Cloud and on-premise security, configuration management, network security, and CSPM.
Stand up an AI/HAI Infrastructure Assurance program that discovers, inventories, and strategically governs all infrastructure that hosts and serves AI/HAI systems, with shadow AI infrastructure prevention as the primary L1 outcome and a defensible risk-tier rubric as the primary L2 deliverable.
Explore →Publish the priority policies and compliance map that make the AI/HAI Infrastructure Assurance program enforceable, so every inference endpoint, model registry, GPU fleet, orchestrator control plane, vector store, AI-specific CI/CD pipeline, and feature store the organization hosts and operates is governed by a documented set of rules, gated before it serves production AI workloads, and defensible to auditors and regulators.
Explore →Build the AI-assurance literacy every platform engineer, SRE, and cloud architect touching AI/HAI infrastructure needs and the practitioner skills the smaller population performing infrastructure security reviews, IaC security assessments, and platform hardening of AI/HAI systems must have, with shadow AI infrastructure awareness as the primary L1 cultural outcome.
Explore →Build and maintain a reusable threat library for the infrastructure that hosts and serves AI/HAI systems, one archetype-level threat model per infrastructure asset type, so every infrastructure asset entering the SM inventory carries a documented threat view before it is provisioned, connected to AI workloads, or exposed to external traffic.
Explore →Translate the threats from TA-Infrastructure and the policies from PC-Infrastructure into a reusable Requirements Pack for AI/HAI infrastructure, a base set plus per-archetype deltas, so every infrastructure asset hosting or serving AI workloads carries a testable Requirements-Evidence Map (REM) rather than a blank slate, and so Software-domain REMs can link directly to the Infrastructure REM of the hosting cluster.
Explore →Publish the reference architectures for safely hosting and serving each AI/HAI infrastructure archetype the organization operates, so platform and MLOps teams have a vetted "green path" that already implements SR-Infrastructure requirements and contains the threats identified by TA-Infrastructure.
Explore →Operate the design checkpoint between intake approval and build-out for every new AI/HAI infrastructure component, confirming the proposed design follows the applicable SA-Infrastructure reference pattern, covers the SR-Infrastructure requirements pack, and documents residual risks before provisioning begins.
Explore →Verify, at go-live and on a recurring cadence, that the actual configuration of AI/HAI infrastructure the organization operates matches the design approved at DR, and that it stays there as components evolve.
Explore →Prove that every AI/HAI infrastructure component the organization operates behaves correctly under adversarial conditions, by running a foundational per-archetype test battery in CI and on a defined cadence, maintaining versioned regression corpora, and escalating to scheduled red-team and continuous adversarial testing at higher maturity levels.
Explore →Run the single unified backlog for all AI infrastructure issues, findings from TA-Infrastructure threat snapshots, SR-Infrastructure gaps, DR-Infrastructure conditions, IR-Infrastructure drift, ST-Infrastructure failures, ML-Infrastructure detections, and external advisories (CVEs for AI-infra components, cloud-provider security bulletins, CNCF advisories, ATLAS updates), with a tier-calibrated incident playbook containing AI-infrastructure-specific containment plays, and regulatory SLA tracking covering GDPR Art. 33, EU AI Act Art. 73, HIPAA, NYDFS Part 500, PCI-DSS, and sector cloud regulations (FedRAMP IR, ISO/IEC 27035).
Explore →Harden the identity, network, compute, supply-chain, and egress/DLP envelopes that surround the infrastructure hosting and serving AI systems, inference endpoints, model registries, GPU/accelerator fleets, orchestrator control planes, vector-store infrastructure, AI-specific CI/CD, and feature stores, so each archetype runs in a least-privilege, observable perimeter and unsanctioned data movement across infrastructure boundaries is detectable before it causes harm.
Explore →Establish the per-archetype logging baseline for all seven AI infrastructure archetypes, operate a small high-signal detection set targeting the top threats from TA-Infrastructure, and produce the evidence trail that satisfies EU AI Act Art. 12 deployer-duty logs, GDPR processor obligations, and applicable cloud-security regulatory requirements, on demand, inside a published SLA.
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