AI Cybersecurity – Primary Use Cases
AI cybersecurity combines artificial intelligence and machine learning technology with traditional security tools to prevent, detect, and respond to cyberattacks. This approach helps organizations better protect against known threats, reduce the time to detect and respond to them, and improve overall incident response capabilities.
The primary use cases for AI in cybersecurity are as follows:
Detection: Using machine learning algorithms, AI can scan large amounts of data at scale to identify patterns and anomalies indicative of potential malicious activity. It can also automatically filter out non-threatening activities to reduce false positives. This can help eliminate the time required to manually analyze, investigate, and prioritize alerts (which can take three or more days on average according to a recent Orca Security study).
Prevention: AI uses machine learning to automate vulnerability scanning to identify software vulnerabilities and prioritize them for patching based on impact and dependencies, speeding up the process while improving detection accuracy. Additionally, AI can perform source code analysis to identify and flag potential threats that could be injected into an application. This enables developers to make changes prior to deployment that would mitigate the threat and ensure business operations are not interrupted.
Identify and Access Management: AI’s advanced data analysis capabilities and automated functionality make it ideal for identity and access management. It can recognize behavior and user patterns to identify suspicious activity and prompt users for additional authentication requirements (like MFA) when it detects abnormal behavior, helping organizations reduce risk and meet compliance standards.