71pc of organizations want to restrict employee device access to the network but it will be unconducive for productivity. Source: Shutterstock

71pc of organizations want to restrict employee device access to the network but it will be unconducive for productivity. Source: Shutterstock

Can machine learning make privileged access easier to manage?

SECURITY intelligence needs to be timely and relevant in order to be actionable.

Since organizations are most vulnerable to internal fraud, having data and intelligence about internal actions and the means to monitor and analyze it at the right time is what makes all the difference.

In fact, 74 percent of businesses report having been breached via compromised privilege access credentials — and this is exactly the problem that machine learning (ML) might be able to help solve.

The technology lends itself perfectly to analyze anomalies in access behavior, providing IT administrators with an upper hand in assessing cyber risks with accuracy in real-time.

Additional authentication can be triggered when an unusual login behavior pattern is detected — automatically. Failing to provide the right details can lock out the user and protect the network from (further) damage.

Too much privileged, not enough management

Privileged access management is on Gartner’s top 10 security projects for three years in a row.

Compromised privileged access enable cyber attackers to breach a system with minimal effort because they’re disguised as an authorized user, allowing them to remain undetected and pass through systems and networks with ease.

This is why 71 percent of organizations want to restrict employee device access to the network — but refrain to do so as it might prove unproductive to existing workflows.

ML, however, can simplify it all. Companies can capitalize on ML to improve predictive accuracy in privileged access management to always be ahead of potential cyber attackers.

Technology and the behavior of cyber attackers will evolve over time and this is exactly why ML is the most suitable for the role of anomaly detection in large enterprises with thousands of users using tens of systems.

Organizations should remember that cyberattack not only renders them vulnerable to financial losses but also downtime that is necessary for investigating, assessing, and rebuilding the compromised systems.

Regardless, a secure enterprise network is good for businesses at all times.