Purpose
Provide secure, reliable runtime and network infrastructure for model serving, gateways, vector stores, agents, and MCP—including supply-chain integrity.
Mandatory checks
Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.
AI data stores and control planes shall not be publicly reachable without authentication—proven by a current CSPM or network scan plus authenticated edge controls or private-only exposure, not by private-subnet intent alone.
- Artifact
- Inventory of in-scope AI data stores and control-plane endpoints; CSPM/network scan covering those surfaces (measuredAt ≤90 days); Authenticated edge-control config, or private-only exposure proven by the scan; Open high/critical public-exposure findings closed or waived with owner and expiry
- Pass condition
- Inventory of in-scope AI data stores and control-plane endpoints exists; 0 of those endpoints are publicly reachable without authentication in the latest CSPM/network scan; findings severity ≥ high are closed or waived with owner and expiry; authenticated edge controls are configured or private-only exposure is proven by the scan (measuredAt ≤90 days). If no AI data stores or control planes exist, score NOT_APPLICABLE.
Why this control exists
Threat mapEliminate unauthenticated public exposure of AI data stores and control planes.
Threats mitigated
Data ExfiltrationSensitive Information DisclosurePrivilege EscalationModel TheftProtects
InfrastructureNetworkDataMemoryModelsMITRE: ATLAS AML.T0049 · ATLAS AML.T0085 · ATLAS AML.T0085.000 · ATLAS AML.T0035 · ATLAS AML.T0044 · ATT&CK T1190 · ATT&CK T1530
Exposed vector databases, model registries, and agent control planes are harvested directly, without any model-layer attack being needed. Requiring authenticated, non-public access removes this path to AI artifact collection and full model access, which is among the most commonly exploited AI exposures in practice.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
Production AI runtime environments shall comply with the organization's documented patching SLA—or hold approved time-boxed waivers—proven by inventory plus vulnerability/age evidence, not by image-tag pinning alone.
- Artifact
- Documented patching SLA for production AI runtime environments; Inventory of production AI runtime environments (containers, VMs, serverless, managed AI platforms, or equivalent); Vulnerability scan and/or image/runtime age report covering that inventory (measuredAt ≤90 days); CVE/patch backlog disposition + waiver register for any SLA exceptions (owner and expiry)
- Pass condition
- Inventory of production AI runtime environments exists; a documented patching SLA covers those environments; 100% comply with that SLA or approved time-boxed waivers exist with owner and expiry; report shows 0 SLA breaches without such waivers and vulnerability/age evidence covers the inventory (measuredAt ≤90 days). If no production AI runtime environments exist, score NOT_APPLICABLE.
Why this control exists
Threat mapBound the exposure window for known vulnerabilities in AI runtime environments.
Threats mitigated
Privilege EscalationUnsafe Code ExecutionSupply Chain CompromiseProtects
InfrastructureRuntimeModelsMITRE: ATLAS AML.T0049 · ATLAS AML.T0105 · ATLAS AML.T0106 · ATT&CK T1190 · ATT&CK T1068 · ATT&CK T1611
Inference servers and agent frameworks have a fast-moving vulnerability surface with publicly available exploits. Enforced patching SLAs shorten the window in which exploitation for privilege escalation, credential access, or host escape is possible.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
Agent and tool runtimes shall communicate only with documented dependencies required for their intended function—enforced by least-privilege network and/or identity controls, not by open east-west access inside the environment.
- Artifact
- Inventory of agent/tool runtimes and their documented required dependencies; Least-privilege connectivity controls for those runtimes (network policy, security groups, service mesh, workload identity, egress gateway, private endpoints, or equivalent); Probe or reviewed test showing unauthorized internal-service access is blocked (measuredAt ≤90 days)
- Pass condition
- Inventory of agent/tool runtimes and their documented required dependencies exists; those runtimes can communicate only with those dependencies; attempts to access unauthorized internal services are blocked by network and/or identity controls (measuredAt ≤90 days). If no agent or tool runtimes exist, score NOT_APPLICABLE.
Why this control exists
Threat mapRestrict outbound reachability from agent and tool runtimes to an approved set of destinations.
Threats mitigated
Data ExfiltrationAgent HijackingTool AbuseSupply Chain CompromiseProtects
NetworkExternal SystemsDataRuntimeMITRE: ATLAS AML.T0025 · ATLAS AML.T0086 · ATLAS AML.T0072 · ATT&CK T1041 · ATT&CK T1567 · ATT&CK T1071
Egress allowlisting means a hijacked agent has nowhere to send data and no channel on which to call home. It converts a successful injection into a contained failure rather than a breach.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
Shared AI accelerator infrastructure shall enforce tenant isolation and noisy-neighbor controls—proven by documented isolation design plus a fresh isolation/capacity test meeting stated limits—not by GPU presence alone.
- Artifact
- Scope note: shared AI accelerator infrastructure present (or explicit N/A for managed-API/CPU-only/single-tenant/dedicated); Isolation design (GPU scheduling, MIG/vGPU, resource quotas, tenant QoS, or equivalent); Isolation and/or capacity test report meeting stated limits (measuredAt ≤90 days)
- Pass condition
- Inventory or explicit scope confirms shared AI accelerator infrastructure; isolation and noisy-neighbor controls are documented; and the latest isolation/capacity test (measuredAt ≤90 days) meets stated limits. If the organization does not operate shared AI accelerator infrastructure (e.g. managed-API-only, CPU-only, single-tenant, or dedicated GPU per workload), score NOT_APPLICABLE.
Why this control exists
Threat mapPrevent one tenant on shared AI accelerators from reading or starving another.
Threats mitigated
Data ExfiltrationUnauthorized Memory AccessDenial of ServiceProtects
InfrastructureDataMemoryAvailabilityMITRE: ATLAS AML.T0029 · ATT&CK T1499
Shared accelerator memory and scheduling can leak residual state between workloads or let one workload exhaust capacity for the rest. Enforced isolation and quotas prevent cross-tenant residue and denial of service against co-tenants.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
Evidence required
- Network diagrams for AI components
- Patch and image scan reports
- Artifact signing/verification configuration
Recommended checks
Strengthen posture beyond the gate. Same measurable structure; non-blocking unless elevated by organizational policy.
Deprecated: superseded by SCI-R1 (SLSA-aligned signed model/container verify-on-deploy). Retained for N−1 readers; new assessments should use the supply-chain successor Check.
- Artifact
- Cluster/admission controller config requiring signed images + last failed-unsigned admission event or drill
- Pass condition
- Unsigned model/container images cannot schedule in production namespaces; policy verified by a failed admission test within 90 days. Prefer scoring SCI-R1 instead.
Why this control exists
Threat mapEnsure only cryptographically verified AI artifacts are allowed to run in production.
Threats mitigated
Supply Chain CompromiseData and Model PoisoningUnsafe Code ExecutionProtects
ModelsInfrastructureRuntimeMITRE: ATLAS AML.T0010 · ATLAS AML.T0058 · ATLAS AML.T0018.002 · ATLAS AML.T0076 · ATT&CK T1195 · ATT&CK T1195.002
Signature verification at deploy time blocks substituted or tampered models and images, including weights carrying embedded malware. It is the enforcement step that turns a provenance claim into an actual guarantee.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
Production AI infrastructure shall be declared in infrastructure-as-code with CIS-aligned (or equivalent) policy checks on every apply/PR—proven by IaC modules, policy-scan config, and a fresh production-stack report—not by console-built stacks alone.
- Artifact
- IaC modules covering production AI infrastructure; CIS-aligned (or equivalent) policy-scan config wired to apply/PR; Latest policy-scan report for production stacks (measuredAt ≤90 days); Critical findings closed or waived with owner and expiry
- Pass condition
- Production AI infrastructure is declared in IaC; CIS-aligned (or equivalent) policy checks run on every apply/PR; critical findings for production stacks are 0 or have dated exceptions with owner and expiry (measuredAt ≤90 days). If no production AI infrastructure is managed by the organization, score NOT_APPLICABLE.
Why this control exists
Threat mapCatch insecure infrastructure defaults before they are deployed.
Threats mitigated
Configuration DriftPrivilege EscalationData ExfiltrationProtects
InfrastructureNetworkRuntimeMITRE: no technique mapped — this control addresses governance or assurance rather than a specific adversary technique.
Policy-checked declarative infrastructure catches public buckets, open security groups, and permissive roles at review time rather than after exposure. It is preventive hygiene that reduces the likelihood of the exposures INF-M1 addresses.
Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.
More detailPhilosophy, failures, practices, validations, examples, crosswalks, and evolution
Engineering philosophy
AI workloads inherit cloud security and SRE fundamentals, then add GPU/runtime and model-artifact supply-chain concerns. There is no AI exception to patching, isolation, or least privilege.
Why it matters
Compromised gateways, exposed vector DBs, or poisoned model artifacts undermine every application-layer control.
Common failures
- Vector databases publicly reachable
- Unpatched inference runtimes
- Unsigned model artifacts from unverified sources
- Overly broad network paths from agents to internal services
Severity & risk
- Severity
- high
- Impact if violated
- Risk level
- high
- Typical residual risk (impact × likelihood)
Engineering best practices
- Prefer private networking to providers where available
- Harden MCP hosts as internet-facing services
- Separate training/fine-tune clusters from serving
- Apply CIS benchmarks to hosts and Kubernetes where used
Automatic validations
- CSPM/misconfiguration scanning
- Image CVE gates in CI
- Admission controllers verifying signatures
Manual validations
- Architecture review for new inference deployments
- Supply-chain review for third-party model downloads
Examples
- A self-hosted Llama deployment sits behind a private gateway with mTLS
- Vector DB accepts connections only from the RAG service identity
References
Crosswalks
Show 18 peer mappingsexpandcollapse
Secure & Resilient Secure and Resilient
NIST AI Risk Management Framework · aligns-with
§7 Support
ISO/IEC 42001 · partial
LLM03 Supply Chain
OWASP Top 10 for Large Language Model Applications · supports
C3.1 Model Authorization & Integrity
OWASP AI Application Security Verification Standard (AISVS) · supports
C4.1 AI Workload Sandboxing & Validation
OWASP AI Application Security Verification Standard (AISVS) · partial
C4.2 AI Hardware Security
OWASP AI Application Security Verification Standard (AISVS) · partial
C4.3 Edge & Distributed AI Security
OWASP AI Application Security Verification Standard (AISVS) · partial
C6.1 Model Artifact Integrity
OWASP AI Application Security Verification Standard (AISVS) · supports
C6.2 AI BOM & Supply Chain Monitoring
OWASP AI Application Security Verification Standard (AISVS) · supports
CWE-16 Configuration
OpenCRE (Open Common Requirements Enumeration) · partial
CWE-327 Use of a Broken or Risky Cryptographic Algorithm
OpenCRE (Open Common Requirements Enumeration) · partial
L4 Deployment & Infrastructure
CSA MAESTRO (Multi-Agentic Threat Model) · supports
CC6 Logical and Physical Access
SOC 2 Trust Services Criteria · evidence-for
A1 Availability
SOC 2 Trust Services Criteria · evidence-for
Security Security
AWS Well-Architected Framework · aligns-with
Level 2 Hosted build + signed provenance
SLSA (Supply-chain Levels for Software Artifacts) · aligns-with
Level 3 Hardened builds
SLSA (Supply-chain Levels for Software Artifacts) · partial
Verify-on-deploy Verification before use
SLSA (Supply-chain Levels for Software Artifacts) · supports
Future evolution
Attested inference environments and standardized model artifact signing ecosystems.