By providing a combination of intelligent decision-making and automation, AI technology can help reduce the rising strain on typical enterprise vulnerability management operations teams.

FREMONT, CA: Artificial intelligence's (AI) influence on several products and services that people take for granted has exploded in recent years. Consider possibilities like self-driving cars, medical imaging, speech recognition, and even playlist optimization. While AI is active in every area, one crucial area for cybersecurity and IT teams is enterprise vulnerability management.

Since the field's inception, technologies in the vulnerability management community haven't progressed much. The vulnerability management area is suitable for AI exploitation, given the rich and historical data, multi-dimensional risk aspects, and a previous brute-force strategy towards remediation.

Defining AI            

AI is a broad phrase that covers a wide range of sophisticated computer science topics, including speech recognition, natural language processing, robotics, symbolic and deep learning, and more.

Machine learning-based systems, which do not require human involvement, use previous data to produce future predictions that enhance with time. Machine learning and other AI technologies can be effective weapons in the fight against today's cyber corrupt individuals, and there are several chances to apply AI technologies in the form of vulnerability management to achieve better results.

To date, the application of AI in vulnerability management has been essentially insignificant (with a few notable exceptions, like specific ground-breaking work in scanning automation utilizing expert systems), but this is unlikely to continue for much longer. Many aspects of the vulnerability management process can considerably benefit from the proper implementation of AI technology.

Developing a Meaningful Vulnerability Risk Score

Advanced vulnerability management systems can use all AI technologies to understand every asset's context comprehensively. Once companies thoroughly understand the asset's context, they can integrate it with in-depth knowledge of the individual vulnerability and the external threat environment to create a "context-driven priority."

The main objective of an intelligent vulnerability management program is to establish priorities and a strategy to minimize risk while optimizing limited repair resources, and the only way to achieve that goal is with a context-sensitive risk assessment.

Discerning Vulnerability Exploitation Trends

Brand marketers are evaluating posts on social media platforms that mention their products utilizing AI-based sentiment analysis technologies. Gathering this data and using AI to analyze it may reveal how a brand is regarded in the market and how that perception changes with time, for good or worse.

Likewise, cybersecurity chat boards, media sites, and other online sources of cybersecurity discussions can be gathered and analyzed to determine which vulnerabilities are most likely to be exploited, which security professionals are most anxious about, and how those views evolve.

Performing Important Asset Detection

Identifying every asset, including those that appear unconventional in a given setting, is the basis of an efficient vulnerability management program. Due to the enormous amount of assets in a standard network, it might be challenging to locate network assets that are contextually out-of-the-ordinary using traditional detection procedures.