Cybersecurity has become an important strategic imperative and enterprises today need to monitor and defend their IT assets from the ever-changing cyber threat landscape. All modern enterprises need a robust and comprehensive cybersecurity program to prevent, detect, assess, and respond to cybersecurity threats and breaches. In many ways, cybersecurity is unique – much of detection and monitoring is all about correlation and prediction—and can benefit from the infusion of artificial intelligence and machine learning solutions for assessment, analytics, and automation.
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Augmenting cybersecurity with artificial intelligence and machine learning
In a hyper-connected digital world, organizations need to process humongous quantities of data originating from disparate systems to detect anomalies, locate vulnerabilities, and pre-empt threats. Unlike most manual tracking methods, AI and ML-based systems can monitor millions of events on a daily basis and facilitate timely threat detection as well as appropriate and quick response.
AI algorithms are developed based on past and current data to define the ‘normal’ and can identify anomalies that deviate from this ‘normal’. Machine learning can then recognize a threat from these patterns and can also be used to evaluate and classify malware and conduct risk analysis.
An AI algorithm can track and record even the smallest anomaly and has a faster learning curve that better understands and analyzes user behaviour. It thus, reduces the workload of security teams which can then focus on incidents that require higher cognitive performance since the algorithms can identify and filter false alarms.
Organizations can also arrest any damage at an early stage by using AI systems to reduce the meantime to detect and the meantime to respond from days to minutes.
Security automation
Automation of security tasks and processes help improve the overall security posture of an organization and transform itself from being a deterministic enterprise into a cognitive one. It helps in the collection and correlation of security data, detection of existing compromises, and generate and implement protections much more rapidly than humanly possible.
Automation can help with complex security processes in a time-sensitive manner while avoiding manual errors and compliance issues as well as reducing the load on IT resources. It also helps by triggering self-healing processes in case of an attack facilitating quick fixes and the quarantine of injured systems.
Automating mundane and routine security processes can also free up members of the security team allowing them to focus on more strategic aspects of cybersecurity. It reduces their fatigue by keeping them at bay from multiple daily alarms and repetitive tasks like patch management, software updates, identity management, horizon scanning, etc. […]
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Read more: www.dqindia.com
Cybersecurity has become an important strategic imperative and enterprises today need to monitor and defend their IT assets from the ever-changing cyber threat landscape. All modern enterprises need a robust and comprehensive cybersecurity program to prevent, detect, assess, and respond to cybersecurity threats and breaches. In many ways, cybersecurity is unique – much of detection and monitoring is all about correlation and prediction—and can benefit from the infusion of artificial intelligence and machine learning solutions for assessment, analytics, and automation.
Copyright by www.dqindia.com
In a hyper-connected digital world, organizations need to process humongous quantities of data originating from disparate systems to detect anomalies, locate vulnerabilities, and pre-empt threats. Unlike most manual tracking methods, AI and ML-based systems can monitor millions of events on a daily basis and facilitate timely threat detection as well as appropriate and quick response.
AI algorithms are developed based on past and current data to define the ‘normal’ and can identify anomalies that deviate from this ‘normal’. Machine learning can then recognize a threat from these patterns and can also be used to evaluate and classify malware and conduct risk analysis.
An AI algorithm can track and record even the smallest anomaly and has a faster learning curve that better understands and analyzes user behaviour. It thus, reduces the workload of security teams which can then focus on incidents that require higher cognitive performance since the algorithms can identify and filter false alarms.
Organizations can also arrest any damage at an early stage by using AI systems to reduce the meantime to detect and the meantime to respond from days to minutes.
Security automation
Automation of security tasks and processes help improve the overall security posture of an organization and transform itself from being a deterministic enterprise into a cognitive one. It helps in the collection and correlation of security data, detection of existing compromises, and generate and implement protections much more rapidly than humanly possible.
Automation can help with complex security processes in a time-sensitive manner while avoiding manual errors and compliance issues as well as reducing the load on IT resources. It also helps by triggering self-healing processes in case of an attack facilitating quick fixes and the quarantine of injured systems.
Automating mundane and routine security processes can also free up members of the security team allowing them to focus on more strategic aspects of cybersecurity. It reduces their fatigue by keeping them at bay from multiple daily alarms and repetitive tasks like patch management, software updates, identity management, horizon scanning, etc. […]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.dqindia.com
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