As the demand for AI surges, AI vendors are devoting greater bandwidth to data security issues. Not only are they being compelled to comply with emerging data privacy regulations (e.g. the EU Data Act), but they’re finding themselves under the microscope of clients skeptical about how their data is being used and processed.
The trouble is, where it concerns tightening data security practices around AI, many orgs aren’t in a position to execute well. According to a survey from BigID, a data control platform, half of organizations rank data security as their top barrier to implementing AI.
Hailing from the app engineering and legal sectors, Abhi Sharma and Leila Golchehreh were well-versed in the challenges at play here. Confident they could build something to address the data security conundrum, the pair launched Relyance AI, a platform that checks if a company’s data usage is aligned with governance policies.
“The concept of how we would build Relyance came to us one evening when we were catching up over pizza in San Francisco,” Sharma told TechCrunch. “Although we came from two very different backgrounds, together, we realized that more could be done to ensure visibility in an organization’s data processing.”
Golchehreh is an attorney by trade, having previously served as senior counsel at Workday and autonomous car startup Cruise. Sharma, a software dev, was a platform engineer at AppDynamics before helping to found FogHorn, an edge AI platform that Johnson Controls acquired in 2022.
Sharma says that most companies face three main hurdles to AI adoption: a lack of visibility to data in AI, the complexity of how data is handled, and the rapid pace of innovation. All these contribute to reputational risk, Sharma says — and open companies to legal threats.
Relyance’s solution is an engine that scans an org’s data sources — such as third-party apps, cloud environments, AI models, and code repositories — and checks to see if they’re in agreement with policies. Relyance creates a “data inventory” and ‘data map,” which it syncs with customer agreements, global privacy regulations, and compliance frameworks.
“Relyance enables organizations to monitor external vendor risks,” Sharma said, “while its data lineage feature tracks data flows across applications to identify potential risks proactively.”
An example of a Relyance-generated data map. Image Credits:Relyance
Now, Relyance isn’t executing on a totally novel concept. Sharma admits that OneTrust, Transcend, Datagrail, and Securiti AI are among the vendors that compete with it in some way. For example, Datagrail offers automated risk monitoring tools that help companies build third-party app risk assessments quickly.
But Relyance appears to be holding its own. Sharma claims that the business is on track to double annual recurring revenue this year, and that Relyance’s customer base — which includes Coinbase, Snowflake, MyFitnessPal, and Plaid — grew 30% in H1.
Setting the stage for further growth, Relyance this month closed a $32 million Series B round led by Thomvest with participation from M12 (Microsoft’s venture fund), Cheyenne Ventures, Menlo Ventures, and Unusual Ventures. Bringing the startup’s total raised to $59 million, the new funds will be put toward growing Relyance’s team to 90 employees by the end of the year.
“We decided to raise funds because the demand for AI continues to grow and new privacy and AI regulations are being put into place globally,” Sharma said. “Our hiring efforts will primarily focus on expanding our engineering team and increasing our go-to-market capacity to support our product development and growth momentum.”
