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AI clusters are high-value targets for cyber threats, requiring a defense-in-depth strategy to safeguard data, workloads, and infrastructure. Kedar Dhuru highlighted how Juniper’s security portfolio provides end-to-end protection for AI clusters, including secure multitenant environments, without compromising performance. The presentation addressed the challenges of securing AI data centers, focusing on securing WAN and data center interconnect links and preventing data loss, which are amplified by the increased scale, performance, and multi-tenancy requirements of these environments.
Juniper’s approach to securing AI data centers involves several key use cases. These include protecting traffic between data centers, preventing DDoS attacks to maintain uptime, securing north-south traffic at the data center edge with application and network-level threat protection, and implementing segmentation within the data center to prevent lateral movement of threats. These security measures can be applied to traditional data centers and public cloud environments with the same functionalities and adapted for cloud-specific architectures. Juniper focuses on high-speed IPsec connections to ensure data encryption without creating performance bottlenecks.
Juniper uses threat detection capabilities to identify indicators of compromise, including inspecting downloaded software and models for tampering and detecting malicious communication from compromised models. Their solution employs multiple firewalls, machine learning algorithms, threat feeds to detect and block malicious activity, and a domain generation algorithm (DGA) and DNS security to protect against threats. The presentation also highlighted Juniper’s new one-rack-unit firewall with high network throughput and MACSec capabilities, along with multi-tenancy and scale-out firewall architecture features.
Personnel: Kedar Dhuru
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