AI and ML Use-Cases in Security

Technological disruption is happening in all stages. Companies face increasingly higher demands for digital resilience as more workload is digitalized, and AI in security is now an essential part of ensuring cybersecurity, which is the cost of running a business.

One of the key subjects that are associated with innovation and digitalization nowadays is Machine Learning and Artificial intelligence. These technologies don’t only bring problems with cybersecurity or increase productivity, they also are great for preventing cyber attacks and making your digital infrastructure more secure. Adopting machine learning techniques to detect attacks early on or before they occur alongside auto remediation faster than humans can respond is infinitely valuable to businesses, especially in the context of AI in security.

Commonly in the cloud these are referred to as SIEM solutions, or Security Information and Event Management Solutions. These come in many shapes and forms, from end to end out-of-the-box deployable solutions, to smaller bits and pieces that fits into an already established ecosystem or solutions.

Many of these not only allows the users to query and get access to security data in real-time but set preventative measures in place. Automated responses and alerts to key personnel. Empowering your organization to have a security operations centre to handle all cases in your digital environment, leveraging the power of AI in security.

Use-case: Identification of user anomalies.

If you are in an organisation which has many users spread over various departments, geographies, teams, and facilities with various work patterns it can be hard to baseline normal user behaviour. Sign-in and log on attempt data is rich in details and is hard to baseline on per user basis and depending on all these factors. Making it a great use-case for Machine Learning, who can analyse all these variables and patterns to increase detection efficiency in log-on attempts and reduce the need for manual processing.

Use-case: Identifying insider threats.

If you’re in a public company, or a company with significant value in intellectual property it is vital to protect against data loss and potential espionage. The way this type of data loss happens is often broad and extremely hard to detect. Businesses often monitor a wide range of variables to determine if it is taking place, whereas sometimes there is not one single variable available to display data loss. Therefore, the people monitoring these things today need a lot of variables and must recognize many different patterns in user behaviour. Machine Learning algorithms that are fed data like this can much easily detect anomalies in behaviour and monitor more variables at once than a human. This data can also be enriched by monitoring public websites for potential data leaks. Being able to detect unknown leakage scenarios than security analysts, especially with the help of AI in security, is invaluable.

Use-case: Finding Command Line Anomalies.

When managing IT infrastructure or doing automation tasks many engineers or developers might use command line tools like PowerShell to automate certain tasks. The variability and intent of different command line words can be different in different context. Making the analysis for this in a security centre quite challenging, as you need to examine the context and outcomes of the command line tools. How they affected the environment around themselves. Using machine learning tools to observe the command line tools, they can benchmark what is normal commands and outcomes of them, and potentially flag them with much greater ease. Improving day to day operations of security centres through AI in security applications.

Use-case: Hunting for threats.

As new ways of attacking businesses are emerging purely correlative and statistical analysis is not enough for security analysts to hunt for the threats in their environment. New attack surfaces and vectors such as AI or ransomware has new methods of hiding and transferring. Machine learning approach here would be to combine many of the use cases mentioned above and the data gathered from them into a centralized place, where the machine learning algorithm can track multiple instances of anomalous behaviour. Feeding security analysts with all the data they need, rather than having them use heuristics to look for the next required data steps, is where AI in security truly shines.

Use-case: Detecting malicious behaviour on the network.

With the emergence of APIs and the complex data systems, integrations, and communication channels nowadays it can be hard to monitor all data traffic. As the sheer number of endpoints and volume of data being transmitted is challenging to keep track of. Machine learning getting all this data ingested makes it much easier to monitor and flag instances of data exfiltration and lateral movement, which is critical when considering AI in security.

Use-case: Identify fraudulent activity.

For many companies it is a huge damage to brand and other value metrics when fraudulent activity takes place. Having Machine Learning algorithms monitor cases of unusual user purchase patterns, and potentially blocking them or making additional assurances before processing them is vital. This can be extra hard if the people behind the fraud have knowledge that allows them to escape detection of normal behaviour, however, these off-cases machine learning is able to predict and flag to immediately drop-down security measures in case they deem it a potential risk. While the human analysts can be slow and only deploy fixed metrics to detect these tasks, the machine learning algorithm is adaptive and can quickly pivot based on unusual behaviour, making AI in security incredibly effective.

If you want to learn more about how you can use AI and ML to secure your business reach out here: https://www.konfitech.com/contact-us

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