​
Custom Confidential AI Infrastructure

Atoma’s secure AI deployment platform transforms any data center into a confidential computing environment capable of running enterprise-scale AI workloads with hardware-level security guarantees. Our technology stack combines advanced containerization, trusted execution environments, and cryptographic networking to deliver unprecedented security without sacrificing performance or flexibility. Whether you need to deploy proprietary LLMs, protect sensitive training data, or ensure regulatory compliance, Atoma’s infrastructure provides the security foundation that enables enterprises to confidently deploy AI at scale while maintaining complete control over their intellectual property and customer data.

​
Core Technology Stack

​
Secured Kubernetes Orchestration

Atoma’s Kubernetes platform is purpose-built for confidential AI workloads:
  • Hardware-Isolated Pods: Every pod runs within hardware-secured enclaves using Intel TDX or AMD SEV-SNP
  • Encrypted Pod-to-Pod Communication: All inter-pod communication is encrypted with keys accessible only within the trust boundary of each pod
  • Confidential Container Runtime: Kata Containers provide VM-level isolation with container-like performance
  • Attestation-Based Scheduling: Kubernetes scheduler verifies hardware attestation before placing workloads
  • Secure Boot Chain: Complete boot integrity from hardware to application layer

​
End-to-End Encrypted Networking

Our networking architecture ensures that data remains encrypted throughout its entire lifecycle:

​
Trust Boundary Architecture

Pod-Level Encryption Keys
  • Each pod generates unique encryption keys within its TEE boundary
  • Decryption only possible within the specific pod’s trusted execution environment
  • Network traffic remains encrypted until it reaches the destination pod’s TEE
Cryptographic Network Fabric
  • Layer 4 encryption between all pod communications
  • Hardware-rooted key derivation and rotation
  • Zero-knowledge networking - even network administrators cannot decrypt traffic

​
Hardware-Level Security Technologies

Intel TDX (Trust Domain Extensions)
  • CPU-level isolation with encrypted memory protection
  • Hardware-enforced attestation and measurement
  • Protection against privileged software attacks and DMA attacks
AMD SEV-SNP (Secure Encrypted Virtualization)
  • Memory encryption with integrity protection
  • Secure nested paging for additional isolation
  • Guest attestation with cryptographic verification
NVIDIA GPU Confidential Computing
  • GPU memory encryption for ML workload protection
  • Secure multi-tenancy for shared GPU resources
  • Hardware attestation for GPU-accelerated workloads
Kata Containers Integration
  • Lightweight VMs with container API compatibility
  • Hardware-backed isolation between workloads
  • Minimal attack surface with optimized guest kernels

​
Enterprise Deployment Capabilities

​
Datacenter-Scale Flexibility

Atoma’s technology scales from single-node deployments to massive distributed clusters:
  • Multi-Region Deployments: Confidential workloads across geographically distributed data centers
  • Hybrid Cloud Integration: Seamless integration with existing cloud and on-premises infrastructure
  • Auto-Scaling: Confidential pods scale based on demand while maintaining security guarantees
  • Custom Hardware Support: Optimized for various CPU, GPU, and accelerator configurations

​
Bring Your Own Cloud (BYOC) Model

Leverage existing cloud investments while adding confidential computing capabilities: Cloud Provider Integration
  • AWS: Native integration with EC2 instances featuring Intel TDX and AMD SEV-SNP
  • Azure: Support for Azure Confidential Computing VMs
  • Google Cloud: Integration with Confidential GKE and Confidential VMs
  • Private Cloud: VMware vSphere, OpenStack, and bare-metal deployments
Confidential Virtual Machine (CVM) Utilization
  • Maximize ROI on existing cloud confidential computing investments
  • Seamless migration from standard to confidential workloads
  • Cloud-native security with enhanced confidential computing layers

​
AI Workload Optimization

Model Deployment & Management
  • Confidential model serving with hardware-protected inference
  • Secure model updates and version management
  • Protected fine-tuning and transfer learning workflows
  • Multi-model serving with isolated execution contexts
Data Pipeline Security
  • Encrypted data ingestion from multiple sources
  • Confidential ETL processing within secured containers
  • Protected model training with encrypted datasets
  • Secure model validation and testing environments

​
Atoma Registry: Secure Key Discovery

​
Distributed Encryption Key Management

Atoma’s registry provides a decentralized, secure mechanism for encryption key discovery and management: Key Features:
  • Distributed Ledger-Based Registry: Immutable and transparent record of deployment encryption keys and access policies
  • Zero-Knowledge Discovery: Find and authenticate encryption keys without exposing sensitive metadata
  • Automated Key Rotation: Seamless key updates with backward compatibility for existing connections
  • Multi-Party Access Control: Granular permissions for different stakeholders and use cases

​
Customer Privacy Expansion

Enable your customers to offer enhanced privacy to their end-users:

​
Privacy-as-a-Service Model

For SaaS Providers
  • Offer “confidential mode” to enterprise customers
  • Cryptographically prove data isolation between tenants
  • Enable customers to maintain control over their encryption keys
For AI Service Companies
  • Protect proprietary model weights while serving customers
  • Offer confidential fine-tuning services
  • Enable private data processing for regulated industries
Market Differentiation
  • Privacy-first positioning expands addressable market
  • Meet enterprise security requirements that competitors cannot
  • Enable new business models based on confidential computing

​
Deployment Architecture Examples

​
Financial Services: Confidential Trading Algorithm

# Confidential trading system deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: trading-ai-confidential
  annotations:
    atoma.ai/tee-required: "true"
    atoma.ai/attestation-policy: "intel-tdx"
spec:
  replicas: 3
  template:
    spec:
      runtimeClassName: kata
      containers:
      - name: trading-model
        image: private-registry/trading-ai:v2.1
        resources:
          limits:
            nvidia.com/gpu: 1
            atoma.ai/confidential-memory: "16Gi"
        env:
        - name: ATOMA_ENCRYPTION_KEY
          valueFrom:
            secretKeyRef:
              name: trading-encryption-keys
              key: model-key
        volumeMounts:
        - name: encrypted-data
          mountPath: /secure-data
      volumes:
      - name: encrypted-data
        csi:
          driver: atoma.ai/encrypted-storage

​
Healthcare: HIPAA-Compliant Medical AI

# Medical imaging AI with HIPAA compliance
apiVersion: v1
kind: Service
metadata:
  name: medical-ai-service
  annotations:
    atoma.ai/compliance: "hipaa"
    atoma.ai/audit-logging: "enabled"
spec:
  type: ClusterIP
  ports:
  - port: 8080
    targetPort: 8080
  selector:
    app: medical-ai
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: medical-ai-network-policy
spec:
  podSelector:
    matchLabels:
      app: medical-ai
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - podSelector:
        matchLabels:
          role: authorized-client
    ports:
    - protocol: TCP
      port: 8080

​
Implementation & Migration

​
Deployment Process

Phase 1: Assessment & Planning
  • Security requirements analysis
  • Infrastructure compatibility review
  • Custom deployment architecture design
  • Compliance framework mapping
Phase 2: Infrastructure Setup
  • Confidential computing cluster deployment
  • Network security configuration
  • Key management system integration
  • Monitoring and logging setup
Phase 3: Application Migration
  • Containerization with confidential computing support
  • Security policy implementation
  • Performance optimization and testing
  • Staff training and documentation
Phase 4: Production & Operations
  • Live deployment with security monitoring
  • Ongoing attestation and compliance verification
  • Regular security updates and patches
  • 24/7 support and incident response

​
Professional Services

Consulting & Architecture
  • Confidential computing strategy development
  • Custom security policy design
  • Compliance framework implementation
  • Performance optimization consulting
Implementation Support
  • Dedicated engineering team for complex deployments
  • Custom integration development
  • Migration project management
  • Training and knowledge transfer
Ongoing Management
  • Managed security operations center (SOC)
  • Continuous compliance monitoring
  • Threat detection and response
  • Regular security assessments and updates

​
Security Guarantees & Compliance

​
Technical Assurances

  • Hardware Attestation: Cryptographic proof of secure execution environment
  • Memory Encryption: All data encrypted in memory with hardware keys
  • Network Isolation: Encrypted communication with pod-level key management
  • Audit Trails: Immutable logs of all security-relevant events
  • Zero-Trust Architecture: Every component verified before processing sensitive data

​
Regulatory Compliance

  • GDPR: EU data protection with data residency controls (in progress)
  • HIPAA: Healthcare data protection with BAA support (in progress)
  • SOX: Financial data integrity and audit requirements (in progress)
  • FedRAMP: Federal security authorization (in progress)
  • ISO 27001: Information security management certification (in progress)