Vulnerability management involves an ongoing cycle of identifying, prioritizing and mitigating vulnerabilities within software applications, networks and computer systems. This proactive strategy is essential for safeguarding an organization’s digital assets and maintaining its security and integrity.
To make the process simpler and easier, we need to involve artificial intelligence (AI). Let’s examine how AI is effective for vulnerability management and how it can be implemented.
Artificial intelligence in vulnerability management
Using AI will take vulnerability management to the next level. AI not only reduces analysis time but also effectively identifies threats.
Once we have decided to use AI for vulnerability management, we need to gather information on how we would like AI to respond and what kind of data needs to be analyzed to identify the right algorithms. AI algorithms and machine learning techniques excel at detecting sophisticated and previously unseen threats.
Figure 1: Chart depicting a regression line.
By analyzing vast volumes of data, including security logs, network traffic logs and threat intelligence feeds, AI-driven systems can identify patterns and anomalies that signify potential vulnerabilities or attacks. Converting the logs into data and charts will make analysis simpler and quicker. Incidents should be identified based on the security risk, and notification should take place for immediate action.
Self-learning is another area where AI can be trained with data. This will enable AI to be up-to-date on the changing environment and capable of addressing new and emerging threats. AI will identify high-risk threats and previously unseen threats.
Implementing AI requires iterations to trai ..
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