Kansas Patent of the Month – October 2024

Cobalt Iron‘s new invention introduces a dynamic and adaptive approach to data security, leveraging machine learning and a policy-based system to address the increasing sophistication of cyber-attacks. This system continuously stores metadata from previous cyber-attacks, inspection policies, and data protection operations, which are used as training data for machine learning models. By monitoring for new security events, the system is able to identify potential threats in real time and determine the most appropriate inspection tools and methods for mitigation.

One key feature is its ability to analyze past incidents, such as ransomware attacks, and adapt future responses based on this historical data. If a new event mirrors a previous threat, the system can adjust inspection operations—either by selecting a different class of inspection tool or by refining the level of inspection performed. This adaptability ensures that data protection is constantly evolving to counter new forms of attack.

The invention further enhances its threat detection capabilities by performing historical analysis of attack patterns, attack timing, and even determining potential attack sources. This allows the system to preemptively address vulnerabilities and better secure data before an attack can cause harm. 

By using advanced techniques such as dynamically adjusting inspection time frames and applying more granular inspection based on risk, the system not only reacts to security incidents but also proactively strengthens its defenses. This inventive approach is designed to provide a resilient and ever-evolving cybersecurity solution, crucial for businesses facing the relentless rise in cyber threats. Cobalt Iron’s solution promises to make data protection smarter, more efficient, and ultimately more secure in an increasingly hostile digital world.

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