Are you looking to improve your information security? If so, you’ve likely read up on adding authentication measures to your IT infrastructure, but do you know how to choose the best measures for your organization? Here’s some good news. Applying analytics to big data for authentication measures has the potential to increase both security and user convenience – a win/win for both you and your clients.
Behavioral Analysis Boosts Risk-Based Authentication Measures
Protecting sensitive data while ensuring the right people have access to the right information and resources in an environment with rapidly changing technology is definitely challenging. The solution for many businesses across the globe and throughout all industries is to use a variety of authentication measures. However, authenticating users based on generic criteria isn’t nearly as effective as authentication measures incorporating specific behavioral analysis with a broad and diverse set of factors.
Big data analytics produces precise information about this behavior which can then be used to choose authentication measures best suited for a mid-size business, enterprise, or organization.
Mining Big Data for Usage Patterns: How it Works
Interactions with systems and applications create device usage patterns and behavioral characteristics over time. And the more people use systems and applications, the better the data will be when eager behavioral analysts (or the behavioral analytics software) start combing through it.
All of this data contributes to the creation of robust user profiles – profiles which are key to risk-based authentication measures.
Risk-based authentication implements measures that verify user identities for each and every session. The current user behavior is compared to their past behavior in previous sessions – the information that contributes to their user profile. If the current behavior is consistent, access is granted. If it isn’t consistent, the user may be denied access or asked to authenticate with additional factors for verification before access is granted.
Search Big Data for Authentication Clues and Save Money
Risk-based authentication is a cost-effective way to expand controlled access to an organization’s virtual private network (VPN). This is particularly useful when working with temporary employees, contractors, or consultants who will only need access for a short period.
In some cases, authentication checks run in the background, evaluating multiple specific risk factors identified by big data analytics. A wide variety of risk factors, identified by big data analytics, combine to create controls to allow or block access to users as required. This increases user session security.
The use of big data analytics to help design and define security measures is also a cost-effective way to expand controlled access to an organization’s virtual private network (VPN). This is particularly useful when working with temporary employees, contractors, or consultants who will only need access for a short period.
Over 30 percent of enterprises will use risk-based authentication to allow remote access by their workforce by the end of 2016, according to a 2013 Gartner report. This is a significant increase over the current figure of less than two percent, and accompanies increasingly complex authentication methods.
As daily business activity is increasingly carried out online either via the internet or through VPNs, authentication becomes a critical part of an organization’s security foundation. Utilizing big data for authentication helps create a highly specific process to best safeguard your organization’s digital information. By doing so, the risk of it falling into the wrong hands decreases exponentially.