Overview

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

Vendor: Holistic AI1
Description: AI governance platform that discovers AI systems including shadow AI, audits models and LLMs for bias, robustness, and data leakage, and enforces regulatory compliance across the AI lifecycle.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	Govern	Identify	Detect	Coverage	Source
AI Model	Govern: Covered	Identify: Covered	Detect: Covered	Primary	1

Framework Relevance

These frameworks include controls relevant to the asset classes Holistic AI defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Holistic AI 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)
CSA AI Controls Matrix	AI Model	Model Security; Governance, Risk and Compliance
ISO 42001	AI Model	A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems
Google SAIF	AI Model	Protect the AI model; ensure model integrity, provenance, and weight security
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)
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)
OWASP AI Exchange	AI Model	Development-time and runtime model threats: model inversion, extraction, evasion, poisoning
OWASP LLM Top 10	AI Model	LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation
OWASP Agentic Security Top 10	AI Model	ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading)

Provenance

Last sourced 2026-06-09.

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Sources

Holistic AI Governance Platform
Vendor source accessed 2026-06-09

Changelog

2026-06-09
Enriched from the Holistic AI documentation.

Found an error? Corrections are welcome. Suggest an edit.

Product Strategy and Positioning

You can use the following frameworks to understand the product’s strategy and its competitive positioning. Performing this analysis is outside the scope of the AI Defense Matrix Catalog, but the following guidance can help you with such an assessment.

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Product Strategy

Lenny Zeltser’s Guide to Creating Cybersecurity Products can help you understand key aspects of the product strategy. You can use your AI tool to gather the data and apply this framework.

Market segment: Who the product is built for: industry, size, and the persona who evaluates it.
Go-to-market motion: How it reaches buyers: top-down sales, bottom-up adoption, or open source.
Pricing model: How value is captured: per-seat, consumption, or outcome-based.
Delivery and operations: How it is deployed, configured, and maintained, including infrastructure-as-code and API coverage.
Customer trust: Certifications, transparency, and supply-chain security a buyer expects from the vendor.
Ecosystem position: A point solution, a platform others build on, or a component of a larger platform.

Strategy Defensibility

Ben Vierck’s rubric can help you assess the defensibility of the SaaS product’s strategy against competitive and other market forces. You can use it with your AI tool for a methodical analysis.

Value delivery: How much of the value is hard to replicate versus standard software a competitor could rebuild.
Switching cost: How costly it is to leave once deployed: integrations, data, workflow, and platform ties.
Compliance moat: Whether certifications or regulatory alignment are a durable advantage or table stakes for this buyer.
Problem complexity: How hard, adversarial, and fast-moving the underlying problem is to solve well.
Buyer profile: Who holds the budget, and how durable that demand is across the market.
Layer: Where the product operates: application, model, infrastructure, platform, or identity control plane.
Proprietary data, content, or IP: Whether it accumulates data, content, or IP that others would find difficult to replicate.