Matos AI SPM
Overview
Product details compiled from public sources, each with a citation.
- Vendor
- CloudMatos1agent
- Description
- Agentless posture management for AI models, training data, and AI services, surfacing misconfigurations and attack paths across AI pipelines.1agent
- Deployment
- SaaS1agent
- Status
- Active1agent
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Detect | Coverage | Source |
|---|---|---|---|---|---|
| AI-Workload Platforms | Protect: Not covered | Secondary | 1 | ||
| AI Model | Primary | 1 | |||
| Training Data | Detect: Not covered | Secondary | 1 |
Framework Relevance
These frameworks include controls relevant to the asset classes Matos AI SPM defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that CloudMatos implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI-Workload Platforms | Containers, microservices, and libraries (AI-specific subset); inference endpoints (platform side) |
| AI Model | Models; Algorithms (model configuration) | |
| Training Data | Training data | |
| CSA AI Controls Matrix | AI-Workload Platforms | Infrastructure Security; Threat & Vulnerability Management |
| AI Model | Model Security; Governance, Risk and Compliance | |
| Training Data | Data Security and Privacy Lifecycle Management; Model Security | |
| ISO 42001 | AI-Workload Platforms | A.6 AI system life cycle; A.4 Resources for AI systems |
| AI Model | A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems | |
| Training Data | A.7 Data for AI systems | |
| Google SAIF | AI-Workload Platforms | Expand strong security foundations; secure and harden the AI deployment environment |
| AI Model | Protect the AI model; ensure model integrity, provenance, and weight security | |
| Training Data | Secure training data; data-security foundations; dataset provenance and integrity | |
| SANS Critical AI Security Guidelines | AI-Workload Platforms | Conventional Security Controls (host AI within the existing ISMS; authentication and access controls; encryption at rest); AI Supply Chain Management (local vs. SaaS hosting trade-offs; internal model garden) |
| AI Model | Conventional Security Controls (protect model parameters with least privilege, encryption at rest, runtime obfuscation, and trusted execution environments); Data/Model Engineering Controls (adversarial training; alignment and fine-tuning); AI Supply Chain Management (public-model caution; transfer-attack exposure) | |
| Training Data | Conventional Security Controls (defend training data; avoid data commingling); Data/Model Engineering Controls (data-quality controls; poison-robust training); Data Minimization and Obfuscation (differential privacy; synthetic data; federated learning) | |
| MITRE ATLAS | AI-Workload Platforms | AML.T0010 AI Supply Chain Compromise; AML.T0012 Valid Accounts (platform credential abuse); container and inference-server exploits |
| AI Model | AML.T0043 Craft Adversarial Data; AML.T0024 Exfiltration via AI Inference API (subtechniques: AML.T0024.001 Invert AI Model and AML.T0024.002 Extract AI Model); AML.T0018 Manipulate AI Model (integrity and backdoor) | |
| Training Data | AML.T0020 Poison Training Data; AML.T0019 Publish Poisoned Datasets; AML.T0024.000 Infer Training Data Membership | |
| OWASP AI Exchange | AI-Workload Platforms | Development-time threats: supply chain attacks, model-platform CVEs, container escape |
| AI Model | Development-time and runtime model threats: model inversion, extraction, evasion, poisoning | |
| Training Data | Development-time threats: data poisoning, backdoor injection, dataset integrity violations | |
| OWASP LLM Top 10 | AI-Workload Platforms | LLM03 Supply Chain (compromised AI platform components); LLM04 Data and Model Poisoning (via platform) |
| AI Model | LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation | |
| Training Data | LLM04 Data and Model Poisoning; LLM03 Supply Chain (dataset provenance) | |
| OWASP Agentic Security Top 10 | AI-Workload Platforms | ASI04 Agentic Supply Chain Vulnerabilities (model and tool-platform components); ASI08 Cascading Failures (platform fault propagation) |
| AI Model | ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading) | |
| Training Data | ASI04 Agentic Supply Chain Vulnerabilities (dataset provenance and integrity) |
Provenance
Last sourced 2026-09-03.
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Sources
- AI Security Posture Management (AI-SPM)
- “Matos AI SPM safeguards your AI pipelines, detects misconfigurations, and identifies attack vectors targeting your AI services.”
- “Protect sensitive training data and operational models from compromise, ensuring seamless and secure AI adoption.”
- “Detect misconfigurations in tools like OpenAI and Amazon Bedrock, enforce best practices, and secure your pipeline with IaC scanning”
Changelog
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Added to the catalog from the CloudMatos AI Security Posture Management solution page.
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