Defending Against Identity Threats Using Risk-Based Authentication

Abstract

Defending against identity-based threats, which have predominantly increased in the era of remote access and working, requires non-conventional, dynamic, intelligent, and strategic means of authenticating and authorizing. This paper aims at devising detailed risk-scoring algorithms for five real-Time use cases to make identity security adaptive and risk-based. Zero-Trust principles are incorporated by collecting sign-in logs and analyzing them continually to check for any anomalies, making it a dynamic approach. Users are categorized as risky and non-risky based on the calculated risk scores. While many adaptive security mechanisms have been proposed, they confine identities only to users. This paper also considers devices as having an identity and categorizes them as safe or unsafe devices. Further, results are displayed on a dashboard, making it easy for security administrators to analyze and make wise decisions like multifactor authentication, mitigation, or any other access control decisions as such.