AI’s drawbacks and difficulties in security

Published On: 28/01/2023 Author: MKK

AI’s drawbacks and difficulties in security

Although it has been demonstrated that Artificial Intelligence (AI) is highly successful in improving cybersecurity, the technology is not without its limitations and difficulties. The following is an in-short list of some of the most significant limits and potential downsides of AI in security domain:

Without human oversight and involvement, relying too much on AI-driven solutions might lead to complacency. Contextualizing AI-generated insights, making key judgments, and responding to complex cyber threats all require human judgment, experience, and intuition.

Cybercriminals are always developing new methods to circumvent AI-powered security systems. Adversaries can potentially render AI systems useless by studying and exploiting their weaknesses to create sophisticated attacks that target vulnerabilities within AI systems.

As a result, it might be difficult to grasp how AI systems arrive at particular conclusions or predictions. When it comes to trust and compliance in key cybersecurity scenarios, the lack of transparency can be a major barrier.

If the data used to train an AI model is itself biased, the resulting system could perpetuate discriminatory or unjust practices. There are also privacy concerns with the collecting, storage, and use of sensitive information because AI systems need access to massive amounts of data.

As a result, AI models may have difficulty correctly interpreting complicated or ambiguous situations due to their limited contextual comprehension. Because of this, normal actions may be mistakenly labeled as malicious, and vice versa, leading to false positives and false negatives in threat detection. Validating and interpreting AI-generated insights still need human interaction and skill.

Adversarial assaults are a threat to AI systems because they include the manipulation of input data in order to trick the AI algorithms. The efficacy of AI-driven security solutions may be compromised if attackers are able to misclassify or make inaccurate judgements using AI systems by inserting small modifications into input data.

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