Machine learning (ML) is being used more and more in cybersecurity to detect threats and prevent attacks. It can be used to develop custom models to detect specific types of cyber-attacks, as well as to monitor and manage security systems. MLOps is the term for the process of using machine learning to manage and operate security systems. ML technology is extremely useful for security applications such as access control, hybrid cloud security solutions, threat detection, big data security, and much more. Here are some of the most promising applications for ML in cybersecurity.
Hybrid Cloud Security Solutions
It may not seem like it at first, but advances in machine learning and cybersecurity can also translate to better physical security, such as in hybrid cloud security camera solutions. Verkada is a leading company with a hybrid cybersecurity and security camera mission that seeks to use ML to better detect threats, backup footage, and much more.
Their hybrid security solutions can record footage to their security cameras themselves and to the cloud, so users always have a backup in case the original camera footage is damaged. It’s extremely unlikely, however, that their security cameras would be vulnerable to hackers and other malicious parties, thanks to ML encryption and the fact that each camera requires two security keys to access: one is stored on the camera itself, and the other camera is stored in the cloud. Even if a thief managed to physically steal a camera, cloud security and access controls keep them from accessing its contents.
Verkada also takes identity management seriously, which is why they partner with single sign-on (SSO) providers to manage access control to their data. This helps ensure that camera footage and other data never fall into the wrong hands, and cybersecurity measures can automatically revoke access from accounts that have been deactivated or that are no longer active to ensure they aren’t used for malicious purposes.
Custom Security Models Developed With MLOps
Machine Learning Operations, or MLOps, is the use of machine learning algorithms and techniques to help with the management and operation of data-driven systems. In the context of cybersecurity, this could mean anything from automatically detecting and responding to attacks, to monitoring and managing data flows in order to identify suspicious activity.
Essentially, it’s possible to create and deploy specialized machine learning applications developed through MLOps for any cybersecurity purpose. MLOps is based on four basic tenants of development. Here is a simple breakdown of each.
Tracking: Data provenance tracking is basically the process of keeping up with where each piece of data comes from and whether it’s up-to-date. ML is great for transforming data, collecting it in a single source, and eliminating out-of-date data or data with errors.
Automation and DevOps: DevOps is a collection of practices for the development and delivery of software solutions. For ML applications, DevOps needs to automate ML model training, as well as ML model retraining whenever software is updated. This all needs to be done in real-time.
Software Monitoring: ML models that seek to make accurate predictions need to be monitored closely so that engineers can see where each prediction came from and how the ML model was trained to arrive at its conclusions.
Reliability: Naturally, ML models won’t do much good for security or anything else if they aren’t consistently reliable. They’ll need to be tested and retested repeatedly to make sure they work.
Once you can make sull use of MLOps, you’ll be able to develop your own custom cybersecurity applications that serve your specific needs and protect all of your data.
Insider Threat Detection
Machine learning can be used to identify malicious or unauthorized activity by analyzing patterns in data. It can also be used to predict future events, such as the likelihood of a cyberattack. Both of these applications can be used for insider threat detection to shut down any malicious attacks from within an organization. By using machine learning, organizations can better protect their networks and data from cyberattacks. With ML monitoring and good access control protocols, insider threats can be predicted and thwarted before they cause trouble.
Machine learning can also be used to improve the accuracy of insider threat detection. Traditionally, insider threats have been detected using rules-based systems, which can be inaccurate. Machine learning can be used to improve the accuracy of insider threat detection by using artificial intelligence to analyze data and identify patterns that could indicate malicious or unauthorized activity.
You can even use ML to improve the efficiency of insider threat detection. Organizations can use machine learning to automate the process of identifying and investigating potential insider threats. This can help to reduce the time and resources required to identify and investigate potential threats, as well as reduce the damages caused by them.
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