Cloud computing security blind spots created by AI stem from the complexity of cloud infrastructure and unique challenges AI introduces in this environment. These blind spots can reveal vulnerabilities that traditional security measures such as strong governance, transparent AI model development, and robust data management practice may not fully address. Here, I offer some suggestions that may help you identify and understand key blind spots in more detail. However, keep in mind that as AI continues to evolve, security requirements will inevitably also move. There is much more research available on cloud security scenarios besides the notes below. (However, this article does not suggest avoiding AI in any way)
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Model Exploitation and AI Manipulation – Cyber attackers can exploit AI models deployed in cloud. These “Blind spots” may include – Adversarial attacks – Malicious actors can manipulate inputs to AI models in ways that cause them to make incorrect decisions i.e. bypassing security measures etc. Hackers can tamper with data used to train AI models, leading to compromised or biased outcomes, such as faulty threat detection algorithms etc.
Ownership Uncertainty & Data Privacy – AI often requires access to large datasets, which might include sensitive, personal, or confidential info. In cloud enviro, storage and movement of this data are complex. AI’s automated processing might lead to Data leakage – Sensitive information could inadvertently be exposed during data transfers between cloud regions or thru data-sharing between AI services – With AI processing ownership ambiguity remains very much in play when data is moving across multiple cloud platforms, raising concerns over how and where & by whom data is being stored and accessed.
AI-powered Automation Errors – AI is often used to automate some security processes i.e. anomaly detection, threat analysis, etc. However, AI systems are not infallible and can introduce a ‘False positives/negatives’, thus overlooking REAL threats i.e. generate false negatives or flag benign activities as “Malicious” as false positives, it can lead to security gaps & resource strain.
Lack of Transparency – Many AI models, such as deep learning models, functions as “black boxes” with little transparency on how they make decisions. In cloud security, this can lead to unexplainable behaviors.