Company Overview
Universal confidential computing platform enabling hardware-enforced isolation and encryption for secure AI workloads.
Based in Silicon Valley (Palo Alto, CA), Anjuna offers its Anjuna Seaglass as a solution for organizations navigating the complexities of confidential computing and hardware-enforced isolation for AI systems. The platform is positioned within the broader AI Infrastructure Security category, where AI Security Intelligence tracks 12 companies building specialized capabilities.
Founded in 2018, Anjuna brings several years of market experience to its current AI security positioning, having evolved its platform through multiple technology cycles.
Why Watch This Company
Anjuna addresses a critical gap in the AI security stack: the infrastructure layer that most AI-specific security tools take for granted. As AI workloads demand specialized compute and networking, confidential computing and hardware-enforced isolation for AI systems becomes essential for organizations running production AI systems at scale.
Key Facts
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Headquarters
Palo Alto, CA
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Category
AI Infrastructure Security
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Key Product
Anjuna Seaglass
Primary Product
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Anjuna Seaglass
Universal confidential computing platform enabling hardware-enforced isolation and encryption for secure AI workloads.
AI Infrastructure Security Landscape
AI Infrastructure Security →
AI Infrastructure Security focuses on protecting the compute, network, and platform layers that underpin AI/ML workloads. As enterprises shift AI training and inference to cloud and edge environments, the infrastructure stack — GPUs, model serving endpoints, data pipelines, API gateways, and container orchestration — becomes a high-value target. This category covers solutions that secure these components without introducing latency or limiting model performance.
12 companies tracked in this category
Buyer's Evaluation Framework
Key questions to evaluate any AI Infrastructure Security vendor — including Anjuna:
Does the platform provide security controls specifically designed for GPU clusters, model serving endpoints, and AI pipeline infrastructure?
Can the solution inspect and enforce policies on AI/ML API traffic without adding significant latency to inference calls?
How does the vendor handle multi-cloud and hybrid AI deployments where workloads span different infrastructure providers?
Does the platform integrate with container orchestration and ML pipeline tools (Kubernetes, Kubeflow, MLflow)?
Featured Profiles in AI Infrastructure Security
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Category Peers — AI Infrastructure Security
11 other companies in this category
Cato Networks
Tel Aviv, Israel
★ Featured Profile
Cloudflare
San Francisco, CA
★ Featured Profile
Cylake
Tel Aviv, Israel
Fortanix
Santa Clara, CA
Fortinet
Sunnyvale, CA
Mithril Security
Paris, France
Netskope
Santa Clara, CA
★ Featured Profile
Operant AI
San Francisco, CA
Sophos
Abingdon, UK
Trend Micro
Tokyo, Japan
Zscaler
San Jose, CA
★ Featured Profile