Cybersecurity
The practice of protecting systems, networks, and programs from digital attacks, crucial in safeguarding AI systems against threats.
Definition
Encompasses identity management, network segmentation, encryption, intrusion detection, and secure DevOps for AI pipelines. AI artifacts (models, data) require their own security controls—e.g., model watermarking to detect theft, encrypted model hosting to prevent tampering, and monitoring for adversarial-payload injections.
Real-World Example
A healthcare AI vendor secures its patient-risk model by storing weights in an HSM (hardware security module), authenticating all API calls via OAuth, and running continuous vulnerability scans on its Kubernetes clusters—preventing unauthorized access or malicious model modifications.
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