Artificial Intelligence has now been incorporated in various fields with vast development and implementation, which have been proven to be of great benefit. Artificial Intelligence, also known as AI, is the stimulation of human intelligence in machines. They are programmed meticulously to think like human beings and replicate their activities. The main objective of an AI simulated machine is to achieve and perform all the activities done by a human being. Unlike human beings, AI machines are not forgetful as they are built with colossal storage to record all the required data. They never get tired, run on processers, and finish a specific task much faster than a human being. With the benefits it comes with, AI has now gained popularity and has been accepted into various sectors like the food industry as waiters and chefs, organizations to calculate and work on special projects, healthcare organizations to analyze and programmed to treat patients as well as perform operations and especially in the cybersecurity sector in training data modules to learn how to react to various situations and detecting anomalies/threats and risk based on patterns generated, vulnerability assessments, etc.
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AI has been proven to be beneficial in cybersecurity and has many advantages when detecting vulnerabilities and managing them. AI techniques and machine learning can be a great combination to resolve cyber-related threats, risks, and attacks, especially in vulnerability management to prevent attacks beforehand.
Defining AI and Vulnerability Assessment
AI is a blanket term consisting of numerous advanced computer science areas ranging from voice detection to typical language processing, robotics, and deep representational learning. Scientists and researchers aim to automate intelligent behavior in machines that are capable of doing human tasks. AI scientists and technologists are continuously seeking various methodologies to automate “intelligent” behavior. A single AI component used expansively in several applications is machine learning — the algorithms that support historical data/information to forecast or make decisions about a particular action. More extensive the historical data, the machine learning’s decision-making capabilities improve and make better and accurate predictions about situations or circumstances and are termed as getting smarter. It advances with time and without human interference.
Vulnerability assessment is defined as the systematic review of security weaknesses in a system or network. It assesses if the system is prone to any known vulnerabilities. If yes, it assigns severity levels to those vulnerabilities and suggests mitigation methods. The scanning process to identify vulnerabilities and resolve them are categorized into the following steps:
◉ Vulnerability Identification
◉ Analysis
◉ Risk Assessments
◉ Remediation
Development of a Significant Vulnerability Risk Score
The vulnerability score is significantly similar to the risk score attached to the vulnerabilitiesin the Critical Vulnerabilities and Exposures [CVE] program. The CVE comprises a list of records/data which contains a unique identification number. The unique identification number is used to identify, define, and catalog the vulnerabilities, which are publicly disclosed. It can also be incorporated into the products and services as per the terms of use. Though CVE is useful in determining the vulnerability and its possible risk severity, it lacks context, making it difficult to rationalize certain aspects. A vulnerability may be assigned a high-risk score but, on the specific network, the affected place may be secluded on a secured subnet, or not connected to the internet, or maybe on a device or program which has no operations or services, resulting in little or no risk to the organization. The CVE is excellent to kickstart for context-based risk analysis. Once the asset/device context is attained, it is combined with the knowledge and external threat environment, generating the context-driven priority that is accurate. This can be used to determine the vulnerability severity or the importance of the vulnerability risk/threats.
Vulnerability Exploitation: The Latest Trends
Various brand marketers use AI-based analyses to assess the posts of their products or services on different social media platforms. The result enables the brand marketing employees to understand how the public perceives their products and how it changes and how it has changed over a period of time. This is achieved by AI application, and the data is collected over time and compared to decide what is lacking and what should be improved. Similarly, cybersecurity chat boards and other online sources of cybersecurity information and interaction can be collected and analyzed. This analysis is done by AI technology, which can identify the vulnerability that is exploited chiefly based on the data collected. The technology used to analyze the data collected from multiple interactions, polls, and other information is the Neural Networks and Natural Language Processing (NLP) techniques. The NLP technique can recognize the exact meaning, positive and negative traits, accurate technical information from the transcript. AI is responsible for interpreting vast amounts of data and merging their meanings to gain context for the risks of the given vulnerabilities.
Asset Detection
It is relatively important to detect all the assets/devices for an effective vulnerability assessment, especially those atypical/uncategorized in a given context. Conventional methods are not efficient to detect uncategorized information/data/assets, such as a Linux server in windows machine with database services. These types of conditions require at-most priority from security teams. Pattern recognition, AI techniques are implemented to identify and distinguish uncategorized/unique assets. Novelty, Anomaly Detection, or outlier detection methods/algorithms are enforced to identify uncategorized assets. The most known effective algorithm is the Isolation Forest, where numerous multidimensional representations are used to compare the characteristics of the assets/devices. The uncategorized assets are detected and flagged for identification purposes.
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