DynamoFL, a company specializing in privacy- and compliance-focused generative AI solutions, has successfully closed a $15.1 million Series A funding round. The funding aims to meet the growing demand for DynamoFL’s technology, which enables customers to safely train Large Language Models (LLMs) on sensitive internal data while preserving privacy and compliance. DynamoFL’s flagship technology has already been adopted by Fortune 500 companies in sectors such as finance, electronics, insurance, and automotive.
The Series A funding round was co-led by Canapi Ventures and Nexus Venture Partners, with participation from other investors including Formus Capital, Soma Capital, and individual angel investors with expertise in privacy and machine learning. DynamoFL’s technology addresses the pressing need for AI solutions that maintain compliance and security, especially in the context of large language models that can inadvertently memorize sensitive data during training.
DynamoFL’s solutions offer privacy-preserving training and testing offerings, allowing organizations to fine-tune LLMs on internal data while identifying and documenting potential privacy risks. This approach is crucial in light of evolving global regulatory requirements, such as GDPR and AI regulation acts, that mandate enterprises to detail data risks associated with AI deployments.
The company’s technology comes at a time when concerns about data security and privacy in AI are gaining significant attention from regulatory bodies. DynamoFL’s privacy evaluation suite offers testing for data extraction vulnerabilities and automated documentation, helping organizations ensure that their LLMs are secure and compliant.
“DynamoFL is set to do just that and enable enterprises to adopt AI while preserving privacy and remaining regulation-compliant,” said Jishnu Bhattacharjee, Managing Director at Nexus Venture Partners. The investment reflects the growing demand for privacy-focused AI solutions across various industries and underscores the importance of building AI platforms with privacy and security considerations from the outset.
