The Cyber Security Review | Monday, January 19, 2026
FREMONT, CA: AI security encompasses methods and technologies that protect AI systems from unauthorized access, manipulation, and malicious attacks. These safeguards ensure that AI-powered systems operate correctly, maintain data integrity, and prevent data leaks or misuse.
Given the increasing reliance on AI systems, their security is important. AI security includes both technical safeguards like encryption and secure algorithms, as well as procedural ones like regular audits and compliance checks. Another definition of AI security is the application of AI to enhance security technologies such as threat intelligence, intrusion detection, and email security.
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Major risks related to AI systems
Data breaches: Data breaches are a major risk for AI systems, which frequently handle vast amounts of sensitive information. If an AI system's data storage or transmission routes are compromised, it may allow unwanted access to sensitive information. This not only violates privacy standards, but it can also have serious financial and reputational credibility for businesses. AI systems must employ robust encryption and communication protocols to diminish the risk of data breaches.
Adversarial attacks: These attacks entail modifying input data to deceive AI systems into generating inaccurate predictions or choices. These attacks exploit AI model vulnerabilities by making minor, often unnoticeable changes to the input data, causing the model to misinterpret it and produce undesirable results. AI systems should include adversarial training, which exposes models to both regular and adversarial samples during the training process to protect against adversarial attacks. This enables models to recognize and resist manipulation. Furthermore, providing robust input validation and anomaly detection systems might aid in identifying and preventing adversarial attacks.
Model theft: Model theft, also known as model inversion or extraction, is when an attacker recreates an AI model by extensively querying it and exploiting the replies to approximate its functionality. This can result in intellectual property theft and potential abuse of the model's capabilities. To prevent model theft, limiting the amount of information that may be obtained from model outputs is necessary. Techniques like differential privacy, which adds noise to outputs to disguise the underlying data, can be useful. Furthermore, installing strict access controls and monitoring usage patterns can aid in the detection and prevention of unauthorized efforts to obtain model information.
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