Identity security automation solution provider SecureAuth today announced enhancements to their SecureAuth IdP Adaptive Access Control solution incorporating machine learning and Security Risk Analytics (SRA).
SecureAuth, in a press release, stated these new capabilities will help enterprises fortify their identities against cyber attacks and insider threats. The new machine learning and SRA enhancements should allow enterprises to reduce their detection time, detect elusive threats or threats outside normal threat detection capabilities, and continuously monitor identity profiles to inform authentication decisions. Machine learning allows the SecureAuth solution to detect attackers that have bypassed two-factor authentication and catch insider threats.
“Passwords and two-factor authentication (2FA) are simply not enough to prevent data breaches or even slow them down. Unmasking attackers who often impersonate legitimate users with legitimate credentials requires a great deal of information about each user. Machine learning and SRA delivers that extra degree of knowledge” said SecureAuth CEO Jeff Kukowski in a statement.
“Very few vendors even talk about using machine learning in the authentication process. Even fewer, if any, identify high-risk accounts and treat them differently than others – which is a critical differentiator that enterprises need in the face of increasingly sophisticated threats,” he continued.
Interestingly, these new capabilities bear a striking resemblance to those found in SIEM and security analytics solutions—possibly suggesting digital identity absorbing of other aspects of cybersecurity. Experts have been predicting it for some time. Is this evidence of it occurring?
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