The power to do everything online is something of an ideal. Buying groceries, seeing your doctor via telehealth: the possibilities are endless. Especially with the shutdowns of the last 18 months, logging in for instant access to both essential and entertaining platforms has been a lifesaver. And yet, this world-at-our-fingertips reality isn’t without risks.
In tandem with the rise of online resources is the reality of breaches, fraud, and even identity theft. So far, 2021 has already seen leaks of personality identifiable information (PII) for millions of users through well-publicized incidents, such as Ubiquiti, Parler, Mimecast, Pixlr and more. People’s personal data has been lost, stolen, exposed, and hacked. Unfortunately, this trend isn’t new and can cost companies an average of $3.86 million per breach, not to mention the harm to the users themselves.
People have fundamentally changed the way they interact online and the kinds of online services they use. Because that is true, the burden is on companies to enhance security and protect users’ privacy. But how? For many, the answer lies in artificial intelligence (AI).
A 2019 study by Gartner predicts that, by 2023, 40% of privacy compliance technology will use AI. Global spending on privacy efforts are expected to reach $8 billion by 2022. Clearly, business leaders recognize that data privacy is mission critical and an essential expenditure.
High traffic volume and complex systems far exceed manual efforts for security. The only way to set up effective barricades against hackers is to beat them at their own game. However, just because the original problem is complex doesn’t mean the solution should be. In fact, data privacy solutions should introduce as little friction as possible to the user experience. Otherwise, the very efforts businesses make to protect users will turn those same users off.
The most basic starting point for data privacy has to do with how businesses handle personally identifiable information, which is essentially about a customer’s identity. Businesses face two major issues off the bat:
Regulations vary widely across industries and regions, which makes compliance a challenge, even if the right AI software is available to achieve it. Furthermore, the sheer volume of user events that require authentication can be overwhelming.
Strivacity is a great example of innovation in this space. Their offerings include adaptive access control, which can embed secure, frictionless logins into any application with a simple, no-code integration that includes an identity store focused on customer privacy.
They also have adaptive multi-factor authentication, which is a key way that businesses around the world are verifying customer identities. The keyword in these offerings is “adaptive,” which speaks to how the product can seamlessly integrate with AI.
Strivacy was founded by Keith Graham and Stephen Cox. Cox describes how they solve these two challenges:
“The regulatory challenges of facilitating a consumer to consent to a particular use of data, as well as revoke that consent at an arbitrary point in the future, [is how you cover] your bases when it comes to data privacy. There are policy and practical challenges with the ‘right to be forgotten’ in the machine learning space. However, it’s important that you begin to set your organization up for solving these types of challenges proactively.
