Enterprises spent nearly $100B globally in 2018 on cybersecurity, reports Gartner, but breaches increased a record 44%, opines Identity Theft Resource Center. Unfortunately, this tells us that the money being spent on security – including password protection and two-factor authentication (2FA) – are not preventing identity-related incidents.
In order to thwart, and even prevent, these attacks, we have to unmask attackers who impersonate legitimate users, and this requires a great deal of intelligence. So, how do you shift from traditional methods of access control to the future of cybersecurity? By combining machine learning with multi-factor authentication (MFA) in a way that learns and adapts to prevent breaches.
Machine learning enhances protection against attackers
Machine learning helps organizations identify high-risk users and treat them differently than other, more trustworthy identities. This is a critical solution for organizations in the face of increasingly sophisticated threats, because it analyzes authentication data over time to find anomalies or inconsistencies that can signal attacker behavior. Machine learning baselines “normal” behavior for all users over time and then identifies inconsistent patterns that could indicate a compromise. This additional layer of intelligence helps security professionals and access management systems make more informed decisions around who should and should not gain access.
The power of SecureAuth adaptive authentication
Machine learning and identifying abnormal behavior is one of many layers in our comprehensive breach prevention and access protection solution, SecureAuth IAM.
Security in layers has proven an effective approach to better protection. The more context and data an organization can gather about an access requestor, the better the chance to determine if they pose a threat or not. This starts with multi-factor authentication, but doesn’t end there: SecureAuth SaaS-delivered IAM provides the most comprehensive set of risk analysis in the industry, analyzing behavior through machine learning, along with numerous device, location, IP address, and account type risk checks. All of these risk checks are part of our adaptive authentication solution that enables our customers to cast as many traps as possible to catch attackers.
If you do not look, you will not find
Adaptive authentication offers many layers of security and it also provides a disruption-less user experience — the best of both worlds. The more you know about a user, the more confidence and trust you gain that the user is or is not who they claim to be. For those with high trust, a second factor or multi-factor step is not needed. This means users are only disrupted when risk is present.
Machine learning is a powerful component of a risk-based authentication program. With SecureAuth SaaS IAM you’ll be able to:
- Uncover threats through behavioral analysis to detect abnormalities such as:
- Time/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
- Increase in application login activity
- Provide an elevated level of protection against attackers, including those with valid credentials that bypass traditional 2FA.
- Shrink detection time by identifying anomalous activity and potential risks and passing this intelligence to security operation teams.
- Detect potential insider threats – one the most difficult threats to uncover.
Next steps
Ready to get started? Schedule your own SecureAuth demo.
