```
## Base URL
All API endpoints are accessible through the base URL:
```
https://api.atoma.network
```
## Available Endpoints
Atoma provides the following endpoints:
### Standard Endpoints (OpenAI Compatible)
* Chat Completions: `/v1/chat/completions`
* Embeddings: `/v1/embeddings`
* Image Generations: `/v1/images/generations`
* Models: `/v1/models`
### Confidential Computing Endpoints
* Confidential Chat: `/v1/confidential/chat/completions`
* Confidential Embeddings: `/v1/confidential/embeddings`
* Confidential Images: `/v1/confidential/images/generations`
### Nodes
* Node Registration: `/v1/nodes`
* Node Lock Creation: `/v1/nodes/lock`
The nodes endpoints allow for:
* Node registration and public address updates in the network
* Creating node locks for confidential computing
* Managing node public keys for encrypted communication
## Key Features
1. **OpenAI Compatibility**
* Drop-in replacement for OpenAI API
* Supports all standard OpenAI endpoints
* Compatible with existing OpenAI client libraries
2. **Privacy and Security**
* Support for confidential computing
* Trusted Execution Environments (TEEs)
* End-to-end encryption for sensitive data
3. **Distributed Infrastructure**
* Global network of compute nodes
* Intelligent load balancing
* High availability and fault tolerance
## Quickstart with SDKs
Atoma provides official SDKs for easy integration:
### TypeScript/JavaScript
```typescript
import { AtomaSDK } from "atoma-sdk";
const atomaSDK = new AtomaSDK({
bearerAuth: process.env["ATOMASDK_BEARER_AUTH"] ?? "",
});
async function run() {
const result = await atomaSDK.chat.create({
messages: [
{ role: "user", content: "Hello!" }
],
model: "meta-llama/llama-3.3-70b-instruct"
});
console.log(result);
}
```
### Python
```python
from atoma_sdk import AtomaSDK
import os
with AtomaSDK(
bearer_auth=os.getenv("ATOMASDK_BEARER_AUTH", ""),
) as atoma_sdk:
result = atoma_sdk.chat.create(
messages=[
{"role": "user", "content": "Hello!"}
],
model="meta-llama/llama-3.3-70b-instruct"
)
print(result)
```
{/* Add examples for confidential */}
# Health
Source: https://docs.atoma.ai/cloud-api-reference/health/health
cloud-api-reference/openapi.yml get /health
# Create image
Source: https://docs.atoma.ai/cloud-api-reference/images/create-image
cloud-api-reference/openapi.yml post /v1/images/generations
This endpoint processes requests to generate images using AI models by forwarding them
to the appropriate AI node. The request metadata and compute units have already been
validated by middleware before reaching this handler.
## Errors
* Returns various status codes based on the underlying `handle_image_generation_response`:
- `INTERNAL_SERVER_ERROR` - If there's an error communicating with the AI node
# List models
Source: https://docs.atoma.ai/cloud-api-reference/models/list-models
cloud-api-reference/openapi.yml get /v1/models
This endpoint mimics the OpenAI models endpoint format, returning a list of
available models with their associated metadata. Each model includes standard
OpenAI-compatible fields to ensure compatibility with existing OpenAI client libraries.
# OpenRouter models listing endpoint
Source: https://docs.atoma.ai/cloud-api-reference/models/openrouter-models-listing-endpoint
cloud-api-reference/openapi.yml get /v1/open_router/models
This endpoint returns a list of available models from the OpenRouter
models file. The file is expected to be in JSON format and contains
information about the models, including their IDs and other metadata.
# Create a node lock for confidential compute
Source: https://docs.atoma.ai/cloud-api-reference/nodes/create-a-node-lock-for-confidential-compute
cloud-api-reference/openapi.yml post /v1/nodes/lock
This endpoint attempts to find a suitable node and retrieve its public key for encryption
through a two-step process:
1. First, it tries to select an existing node with a public key directly.
2. If no node is immediately available, it falls back to finding the cheapest compatible node
and acquiring a new stack entry for it.
This endpoint is specifically designed for confidential compute scenarios where
requests need to be encrypted before being processed by nodes.
## Errors
- `INTERNAL_SERVER_ERROR` - Communication errors or missing node public keys
- `SERVICE_UNAVAILABLE` - No nodes available for confidential compute
# Create node
Source: https://docs.atoma.ai/cloud-api-reference/nodes/create-node
cloud-api-reference/openapi.yml post /v1/nodes
This endpoint allows nodes to register or update their public address in the system.
When a node comes online or changes its address, it can use this endpoint to ensure
the system has its current address for routing requests.
## Errors
Returns various `AtomaProxyError` variants:
* `MissingHeader` - If the signature header is missing
* `InvalidHeader` - If the signature header is malformed
* `InvalidBody` - If:
- The request body cannot be read
- The signature is invalid
- The body cannot be parsed
- The sui address doesn't match the signature
* `InternalError` - If:
- The state manager channel is closed
- The registration event cannot be sent
- Node Sui address lookup fails
# On-Demand AI Services
Source: https://docs.atoma.ai/documentation/get-started/on-demand-offerings
Confidential generative AI APIs with multi-modal support, RAG capabilities, and composable encryption for enterprise-grade privacy
## Confidential Generative AI APIs
Atoma's on-demand offerings provide immediate access to state-of-the-art generative AI models through privacy-preserving APIs that guarantee confidentiality for both system prompts and user data. Our platform leverages **composable encryption** technology to enable unprecedented levels of privacy protection, allowing service providers and end-users to maintain control over their respective sensitive information.
### Core Capabilities
**Multi-Modal AI Processing**
* Text generation and completion (e.g. gpt-oss, Llama, Mistral, DeepSeek, Qwen, Kimi models).
* Image generation and editing (Flux, Stable Diffusion, etc).
* Audio processing and speech synthesis.
* Video analysis and generation.
* Code generation and completion.
**Retrieval-Augmented Generation (RAG)**
* Private document embedding and indexing.
* Confidential knowledge base querying.
* Secure vector database operations.
* Private semantic search capabilities.
**High-Performance Inference**
* Sub-second response times for most queries.
* Batch processing for large-scale operations.
* Real-time streaming responses.
* Auto-scaling based on demand.
## Composable Encryption: Dual Privacy Protection
Atoma's breakthrough **composable encryption** technology enables simultaneous protection of both system-level prompts (service provider IP) and user prompts (customer data), addressing a critical gap in current AI privacy solutions.
### How It Works
Dual-Layer Privacy Architecture
**Layer 1: System Prompt Protection**
* Service providers' proprietary prompts, instructions, and system configurations remain encrypted
* AI service companies can protect their prompt engineering IP and competitive advantages
* System-level context and fine-tuning parameters are never exposed
**Layer 2: User Data Protection**
* End-user prompts, documents, and responses are encrypted with user-controlled keys
* Customer data remains confidential from both Atoma and the service provider
* The combination of advanced cryptographic primitives and trusted execution ensure only the user can decrypt their data
### Real-World Use Case: Cursor IDE
Consider how Cursor IDE could leverage Atoma's composable encryption:
**Cursor's Protected Assets:**
* Proprietary system prompts that make Cursor's AI unique
* Custom code analysis algorithms and instructions
* Specialized programming assistance methodologies
* Training data and model fine-tuning parameters
**Developer's Protected Assets:**
* Private repository code and intellectual property
* Confidential business logic and algorithms
* Sensitive customer data within codebases
* Personal coding patterns and preferences
**Result:** Cursor can offer AI-powered development assistance while protecting both their competitive AI technology and their customers' proprietary code.
## Enterprise-Grade API Features
### OpenAI-Compatible Interface
```bash
# Standard OpenAI API call with Atoma's confidential computing
curl https://api.atoma.network/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $ATOMA_API_KEY" \
-d '{
"model": "gpt-oss-120b",
"messages": [
{"role": "system", "content": "Your protected system prompt"},
{"role": "user", "content": "User query with private data"}
],
"confidential": true,
"user_encryption_key": "user_public_key"
}'
```
### Advanced Privacy Controls
**Encryption Key Management**
* User-controlled encryption keys for data sovereignty.
* Hardware Security Module (HSM) key storage.
* Automatic key rotation and versioning.
* Multi-party key escrow options.
**Audit and Compliance**
* Comprehensive audit logs with cryptographic integrity.
* Close to real-time attestation of secure execution.
* Compliance reporting for GDPR, HIPAA, SOX.
**Access Controls**
* Fine-grained permission management.
* Role-based access to different encryption layers.
* Temporary access tokens with automatic expiration.
* Multi-factor authentication for sensitive operations.
# null
Source: https://docs.atoma.ai/documentation/get-started/overview
Enterprise-grade confidential AI infrastructure with cryptographic guarantees for data privacy and model weight protection
## Atoma AI: Confidential Computing for Enterprise AI
Atoma AI delivers enterprise-grade confidential computing infrastructure specifically designed for secure, large-scale AI deployments. Our platform combines Trusted Execution Environments (TEEs), advanced cryptography, and distributed ledger technologies to provide verifiable data privacy and model weight protection that meets the stringent security requirements of enterprises, developers, and AI service providers.
### Core Security Guarantees
**Data Privacy**: End-to-end encryption with execution-time protection ensures your sensitive data remains confidential throughout the entire AI processing pipeline.
**Model Weight Protection**: Proprietary AI models are isolated within hardware-secured enclaves, preventing unauthorized access or extraction of intellectual property.
**Verifiable Execution**: Cryptographic attestation provides auditable proof that your AI workloads execute within genuine secure environments without tampering.
**Compliance Ready**: Our infrastructure supports regulatory compliance frameworks including GDPR, HIPAA, and SOX through comprehensive audit trails and attestation reports.
## Platform Architecture
### Secure AI Deployment Infrastructure
Our enterprise deployment platform provides:
* **Hardware-Level Security**: Intel TDX, AMD SEV-SNP, and NVIDIA Confidential Computing technologies ensure isolation at the chip level.
* **Kubernetes Integration**: Native integration with enterprise Kubernetes clusters through simple CLI commands and automated provisioning.
* **Cryptographic Attestation**: Hardware-rooted attestation validates the integrity of both software and hardware stacks, including co-location verification.
* **Multi-Cloud Compatibility**: Seamless integration with AWS, Azure, GCP, and hybrid cloud environments.
* **Developer-Friendly SDKs**: Drop-in replacements for popular AI frameworks (OpenAI, HuggingFace, LangChain) with confidential computing enabled.
* **Enhanced Trust**: Distributed ledger technology provides transparent and auditable attestation verification and encryption key management.
**Supported Workloads:**
* Large Language Model (LLM) inference and fine-tuning.
* Multi-modal AI (text, image, video, audio).
* Retrieval-Augmented Generation (RAG) pipelines.
* Custom AI model deployment and optimization.
### On-Demand Confidential AI Services
For immediate access to confidential AI capabilities:
* **Confidential API Access**: OpenAI-compatible APIs with cryptographic guarantees for system and user prompts privacy and response confidentiality.
* **Managed Infrastructure**: Access to Atoma's TEE-enabled compute clusters without infrastructure management overhead.
* **Competitive Pricing**: Cost-effective confidential computing with transparent, usage-based pricing models.
## Enterprise Use Cases
**Financial Services**: Deploy fraud detection and risk assessment models while maintaining customer data privacy and regulatory compliance.
**Healthcare**: Process sensitive patient data for diagnostic AI while ensuring HIPAA compliance and protecting proprietary medical algorithms.
**Legal & Consulting**: Analyze confidential documents and client data using AI without exposing sensitive information to third parties.
**AI Model Providers**: Protect valuable model weights and training data while offering AI services to enterprise clients with privacy guarantees.
## Getting Started
Choose your deployment approach based on your requirements:
Access state-of-the-art AI models through privacy-preserving APIs with zero infrastructure management
Deploy and manage your own confidential AI infrastructure with full control and customization
## Technical Resources
Understand Atoma's confidential computing architecture and security model
Complete API reference and SDK documentation for developers
## Security & Compliance
**Certifications**: SOC 2 Type II, ISO 27001, FedRAMP (pending)
**Audit Reports**: Regular third-party security audits and attestation reports available for enterprise customers
**Trust & Privacy**: Learn more about our [security model and privacy guarantees](/documentation/get-started/trust-and-privacy)
# Secure AI Deployments
Source: https://docs.atoma.ai/documentation/get-started/secure-ai-deployments
Enterprise-grade confidential Kubernetes infrastructure for custom AI deployments with hardware-level security and end-to-end encryption
## 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
```yaml
# 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
```yaml
# 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)
# Technical Architecture
Source: https://docs.atoma.ai/documentation/get-started/technical-architecture
Comprehensive technical overview of the confidential computing platform, including automated Kubernetes deployments, DLT-based registry, and composable encryption
# Atoma Technical Architecture Report
## Summary
Atoma represents a breakthrough in confidential computing infrastructure, delivering the industry's first scalable platform that combines automated Kubernetes deployments, distributed ledger-based attestation, and composable encryption for enterprise AI workloads. This technical report details the core architectural components that enable Atoma to provide hardware-level security guarantees while maintaining cloud-native scalability and developer experience.
## 1. Automated Confidential Kubernetes Platform
### 1.1 Master Pod Architecture
Atoma's Kubernetes deployment model centers around a sophisticated master pod architecture that orchestrates confidential computing resources:
**Master Pod Responsibilities:**
* **Attestation Orchestration**: Coordinates hardware attestation across all worker nodes
* **Key Management**: Manages encryption key lifecycle and distribution
* **Security Policy Enforcement**: Ensures compliance with defined security policies
* **Resource Allocation**: Optimizes placement of confidential workloads based on hardware capabilities
### 1.2 Confidential Computing Integration
**Trusted Execution Environment (TEE) Stack:**
```mermaid
graph TD
A[Application Layer] --> B[Kata Container Runtime]
B --> C[Guest Kernel]
C --> D[Hypervisor]
D --> E[TEE Hardware Layer]
E --> F[Intel TDX / AMD SEV-SNP]
F --> G[CPU Hardware]
```
**Technical Implementation:**
* **Memory Isolation**: Hardware-enforced memory encryption with 256-bit AES keys
* **CPU State Protection**: Register and cache isolation preventing side-channel attacks
* **Interrupt Handling**: Secure interrupt virtualization within TEE boundaries
* **DMA Protection**: IOMMU-based protection against DMA attacks
### 1.3 Kata Containers Deep Integration
Atoma leverages Kata Containers with custom modifications for enhanced security:
**Performance Optimizations:**
* **Micro-VM Boot Time**: Fast startup time for confidential containers
* **Memory Overhead**: Lower overhead compared to standard containers
* **I/O Performance**: Near-native disk and network performance through paravirtualization
### 1.4 GPU Operator Integration
**NVIDIA Confidential Computing Integration:**
* **GPU Memory Encryption**: Hardware-based encryption of GPU memory contents
* **Secure Multi-Tenancy**: Cryptographic isolation between GPU workloads
* **Attestation Integration**: GPU hardware attestation integrated with broader platform attestation
### 1.5 Encrypted Messaging Infrastructure
**Network Security Properties:**
* **Perfect Forward Secrecy**: Unique session keys for each communication session
* **Post-Quantum Cryptography**: Quantum-resistant key exchange algorithms
* **Zero-Knowledge Networking**: Network infrastructure cannot decrypt pod communications
* **Automatic Key Rotation**: Session keys rotated on a periodic basis or upon certain threshold limit of data transfer
## 2. Atoma Registry: Distributed Ledger Infrastructure
### 2.1 Disributed Ledger-Based Attestation and Key Management
* Distributed ledger technologies allow storing remote attestation and master encryption keys in an immutable and transparent way.
* Creates a vehicle for auditing procedures, and build a full historical chain of trust.
### 2.2 Remote Attestation Verification Service
**Attestation Verification at Scale:**
* **Throughput**: Can process a large number of attestation verifications per second
* **Latency**: Low average verification time
* **Availability**: 99.99% uptime SLA with automatic failover
### 2.3 Key Registration and Discovery
**Encryption Key Lifecycle Management:**
* In order to safeguard sensitive workloads and avoid potential replay attacks from malicious actors, Atoma enforces key rotation management sessions,
allowing cluster to rotate keys in a coordinated way, without affecting their underlying services. This process ensures a smooth rotation mechanism,
increasing security guarantees without compromising availability and uptime.
## 3. Composable Encryption Architecture
### 3.1 Multi-Layered Data Protection
**Encryption Layer Architecture:**
```mermaid
graph TD
A[User Data] --> B[User Encryption Layer]
B --> C[System Encryption Layer]
C --> D[Transport Encryption Layer]
D --> E[Hardware Encryption Layer]
F[System Prompts] --> G[System Encryption Layer]
G --> D
H[Application Logic] --> I[Application Encryption Layer]
I --> C
```
### 3.2 Key Isolation and Management
## 4. High-Efficiency Deployment Architecture
### 4.1 Automated Deployment Pipeline
**Infrastructure as Code with Security Integration:**
```yaml
# Atoma deployment template
apiVersion: atoma.network/v1
kind: ConfidentialDeployment
metadata:
name: enterprise-ai-deployment
spec:
confidentialityLevel: "L3" # Hardware-isolated with attestation
scalingPolicy:
minReplicas: 3
maxReplicas: 100
targetCPUUtilization: 70
targetMemoryUtilization: 80
securityPolicy:
attestationRequired: true
keyRotationInterval: "15m"
auditLogging: "comprehensive"
networkPolicy: "zero-trust"
template:
spec:
runtimeClassName: kata-atoma
containers:
- name: ai-model
image: private-registry/model:secure-v1.0
resources:
requests:
memory: "8Gi"
cpu: "4"
nvidia.com/gpu: "1"
limits:
atoma.ai/confidential-memory: "16Gi"
atoma.ai/secure-cpu: "8"
env:
- name: ATOMA_ATTESTATION_URL
value: "https://attestation.atoma.ai"
- name: ATOMA_KEY_DISCOVERY_URL
value: "https://registry.atoma.ai/keys"
securityContext:
allowPrivilegeEscalation: false
runAsNonRoot: true
seccompProfile:
type: RuntimeDefault
capabilities:
drop: ["ALL"]
add: ["NET_BIND_SERVICE"]
```
## 5. Conclusion
Atoma AI's overall architecture represents a fundamental advancement in confidential computing infrastructure. By combining automated Kubernetes deployments, blockchain-based attestation, composable encryption, and universal SDK integration, Atoma delivers the industry's first comprehensive platform for secure, scalable AI workloads.
The platform's innovative approach to multi-layered encryption, coupled with hardware-rooted security guarantees and seamless developer experience, positions Atoma as the definitive solution for enterprises requiring both security and scalability in their AI infrastructure.
Key technical achievements include:
* **First-of-its-kind** scalable remote attestation verification service
* **Industry-leading** deployment automation for confidential computing
* **Breakthrough** composable encryption enabling dual-layer data protection
* **Comprehensive** SDK ecosystem supporting all major AI frameworks
* **Production-ready** performance with lower than 5% security overhead