As demand for AI increases, AI providers are dedicating increasingly bandwidth to data security issues. Not only are you forced to comply with recent data protection regulations (e.g. the EU Data Protection Act), but you might be also within the highlight of consumers who’re skeptical about how their data is used and processed.
The problem is that many organizations are unable to tighten data security practices related to AI. According to a Opinion poll According to BigID, a knowledge governance platform, half of firms rank data security as their biggest obstacle to implementing AI.
Abhi Sharma and Leila Golchehreh come from the fields of app engineering and law and were very aware of the challenges. Confident that they may develop something to resolve the information security problem, the 2 began Trust AIa platform that checks whether an organization's data usage complies with governance policies.
“The idea of how we’d construct Relyance got here to us one evening after we met over pizza in San Francisco,” Sharma told TechCrunch. “Although we had two very different backgrounds, together we recognized that more could possibly be done to make sure transparency in a company’s data processing.”
Golchehreh is a lawyer by occupation and previously served as senior counsel at Workday and autonomous automobile startup Cruise. Sharma, a software engineer, was a platform engineer at AppDynamics before helping found FogHorn, an edge AI platform that Johnson Controls acquired in 2022.
Sharma says that almost all firms face three primary obstacles to adopting AI: lack of information visibility in AI, the complexity of information processing, and the rapid pace of innovation. All of this contributes to reputational risk, says Sharma – and exposes firms to legal threats.
Relyance's solution is an engine that scans a company's data sources – resembling third-party apps, cloud environments, AI models and code repositories – and checks whether or not they comply with policies. Relyance creates a “data inventory” and “data map” that syncs with customer agreements, global privacy regulations and compliance frameworks.
“Relyance enables organizations to observe risks from external vendors,” said Sharma, “while its data lineage capability tracks data flow across applications to proactively discover potential risks.”
Now Relyance will not be implementing a very recent concept. Sharma admits that OneTrust, Transcend, DataGrail and Securiti AI are among the many vendors that compete with him indirectly. For example, DataGrail offers automated risk monitoring tools that help firms quickly create risk assessments for third-party apps.
But Relyance appears to be holding its own. Sharma claims that the corporate is on the right track to double annual recurring revenue this yr and that Relyance's customer base – which incorporates Coinbase, Snowflake, MyFitnessPal and Plaid – grew 30% in the primary half of the yr.
Laying the muse for further growth, Relyance closed a $32 million Series B round this month led by Thomvest with participation from M12 (Microsoft's enterprise fund), Cheyenne Ventures, Menlo Ventures and Unusual Ventures. The startup's total revenue is $59 million. The recent funding will go toward expanding the Relyance team to 90 employees by the tip of the yr.
“We decided to boost funds since the demand for AI continues to grow and recent privacy and AI regulations are being introduced all over the world,” said Sharma. “Our hiring efforts will primarily give attention to expanding our engineering team and increasing our go-to-market capabilities to support our product development and growth momentum.”
