Network Security
Measures and controls (e.g., segmentation, firewalls, intrusion detection) to protect AI infrastructure and data pipelines from unauthorized access or tampering.
Definition
Encompasses firewalls, VLANs, zero-trust segmentation, secure API gateways, and AI-specific defenses (model-extraction detection, encrypted inference). Governance involves network-access policies, regular penetration testing, automated security compliance scans, and integration of AI-infrastructure logs into SIEM platforms to detect and respond to threats in real time.
Real-World Example
A cloud-hosted ML platform uses micro-segmentation to isolate training environments from production inference clusters, deploys web-application firewalls to protect model APIs, and feeds network logs into a SIEM system with rules to detect unusual data-exfiltration patterns—ensuring AI assets remain secure against cyber threats.
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