AI Security Infrastructure

Multi-Layer Security Architecture

AI infrastructure security operates across four layers: physical security (facility access, surveillance, guards), network security (firewalls, segmentation, DDoS protection), data security (encryption, access controls, audit logging), and model security (model access, inference logging, adversarial attack protection).

Each layer must be independently robust — a breach at any layer compromises the entire system. For example, strong network security cannot compensate for physical access to a GPU node, which allows direct memory extraction of model weights and training data.

Data Protection & Encryption

AI infrastructure must protect training data (often proprietary or sensitive), model weights (the core IP), and inference data (user queries and responses). Encryption at rest (AES-256), encryption in transit (TLS 1.3), and secure enclaves (confidential computing) are the standard protections.

For government and defence workloads, air-gapped networks (physical disconnection from the internet), dedicated hardware (no shared tenancy), and classified security clearances for operational staff are required. Constellation's facilities support these requirements through dedicated secure zones within data center campuses.

Compliance Frameworks

AI infrastructure must meet industry-specific compliance: SOC 2 Type II (general security), ISO 27001 (information security), HIPAA (healthcare data), PCI-DSS (payment data), FedRAMP (US government), GDPR (EU data protection), and regional data sovereignty mandates. Each framework requires specific controls, audit trails, and regular third-party assessments.

Constellation's facilities are designed for multi-framework compliance from the ground up, enabling clients across regulated industries to deploy AI workloads with confidence.

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