AI Defense Matrix Catalog

Manifest AI Risk

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Overview

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

Vendor
Manifest1
Description
AI bill-of-materials platform that discovers models and datasets across the enterprise, tracks their provenance, and continuously monitors them for vulnerabilities.1
Deployment
SaaS1
Status
Active1

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 IdentifyDetect Coverage
AI Model Primary 1
Training Data Secondary 1

Framework Relevance

These frameworks include controls relevant to the asset classes Manifest AI Risk defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Manifest implements these controls or is certified against them.

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Framework Asset class Relevant controls
NIST IR 8596 AI Model Models; Algorithms (model configuration)
Training Data Training data
CSA AI Controls Matrix AI Model Model Security; Governance, Risk and Compliance
Training Data Data Security and Privacy Lifecycle Management; Model Security
ISO 42001 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 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 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 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 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 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 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 2026-06-13.

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Sources

  1. Manifest AI Bill of Materials
    Vendor source accessed 2026-06-13
    • “Discover AI assets across the enterprise, including shadow AI.”
    • “AIBOMs go further by including datasets and models, the core of AI systems.”

Changelog

  1. Added to the catalog from the Manifest AI Bill of Materials documentation.

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