An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?

The correct answer is B. Using historical data to train detection software. Training the detection software on historical threat data (B) allows the AI system to learn real-world attack patterns and automate detection with consistency - directly reducing reliance on human judgment and thus minimizing human error. Manual alert monitoring (A) inherently…

AI Security Design and Implementation

Question

Options

AManual monitoring of alerts
BUsing historical data to train detection software
CUtilizing machine learning algorithms to ensure responsible use
DConducting periodic penetration testing

How the community answered

(24 responses)

A
8% (2)
B
88% (21)
C
4% (1)

Explanation

Training the detection software on historical threat data (B) allows the AI system to learn real-world attack patterns and automate detection with consistency - directly reducing reliance on human judgment and thus minimizing human error. Manual alert monitoring (A) inherently reintroduces human error. Using ML for 'responsible use' (C) is a governance concept, not a detection strategy. Periodic penetration testing (D) identifies vulnerabilities but is a point-in-time exercise that does not improve continuous automated detection or reduce operational human error.

Topics

#AI threat detection#Machine learning training#Minimizing human error#Automated security

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