Artificial Intelligence (AI) has quickly proven its relevance in the current state of the cybersecurity environment,
primarily because of its ability to help organizations identify possible threats more quickly and efficiently. By constantly
analyzing large amounts of data related to the security environment, AI-powered detection systems can detect unusual
patterns of behavior that could potentially go unnoticed. This has greatly improved the monitoring process and the early
identification of threats. However, the increasing dependence on automated detection systems also poses a possible threat, as
organizations become more reliant on these systems and may feel more secure than they actually are. This paper will discuss
the risks posed by these dependencies through the analysis of the current literature available in the academic community, as
well as through a great deal of experience in the cybersecurity environment. It will cover topics such as automation bias,
adversarial attacks, and the probabilistic nature of algorithmic decision-making, all of which pose possible risks to the current
state of the security environment. To counter these risks, this paper will propose a Controlled Trust Scheme that aims to
strike a balance between the efficiency of machine-based systems and human oversight. The findings show that the
effectiveness of the current cybersecurity environment not only depends on technological development but also on proper
governance to ensure that the confidence of the organization is in line with its actual preparedness.
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