AI Governance

```wiki #AI Governance: Server Configuration

This article details the server configuration necessary for robust AI Governance, focusing on infrastructure aspects. This is intended as a guide for new system administrators managing AI-related systems within our environment. Understanding these configurations is crucial for maintaining compliance, security, and performance. Refer to Data Security Policy and AI Ethics Guidelines for overarching principles.

Introduction to AI Governance and Server Requirements

AI Governance encompasses the policies, procedures, and technologies used to manage the risks and ensure the responsible development and deployment of Artificial Intelligence systems. Our infrastructure must support these goals. Key requirements include detailed logging, access control, audit trails, and the ability to reproduce model training runs. This necessitates specific server configurations beyond standard application hosting. See also Change Management Process.

Hardware Specifications

The following table outlines the minimum hardware specifications for AI Governance servers. These are baseline requirements and may need to be scaled based on the complexity of the AI models and data volumes.

Component	Minimum Specification	Recommended Specification
CPU	Intel Xeon Silver 4310 (12 cores)	Intel Xeon Gold 6338 (32 cores)
RAM	64 GB DDR4 ECC	128 GB DDR4 ECC
Storage (OS)	500 GB NVMe SSD	1 TB NVMe SSD
Storage (Data/Logs)	4 TB HDD (RAID 1)	8 TB HDD (RAID 10)
Network Interface	1 Gbps Ethernet	10 Gbps Ethernet

These specifications are designed to support both the governance tools themselves and the underlying AI models. Consider consulting Capacity Planning Guide for detailed calculations.

Software Stack and Configuration

The software stack is critical for enabling AI Governance features. We utilize a combination of open-source and proprietary tools. All servers will run CentOS 8 with SELinux enforced in Permissive mode initially, transitioning to Enforcing after testing.

Operating System & Baseline Security

CentOS 8 (or supported equivalent)
SELinux – Permissive (initial), Enforcing (final) - see SELinux Configuration
Firewalld – configured with strict rules (see Firewall Ruleset)
Regular security patching via Automated Patching System

Governance Tools

**MLflow:** For tracking model experiments, parameters, and results. Configured with a dedicated PostgreSQL database.
**Prometheus & Grafana:** For monitoring system performance and resource utilization. See Monitoring Dashboard Guide.
**ELK Stack (Elasticsearch, Logstash, Kibana):** For centralized logging and analysis. Logs are retained for 90 days. See Log Analysis Procedures.
**Open Policy Agent (OPA):** For enforcing policies related to data access and model deployment. Requires integration with our central identity management system, Active Directory Integration.

Detailed Server Roles & Configurations

We deploy three primary server roles dedicated to AI Governance: Model Registry, Audit Logging, and Policy Enforcement. Each role has specific configuration requirements.

Server Role	Primary Function	Key Software	Network Ports (Open)
Model Registry	Central repository for AI models and their metadata.	MLflow, PostgreSQL, Python 3.9	80, 443, 5432
Audit Logging	Collects and stores logs related to AI model usage, data access, and policy violations.	ELK Stack, Filebeat, Metricbeat	5601, 9200, 5044
Policy Enforcement	Enforces pre-defined policies regarding data access, model deployment, and usage.	Open Policy Agent (OPA), Rego policy language	8181

The Model Registry server requires careful configuration of the MLflow tracking URI and artifact storage location, as outlined in the MLflow Deployment Guide. The Audit Logging server must be integrated with all AI model deployment pipelines to ensure comprehensive coverage. Policy Enforcement relies heavily on correctly written Rego policies. See Rego Policy Writing Standards.

Scaling and High Availability Considerations

As our AI deployments grow, we must consider scaling the AI Governance infrastructure. The Model Registry and Audit Logging servers are prime candidates for horizontal scaling using a load balancer. The Policy Enforcement server can be scaled by deploying multiple instances behind a reverse proxy. Refer to High Availability Architecture for detailed information on disaster recovery and failover procedures.

Intel-Based Server Configurations

Configuration	Specifications	Benchmark
Core i7-6700K/7700 Server	64 GB DDR4, NVMe SSD 2 x 512 GB	CPU Benchmark: 8046
Core i7-8700 Server	64 GB DDR4, NVMe SSD 2x1 TB	CPU Benchmark: 13124
Core i9-9900K Server	128 GB DDR4, NVMe SSD 2 x 1 TB	CPU Benchmark: 49969
Core i9-13900 Server (64GB)	64 GB RAM, 2x2 TB NVMe SSD
Core i9-13900 Server (128GB)	128 GB RAM, 2x2 TB NVMe SSD
Core i5-13500 Server (64GB)	64 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Server (128GB)	128 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Workstation	64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000

AMD-Based Server Configurations

Configuration	Specifications	Benchmark
Ryzen 5 3600 Server	64 GB RAM, 2x480 GB NVMe	CPU Benchmark: 17849
Ryzen 7 7700 Server	64 GB DDR5 RAM, 2x1 TB NVMe	CPU Benchmark: 35224
Ryzen 9 5950X Server	128 GB RAM, 2x4 TB NVMe	CPU Benchmark: 46045
Ryzen 9 7950X Server	128 GB DDR5 ECC, 2x2 TB NVMe	CPU Benchmark: 63561
EPYC 7502P Server (128GB/1TB)	128 GB RAM, 1 TB NVMe	CPU Benchmark: 48021
EPYC 7502P Server (128GB/2TB)	128 GB RAM, 2 TB NVMe	CPU Benchmark: 48021
EPYC 7502P Server (128GB/4TB)	128 GB RAM, 2x2 TB NVMe	CPU Benchmark: 48021
EPYC 7502P Server (256GB/1TB)	256 GB RAM, 1 TB NVMe	CPU Benchmark: 48021
EPYC 7502P Server (256GB/4TB)	256 GB RAM, 2x2 TB NVMe	CPU Benchmark: 48021
EPYC 9454P Server	256 GB RAM, 2x2 TB NVMe

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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️