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Enterprise Security Magazine | Monday, October 19, 2020
The vulnerability management and remediation is utilizing artificial intelligence to increase its efficiency.
FREMONT, CA: Today, artificial intelligence is influencing or solely responsible for various products and services. It is used in almost everything starting from medical imaging, optimizing the playlist, self-driving vehicles, and speech recognition. There was a lack of artificial intelligence in one area, and it is enterprise vulnerability management. Since the initial days, technologies in the vulnerability management sector have evolved significantly less. But due to immense rich historical data and multi-dimensional risk elements, the vulnerability management sector is implementing AI.
Artificial intelligence consists of various areas in advanced computer science, and it includes everything from speech recognition, natural language processing, deep learning to robotics and symbolic. AI technologists are continuously trying to automate intelligent behavior or put programming computers to conduct human tasks.
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The AI component, which is immensely utilized in several applications, is machine learning, an algorithm that uses historical data to make decisions or predictions. The accuracy of the forecast increases along the high amount of historical data. It becomes easy to improve the predictions of machine learning when more historical data is gathered.
The usage of AI in vulnerability management has been immensely inconsequential, but it will not take much time to change this situation. Several elements in the vulnerability management process can benefit from the AI techniques if it is used accurately.
Developing a Meaningful Vulnerability Risk Score
Context-based vulnerability risk scoring comes at a later stage in the entire vulnerability management procedure, but it is essential. Today, vulnerability is exceptionally identical to the risks attached to every vulnerability in the CVE (Critical Vulnerabilities and Exposures) program. It can be a useful starting point to evaluate every individuality's cruciality, but it is an insufficient measure for the vulnerability's risk to the enterprise.
The modern vulnerability management can use every AI technique to collectively develop a better understanding of the context in every asset. When a highly advanced appreciation of the asset's context is obtained, it can be collaborated with the in-depth knowledge of the vulnerability and the peripheral threat environment to develop a context-driven priority. The intelligent vulnerability management program's objective is to establish priorities and strategies to decrease risk while optimizing limited remediation resources. The only accurate process to accomplish the goal is through context-sensitive assessment of risk.
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