While AI enhances monitoring and threat detection, it also introduces new security risks. AI systems rely on large datasets that, if compromised, can expose sensitive operational information. Malicious actors could manipulate AI models or training data to cause misclassification, leading to system mismanagement or breaches.
Additionally, reliance on automated decision-making can create blind spots if algorithms behave unpredictably. Protecting AI-driven infrastructures requires securing data pipelines, ensuring algorithm transparency, and maintaining human oversight.
Continuous model auditing, strong access controls, and robust cybersecurity frameworks are essential to mitigating these evolving risks.
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