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Overview

ARPIA is an enterprise AI platform that helps organizations integrate, deploy, and manage AI within their existing infrastructure. Delivered as Platform-as-a-Service (PaaS), ARPIA connects legacy systems and data sources into a unified knowledge layer, then provides governed tooling to build and operate AI workloads on top of that foundation — with security and access control applied at each layer.

Purpose of this documentation

This document provides a technical overview of ARPIA's architecture and security framework. It is written for technical leaders, developers, security and compliance professionals, and system administrators who are implementing, operating, or evaluating the platform.

What ARPIA enables

  • Deploy enterprise AI solutions with minimal disruption to existing operations
  • Integrate legacy systems and data sources into a unified knowledge layer
  • Apply consistent security controls across AI workloads
  • Scale AI capabilities with governed access and centralized control
  • Automate workflows and support data-driven decision-making
ARPIA platform architecture

Platform architecture

The platform is organized into four layers.

Data Layer

  • Integration tooling for connecting diverse data sources
  • Enterprise storage with encryption at rest
  • Scalable architecture for large-scale data operations

Computing Layer (Workshop)

  • Containerized processing on Kubernetes clusters
  • Automated scaling and resource optimization
  • Isolated execution environment for AI workloads

Knowledge Layer (Knowledge Grid)

  • Centralized knowledge and retrieval layer
  • Unified, governed data access
  • Controlled knowledge management and distribution

Deployment Layer (AppStudio)

  • Application packaging and deployment
  • Security controls applied at deploy time
  • Lifecycle management for enterprise applications

Security framework

Protection

  • TLS 1.2 / 1.3 for data in transit
  • Encryption at rest for stored data
  • Encrypted credential and secret management
  • Network segmentation and monitoring across the platform stack

Identity and access management

  • Multi-factor authentication (MFA)
  • Single sign-on via Google Workspace and Microsoft 365 / Entra ID
  • SAML 2.0 and SCIM provisioning for enterprise deployments
  • OAuth 2.0, including for MCP server integrations
  • Role-based access control with governed, least-privilege access

Compliance and governance

  • ISO/IEC 42001:2023 (AI Management System) — certified
  • SOC 2 Type II — examination completed; report in issuance
  • Alignment with applicable data-protection regulation, including Panama Law 81
  • Audit logging, monitoring, and evidence retention
  • Sub-processor governance, including the multi-provider LLM gateway

Deployment and data residency

  • Deployment options: cloud, on-premise, and hybrid
  • Data residency configurable by deployment region
  • Documented incident response, monitoring, and alerting

Target audience

  • Enterprise architects and technical leaders
  • Security officers and compliance managers
  • Development teams and system administrators
  • AI/ML engineers and data scientists
  • IT operations teams

Together they serve as both a technical reference and an implementation guide for deploying ARPIA's enterprise AI capabilities under enterprise security standards.