AI Defense Matrix Catalog

Confidential AI

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Overview

Product details compiled from public sources, each with a citation.

Vendor
Confidential AI1agent
Description
Confidential-computing stack that runs AI inference, agents, and training inside hardware-encrypted TEEs, keeping prompts, model weights, credentials, and training data protected.1agent
Deployment
SaaS, Self-hosted5agent
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 Protect Coverage
AI-Workload Platforms Secondary 4
AI Model Secondary 3
Training Data Secondary 2
Runtime AI Data Primary 5

Framework Relevance

These frameworks include controls relevant to the asset classes Confidential AI defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Confidential AI 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
Runtime AI Data Prompts (runtime); inference 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
Runtime AI Data Data Security and Privacy Lifecycle Management; Application and Interface 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
Runtime AI Data A.7 Data for AI systems; A.8 Information for interested parties
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
Runtime AI Data Expand AI red-teaming; runtime input and output safety; prompt defense
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)
Runtime AI Data Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails)
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
Runtime AI Data AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt (system prompt leaks as inference data; the AI Coding and Orchestration Tools row owns it as configuration)
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
Runtime AI Data Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering
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)
Runtime AI Data LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling
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)
Runtime AI Data ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs)

Provenance

Last sourced 2026-07-16.

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Sources

  1. Confidential AI
    Vendor source accessed 2026-07-16
  2. c8s Is Now Generally Available
    Vendor source accessed 2026-09-22
    • “c8s allows you to run workloads such as inference, training, or processing of customer or proprietary data privately, such that your dev team, your system administrators, and your cloud provider cannot see any of the runtime data.”
  3. C8s Kubernetes Made Confidential
    Vendor source accessed 2026-09-22
    • “Sensitive artifacts like model weights stay encrypted at rest and only decrypt inside a pod that proves what it is.”
  4. Confidential AI c8s Confidential Kubernetes docs
    Vendor source accessed 2026-09-22
    • “workloads run in hardware-encrypted memory the host cannot read.”
  5. Confidential AI Pricing
    Vendor source accessed 2026-10-05
    • “Pay per token. OpenAI-compatible API, open-weight models in TEEs. Prompts and responses stay invisible to us.”

Changelog

  1. Refreshed a matrix coverage source quote.

  2. Refreshed source links and quotes against the current vendor pages.

  3. Corrected matrix coverage (erratum): removed the agent-identities row and updated the training-data citation.

  4. Added to the catalog from the Confidential AI documentation.

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