TL;DR:
- Cobalt Iron secures patent (U.S. Patent 11765187) for machine learning-driven cyber inspection in its Compass® enterprise SaaS backup platform.
- The patent introduces historical analysis, machine learning integration, adaptive inspection, and policy-driven inspection to enhance cyber event detection and data validation.
- Machine learning technology advancements enable the optimization of cyber inspection tools based on security conditions and events.
- The patent empowers Cobalt Iron Compass to analyze cyber attack patterns, target specific data or applications, and improve cyber inspection effectiveness.
- Ultimately, this innovation lowers the risk of undetected cyber security events and enhances data validation operations.
Main AI News:
In a recent development, Cobalt Iron Inc. has secured a groundbreaking patent that promises to revolutionize cyber inspection through the power of machine learning. U.S. Patent 11765187, issued on September 19, introduces innovative capabilities for Cobalt Iron Compass®, an enterprise SaaS backup platform. This cutting-edge technology aims to enhance cyber event detection and data validation processes by dynamically assessing and adapting the use of cyber inspection tools.
As the frequency and complexity of cyberattacks continue to rise, businesses are confronted with the daunting task of defending their digital assets. The ever-evolving tactics of malicious actors necessitate proactive measures and constant adjustments in cybersecurity strategies. Traditional cybersecurity measures that were effective yesterday may fall short tomorrow. Moreover, the industry has been lacking comprehensive insights into the performance of cyber protection, detection, and inspection operations.
Cobalt Iron’s patent addresses these critical concerns with a range of unique features, making it a standout invention in the cybersecurity landscape:
- Historical Analysis: The patent encompasses a historical analysis of the effectiveness of various cyber inspection tools in detecting different types of cyber events within specific data types.
- Machine Learning Integration: It introduces machine learning techniques to optimize cyber inspection operations, ensuring that the most suitable tools are employed for specific data or cyber threats.
- Adaptive Inspection: The patent facilitates the automatic adjustment of cyber inspection timing based on cyber attack indicators, offering businesses the flexibility to respond swiftly to potential threats.
- Policy-Driven Inspection: It allows for policy-driven cyber inspection using multiple tools and inspection levels, tailored to different data lifecycle events and types of cyber events.
This patent introduces advancements in machine learning technology that will significantly enhance the functionality of the Cobalt Iron Compass:
- Storing and analyzing machine learning training data related to cyberattacks, inspection class policies, data protection operations, cyber inspection operations, and operational forensics data, including ransomware attacks.
- Establishing inspection-class policies based on security conditions or events, specifying inspection tools and levels for each condition or event within defined security zones.
- Monitoring for various security conditions and events and dynamically determining the appropriate inspection tool and level based on analysis of training data and inspection class policies.
- Performing cyber inspection operations, dynamically adjusting inspection tools and levels to mitigate the risk of future attacks, and adapting inspection timing based on attack indicators.
- Conducting cyber attack forensics and historical analysis to gain proactive insights into attack patterns, timings, sources, and consequences and imposing access control restrictions on data objects with similarities to those targeted in previous attacks.
For example, Cobalt Iron Compass can employ these techniques to identify attack patterns and target specific data or applications, automatically enforcing access controls to safeguard similar data or applications within the enterprise.
Furthermore, the technology allows Compass to leverage machine learning training data to optimize cyber inspection tool selection and inspection levels, thereby enhancing its effectiveness in detecting specific cyber attack patterns.
Ultimately, the business outcome is a reduced risk of undetected cyber security events and continuous improvement in data validation operations. According to Rob Marett, Chief Technology Officer at Cobalt Iron, “Organizations are in dire need of more proactive assistance in protecting their data and other IT resources and in detecting suspicious cyber activities. These new techniques apply machine learning analysis to figure out in advance which cyber inspection tools will be best for different scenarios. This allows businesses to continually optimize cyber inspection operations, thus improving their ability to detect cyber events and validate data.”
Conclusion:
Cobalt Iron’s patent marks a significant advancement in the cybersecurity market. Applying machine learning to cyber inspection offers businesses a proactive and dynamic approach to cyber threat detection and data validation. This innovation aligns with the growing need for more robust cybersecurity measures in an increasingly complex threat landscape, providing organizations with the tools to stay ahead of evolving cyber threats and protect their critical data assets.
