Which of the following is a disadvantage of machine learning in cybersecurity?

The correct answer is B. It requires extensive training data. Machine learning in cybersecurity requires extensive training data (B) to build accurate models, and obtaining enough high-quality, labeled threat data is costly, time-consuming, and often scarce - making this a genuine operational disadvantage. Why the distractors are wrong: A…

Threat Hunting Fundamentals

Question

Options

AIt can process large datasets quickly
BIt requires extensive training data
CIt can automatically update security policies
DIt can operate without any human intervention

How the community answered

(24 responses)

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

Explanation

Machine learning in cybersecurity requires extensive training data (B) to build accurate models, and obtaining enough high-quality, labeled threat data is costly, time-consuming, and often scarce - making this a genuine operational disadvantage.

Why the distractors are wrong:

A - Processing large datasets quickly is a strength of ML, not a weakness.
C - Automatically updating security policies is also considered a benefit, reducing manual overhead.
D - Operating without human intervention is similarly framed as an advantage (automation), though in practice full autonomy raises its own concerns.

Memory tip: Think "GIGO" - Garbage In, Garbage Out. ML is only as good as the data it trains on, so the hunger for large, quality datasets is the core liability to remember.

Topics

#Machine Learning#Training Data Requirements#ML Limitations#Cybersecurity Tools

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