The Key Use Cases of AI for Cybersecurity

By CIO Applications| Thursday, October 15, 2020

Companies can implement AI to improve network security by analyzing network traffic patterns and suggesting functional workloads grouping and security policy.

FREMONT, CA: Cybercriminals are developing new and sophisticated ways to access controls, firewalls, and jeopardizing highly secure networks. AI-powered solutions are necessary to solve security issues and provide opportunities to develop more robust solutions.

AI will substantially boost security systems, minimizing criminal intelligence using million of resources in case of an attack or malicious behavior.

Here are four ways how AI can improve cybersecurity:

Data Centers

AI can enhance and track important processes in the data center. Its calculative powers and constant monitoring abilities offer insights into what would optimize hardware and infrastructure efficiency and security.

Also, AI minimizes the cost of hardware maintenance by notifying when devices need to be fixed. These alerts help fix the computer before it gets damaged.

Threat Detecting

A signature-based technique can detect threats to 90 percent. AI techniques can raise identification rates to 95 percent, but with an explosion of false positives. The proper solution will be combining traditional approaches and AI, leading to a detection rate of 100 percent without any false positives.

Organizations can also implement AI to optimize the process of threat hunting by incorporating behavioral analysis.

Network Security

Traditional network security has two time-consuming components that determined security policies and understands an organization’s network topography. Companies can implement AI to improve network security by analyzing network traffic patterns and suggesting functional workloads grouping and security policy.

Vulnerability Management

Conventional vulnerability database is essential in handling and containing known vulnerabilities, however, AI and machine learning techniques can analyze user account, endpoint, and server baseline behaviors and detect abnormal behaviors. This can protect organizations before vulnerabilities are officially reported and patched.