As the demand for AI skyrockets, AI vendors are devoting more and more bandwidth to data security issues. Not only are they being forced to comply with new data privacy regulations (such as EU data law), they are also under scrutiny from clients who are skeptical about how their data is being used and processed. I realized that.
The problem is that many organizations are not in a good position to enforce their data security practices around AI. According to a study by data management platform BigID, half of organizations cite data security as the biggest barrier to AI adoption.
Abhi Sharma and Leila Golchehreh came from the app engineering and legal sectors and were familiar with the challenges happening here. Convinced they could build something to address the challenges of data security, they launched Relyance AI, a platform that checks whether a company's data usage is aligned with governance policies.
“The concept of how to build Relyance came to me one night while eating pizza and catching up in San Francisco,” Sharma told TechCrunch. “Even though we come from two very different backgrounds, we realized we could do more to ensure visibility into our organizations’ data processing.”
Mr. Gaultchere is a lawyer by trade and previously served as a senior advisor at Workday and self-driving car startup Cruise. Sharma, a software developer, was a platform engineer at AppDynamics before helping found FogHorn, an edge AI platform acquired by Johnson Controls in 2022.
Sharma says most companies face three main hurdles to AI adoption. The lack of data visibility in AI, the complexity of how data is processed, and the rapid pace of innovation. All of this contributes to reputational risk and exposes companies to legal threats, Sharma said.
Relyance's solution is an engine that scans an organization's data sources, including third-party apps, cloud environments, AI models, and code repositories, to ensure they are compliant with policies. Relyance creates “data inventories” and “data maps” that are synchronized with customer agreements, global privacy regulations, and compliance frameworks.
“Relyance enables organizations to monitor external vendor risks, and its data lineage capabilities track data flows across applications and proactively identify potential risks,” said Sharma. ”.
An example of a data map generated by Relyance. Image credit: Reliance
Now, Relyance isn't running on an entirely new concept. Sharma acknowledged that OneTrust, Transcend, Datagrail, and Securiti AI are among the vendors that compete with this in some way. For example, Datagrail provides automated risk monitoring tools that allow businesses to quickly build risk assessments for third-party apps.
But Reliance seems to be holding its own. Sharma claims that the business is on track to double its annual recurring revenue this year and that Relyance's customer base, which includes Coinbase, Snowflake, MyFitnessPal and Plaid, grew 30% in the first half.
Reliance this month laid the groundwork for further growth with a $32 million Series B led by Tom Best with participation from M12 (Microsoft's venture fund), Cheyenne Ventures, Menlo Ventures and Unusual Ventures. Completed the round. The startup has raised a total of $59 million, and the new funding will be used to expand Reliance's team to 90 employees by the end of the year.
“We decided to raise funding as the demand for AI continues to grow and new regulations regarding privacy and AI are introduced around the world,” Sharma said. “Our hiring efforts will primarily focus on expanding our engineering team and increasing our go-to-market capabilities to support our product development and growth momentum.”