SecureAuth enhanced the SecureAuth IdP adaptive access control solution with the introduction of machine learning, and identity governance as part of its adaptive risk analysis.
Machine learning capabilities find anomalies and inconsistencies over time to eliminate identity-based threats, while the intersection of access control and identity governance technology identifies and monitors privileged account access, taking action to protect targeted systems. Together, the new capabilities add protection from privilege misuse and cyberattacks.
By adding machine learning, enterprises that deploy SecureAuth IdP gains:
- Identity security improvements, including single and multi-factor authentication,
- The ability to focus on key threats and decrease detection time,
- Monitoring of identity behavior to better inform authentication decisions.
“With these new capabilities, enterprises using SecureAuth IdP will have even greater confidence that users requesting access to their networks are who they claim to be – and that they don’t have bad intentions behind their actions,” said Keith Graham, chief technology officer at SecureAuth.
“Passwords and two-factor authentication (2FA) are important, but alone are no longer enough to stop identity-related security incidents or even slow them down. Unmasking attackers who often impersonate legitimate users with legitimate credentials requires a great deal of information about each user. Ultimately, it’s about establishing a higher level of trust between enterprises and their users. Machine learning and the intersection of identity governance delivers that extra degree of knowledge to do that.”
SecureAuth IdP machine learning reinforces protection against attackers
Machine learning helps organizations identify high-risk users and treat them differently than other, more trustworthy identities. This is a differentiator that enterprises need in the face of sophisticated threats.
Machine learning enables SecureAuth IdP to analyze large data sets to find anomalies or inconsistencies in behavior that can signal attacker behavior. It baselines “normal” behavior for all users and identifies inconsistent patterns that could indicate a compromise – helping security professionals make informed decisions. SecureAuth IdP machine learning:
- Analyzes data to detect anomalous time or day of the week of login activity, new or rarely used IP address, new or rarely visited location, change in login success frequency, change in login failure frequency, and increase in application login activity,
- Delivers protection against attackers, even those with valid credentials and who can bypass 2FA,
- Decreases detection time and the volume of alerts provided by following anomalous behavior.
SecureAuth identity governance capabilities focus on user privilege
The integration of SecureAuth’s identity governance capabilities provides SecureAuth IdP with information about users whom attackers often prefer and pursue. Users with privileged or sensitive access are favored by attackers because of the access they possess.
Attackers also infiltrate networks and create fictitious users with access rights they covet, but in doing so, they often violate segregation of duties (SoD) policy. SecureAuth IdP can now identify these violations and potentially fictitious accounts.
With SecureAuth IdP customers can now:
- Improve breach prevention by keeping a close eye on high-value access rights,
- Identify users who pose greatest value to attackers because of their access (privileged or sensitive) – and then conduct deeper threat analysis,
- Identify and add a layer security to common SoD violations.
By identifying users who have privileged access and by applying more scrutiny to them, the intersection enables SecureAuth IdP customers to identify attackers masquerading as users with privileged access – all without compromising the user experience of a legitimate user.