Company Overview
Industrial AI robotics company building safe and reliable autonomous robots with AI safety at the core.
Based in Silicon Valley (San Jose, CA), Robust AI offers its Robust AI Carter as a solution for organizations navigating the complexities of adversarial robustness testing and attack mitigation for ML models. The platform is positioned within the broader AI Model Security category, where AI Security Intelligence tracks 9 companies building specialized capabilities.
Founded in 2019, Robust AI brings several years of market experience to its current AI security positioning, having evolved its platform through multiple technology cycles.
Why Watch This Company
Model security is the cybersecurity category that most closely mirrors the evolution of software supply chain security a decade ago — and Robust AI is addressing this parallel head-on. Their approach to adversarial robustness testing and attack mitigation for ML models tackles a threat surface that will only expand as organizations deploy more models in more critical workflows.
Key Facts
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Headquarters
San Jose, CA
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Category
AI Model Security
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Key Product
Robust AI Carter
Primary Product
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Robust AI Carter
Industrial AI robotics company building safe and reliable autonomous robots with AI safety at the core.
AI Model Security Landscape
AI Model Security →
AI Model Security protects machine learning models from adversarial manipulation, supply chain compromise, intellectual property theft, and integrity attacks throughout their lifecycle. As models become core enterprise assets — trained on proprietary data and deployed in critical decision paths — they represent high-value targets for adversaries seeking to poison training data, inject backdoors, steal model weights, or manipulate inference outputs.
9 companies tracked in this category
Buyer's Evaluation Framework
Key questions to evaluate any AI Model Security vendor — including Robust AI:
Can the platform scan ML models for vulnerabilities, backdoors, and malicious payloads before deployment?
Does the solution verify model provenance and integrity throughout the ML supply chain?
How does the vendor protect against adversarial attacks — both at training time (data poisoning) and inference time (evasion attacks)?
Does the platform support the full range of model architectures, including LLMs, vision models, and multi-modal systems?
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Category Peers — AI Model Security
8 other companies in this category