The Three Core Factors of Artificial Intelligence to Enhance Cybersecurity

The Three Core Factors of Artificial Intelligence to Enhance Cybersecurity

Internal Revenue Service issued a request to learn more about how artificial intelligence can improve cybersecurity.

The request went beyond just using machine-learning technologies. The agency wanted to know how to create a system that continuously learns its environment, identifies previous unknown trends and analyzes data to provide actionable data for officials.

One of the most prominent buzzwords in the federal government is Artificial intelligence, and the government has made strides to bring artificial intelligence into agencies, but it has only begun to scratch the surface of its capabilities.

Most cybersecurity solutions require too much human intervention and institutional knowledge because they use signature-based methodology. These systems require constant updates to those rules – taking up employee time and analysts only look at a single part of the enterprise. Artificial intelligence can augment that human element to be more productive.

Although the role of the human will always be an important component for cybersecurity, artificial intelligence is the science of training systems to emulate human intelligence through continuous learning. The ability for a system to learn about the environment it must protect, automatically handling tasks and searching for anomalies in user behavior. Artificial intelligence can recognizing complex patterns of malicious behavior, and analyze large volumes of data, and drive rapid detection of incidents and automated response.

The federal government has largely pieced together its cybersecurity systems in a fragmented approach to protecting systems. Artificial intelligence can help eliminate visibility gaps. Analytics help close those gaps that are a result of this approach to identify malicious activity in areas that human analysts might miss.

Artificial intelligence relies on security analytics lifecycle, which is made up of three pillars: data, discovery, and deployment. For artificial intelligence to be successful, it must be able to flow through these three pillars quickly and successfully. Let’s look at each area:

Data – For artificial intelligence to work, it needs data to analyze. The federal government has large amounts of data and with the right streams, the key will be to identify the right data to get the best results. Additionally, better information sharing between the private sector and federal government can enhance this data inventory, increasing the data available to get a more comprehensive understanding of the threat landscape, as well as best practices for mitigating those threats.

Discovery – This is the process of taking data and using technology to provide insights into security networks. With machine learning and artificial intelligence, agency personnel will build models for supervised and unsupervised purposes. It looks for outliers in the data that can show anomalies that are indicative of security incidents and finds areas of concern that human analysts would have a difficult time finding.

Deployment – This is where the value of analytics is realized. The take the findings from the discovery phase and make changes to their system to combat these issues. It is important to reemphasize, however, that better data collection, sharing, and utilization is needed to adopt more advanced capabilities like artificial intelligence.

These three steps work in accord to provide valuable insights across a government enterprise. IRS and other federal agencies are taking the right steps by first investing in advanced data analytics solutions and looking at artificial intelligence to strengthen their security posture.


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