Cobalt Iron Earns Patent for Machine-Learning-Driven Authentication Control

Cobalt Iron Inc., a leading provider of SaaS-based enterprise data protection, announced that it has been granted a patent on its technology for machine learning (ML) optimization of authentication control. Issued on April 19, US Patent 11310237 describes new capabilities for Cobalt Iron Compass, an enterprise SaaS backup platform. Compass already has the patented ability to optimize user authentication and access to IT resources dynamically based on what’s happening in the environment. This new patent extends that capability by applying new ML techniques — adding yet another layer of intelligence and security to enterprise IT resources and operations.

Controlling authorization and access to IT resources such as data, data centers, hardware devices, and applications is a critical part of securing any enterprise business. Yet authentication controls in practice today are static, seldom changed, and unresponsive to changing conditions and events. Often these controls are not monitored or analyzed to determine whether they are effective or optimized to achieve company security and safety objectives. And sometimes security administrators must change user authentication roles temporarily to ensure business security and safety under unusual circumstances, such as a fire or major weather event. The result is security holes that leave enterprise data, operations, and infrastructure vulnerable to attack.

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To address such problems, Cobalt Iron’s newly patented ML techniques continually improve authentication controls over time by learning from the results of past controls. What’s more, the technology automatically adjusts authorization controls based on conditions, events, project status, access activities, etc. This eliminates the pervasive security exposures of obsolete, and unresponsive authorization controls and makes the entire IT infrastructure more secure and more intelligent.

The techniques disclosed in this patent are:

• Collect training data, including environmental event data, permission access patterns of users, access control duration data, security events and alerts, project data, cyber event information, security event logs, data protection operational results, and such.
• Analyze training data to determine the effectiveness of authentication controls during previous conditions and events.
• Generate ML rules to potentially adjust authentication controls during future conditions and events.
• Monitor for various conditions and events, including environmental events.
• Dynamically adjust user authentication privileges based on generated ML rules.
• Modify durations of user authentication privilege adjustments responsive to generated ML rules.

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Security administrators and other IT professionals who are responsible for maintaining security, authentication, and access control in their environments may be able to leverage Cobalt Iron’s new patented techniques to, for example, restrict physical access automatically and temporarily to data center buildings or rooms during a fire or flood. In another example, access privileges for key IT personnel to data, data centers, or applications may be temporarily increased prior to a weather event to expedite emergency backup procedures.

“Today’s authentication control practices can’t keep up with continually changing business environments, and that can easily lead to security risks,” said Richard Spurlock, CEO and founder of Cobalt Iron. “The novel techniques in this patent use extensive data collection, analytics, and machine learning to adjust user authentication and access to IT resources dynamically based on environmental events and operational outcomes. Not only does this mean that IT infrastructures and business security controls become more intelligent over time, but they automatically adjust themselves to continue meeting business security and safety needs.”

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