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Confidence, a San Francisco, California-based startup developing a real-time codebase analytics platform, emerged from caution today with $ 30 million raised in seed and series A rounds from Unusual Ventures and Menlo Ventures. Co-CEOs Leila R. Golchehreh and Abhi Sharma say the funds will be used to expand the company’s sales and engineering teams, as well as accelerate Relyance’s go-to-market strategy.
In a modern business, code is responsible for moving, managing, and protecting data. The problem is that the code changes almost continuously. Legal, governance and compliance teams do not know from moment to moment what the code is doing and if it is complying with regulatory and contractual obligations. The faster the business moves, the bigger, and the more dangerous, the knowledge gap potentially becomes.
Relyance seeks to bring greater visibility to code bases by leveraging natural language processing and compiler-style source code analysis techniques, allowing the platform to understand the semantics of personal data processing as the code is developed. writes and is inserted in the organization. The idea is that data governance can be approached proactively at design time when a system is being built rather than after the fact, establishing a more cross-functional understanding of an organization’s data operations.
“With Relyance AI, privacy and legal teams can talk about codes and engineering teams can talk about contracts. Privacy professionals can see in real time how their organization’s code handles personal and confidential data, instead of relying on forms and surveys that quickly become out of date, ”Golchehreh told VentureBeat by email.
Privacy, governance and risk management challenges pervade the technology industry. The average business has 534,465 files containing sensitive information. At the same time, only 10% of consumers feel they have full control over your personal information.
Outlining the hurdles to overcome, 57% of senior executives rank “risk and compliance” as one of the top two risk categories for which they feel least prepared. Address. Mistakes can be costly – the average company spends $ 10,000 per employee to cover expensesand corporations Paid out $ 15 billion in fines for US regulatory violations in 2015 alone.
Golchehreh and Sharma, two old friends, outlined Relyance’s idea during a six-hour lunch in San Francisco in late 2019, just before the pandemic. Sharma, both tech entrepreneurs, has a background in data science and artificial intelligence, while Golchehreh has worked as an attorney and thought leader creating data protection programs at organizations like Workday.
“With the explosion of data, we continue to view data privacy and governance as one of the most challenging issues in modern society that needs a fresh, technology-driven perspective. We are now reinventing the state of the art in privacy and data governance technology from the ground up, looking to build a generational, mission-driven enterprise, “Sharma told VentureBeat.
Relyance aims to address challenges with models that analyze a company’s contracts, policies, and requirements, creating a proxy to set restrictions and rules, and generate compliance records. The rendering captures specific data protection requirements, which act as constraint solvers against which Relyance compares data flows and processing activities. If there is a misalignment between any of the requirements and reality, the platform presents it to organizations as a perspective.
Relyance connects to external vendor APIs, infrastructure tools and code repositories, inventorying data assets through internal APIs and third-party systems. The platform generates universal records of processing activities, learning things like data types and categories, the purpose of processing, the basis of the transfer, security measures, and processing activities. Relyance can detect missing vendor and data processing agreements, invalidated compliance frameworks in agreements, and missing data categories. Additionally, it can display key terms such as security and codebase inspection by providing visibility into how personal data is shared between internal services and external providers.
“Our team has in-house experience in the domain of data protection and deep machine learning. Additionally, we work with experienced attorneys to help train, direct, and tag our natural language processing models using real data and underlying privacy and legal documents, ”Golchehreh told VentureBeat.
Relyance competes with OneTrust, BigID, Ethyca, DataGrail and Securiti.AI in the $ 7.39 billion global risk management market, but Sharma believes his code-based technical approach and skills set him apart. Current customers include Dialpad, Patreon and Samsara, as well as “dozens” of other brands in financial technology, communications and games.
“The pandemic has made the whole world connected to the Internet and there has been an exponential increase in digitization in all dimensions. In that regard, the pandemic has dramatically accelerated the need for the Relyance solution, which offers visibility and transparency into data flows and processing not only in an organization internally, but also in the myriad new tools and technologies adopted at a speed dizzying. Almost every aspect of conducting business has gone digital and the processing of personal data is increasing dramatically, all as privacy laws expand in the US and around the world. This means that organizations need Relyance AI to help manage their privacy and data protection programs now more than ever, ”said Leila.
Relyance has 31 employees and expects to expand to more than 50 by the end of the year.
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