Technology research and consulting firm Gartner recently published their Market Guide for AI Trust, Risk & Security Management. Cape Privacy is included among the wide-ranging list of vendors who apply artificial intelligence in its various forms to help address the challenges of protecting data and the networks and systems that work with, store, and manage data. According to Gartner, who shortened the category to AI TriSM, the guide "comprises multiple software segments that ensure AI model; governance, trustworthiness, fairness, reliability, efficacy, security and data protection."
What we find interesting about the broad scope of the guide is that there are so many different ways working with data can pose a risk to an organization's business operations, and that there has been a concerted effort to apply the power of artificial intelligence to find innovative ways to mitigate those many risks. It's interesting that, in their 2019 AI in Organizations survey, Gartner identified privacy and security concerns as a major barrier to AI implementation. Now, as the technology continues to mature and innovators are finding interesting ways to use it, we're seeing the power of AI being put to work in order to address those concerns.
Cape Privacy is focused on preserving the privacy of sensitive data so that it can be put to work by data scientists to solve problems in business and research. Although we show up in the market guide under encryption as our differentiating capability, we go well beyond just encryption. Our Cape Privacy encrypted learning platform uses a combination of advanced machine learning and cryptography, a process known as secret sharing, and secure multi-party computation (SMPC) to give data scientists access to sensitive and regulated third-party data while preserving the privacy and integrity of that data.
Incidentally, SMPC was recently recognized by Gartner as progressing along its 2021 Privacy Hype Cycle toward the "slope of enlightenment." Our use of the technique may accelerate it quickly to the "plateau of productivity. That's because there is tremendous value contained in the large data sets being collected by organizations in every industry, waiting to be unlocked.
Healthcare and medical research, financial services, retail, and government especially collect a lot of detailed data about us. The problem is, data scientists are often prohibited from accessing that data because of various regulations. These are important and necessary laws that protect the public from being exploited by unscrupulous people, but if those rich sources of data could be analyzed in their complete state, using today's sophisticated data models, all kinds of .
Think about how quickly a handful of pharmaceutical research organizations were able to develop vaccines in response to the COVID-19 pandemic. That was possible in part because, in the face of a global crisis, those organizations were willing to share some intellectual property to accelerate research and development. Now imagine if any organization with valuable data had confidence that they could share their information while preserving the integrity of the individuals associated with each piece of that data set. Instead of incremental improvements, we would see quantum leaps forward in scientific innovations.
Of course, there is value in incremental improvement, too. In financial services, a fraction of a percentage of performance improvement, multiplied over the course of millions--and even billions--of transactions, can add up to big numbers. And, less altruistically, organizations that have been collecting data in the course of their operations could create new revenue streams by selling secure, privacy preserving access to that data because the protections in place mean no regulations are violated. That's exciting, too.
What we have already seen from the organizations that use our encrypted learning platform tells us that, when smart, talented people have access to large, rich, and interesting data sets, they will always surprise you with the creative ways they solve problems. Different experiences and perspectives lead to unexpected breakthroughs. It's exciting to be a part of it by providing the means to bring data and innovators together, securely.
At Cape Privacy, our approach to data science and privacy preserving data access combines advanced machine learning capabilities, secret sharing, SMPC, to protect privacy by default. There is no key sharing, and the data remains encrypted throughout the training process. Data owners and data scientists don't use the same data model. That way, the data's custodians are assured of the integrity of their data, and users can maintain the confidentiality of their proprietary models.
If you are looking for a way to access protected third party data, or if you are a data owner interested in making your data available to other data scientists and innovators, here's a describing how encrypted learning works. Or, if you want to have a conversation about how encrypted learning can be applied to your organization, get in touch. We'd love to talk to you.