and deploy LLMs while maintaining data security, privacy and compliance," said Dan Benjamin, co-founder and CEO, Dig Security.

These advancements come on the heels of Dig adding capabilities for OCR to the Dig Data Security Platform.




Edited by Alex Passett
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