Protect AI vs Robust Intelligence

Both compete in Red-Teaming & AI Security. Protect AI positions itself as “End-to-end security for the AI and machine-learning supply chain”, while Robust Intelligenceleads with “AI Firewall and automated model validation to secure AI from build to production”. The table below compares what each publishes.

Where Protect AI pulls ahead

Publishes support for SOC 2, which Robust Intelligence does not. Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle

Where Robust Intelligence pulls ahead

Enterprise security and ML risk teams wanting adversarial testing plus runtime protection mapped to AI-security standards

Both map to NIST AI RMF, so framework coverage alone will not separate them — the decision usually comes down to who operates the tool and how it fits your existing stack.

	Protect AI	Robust Intelligence
Positioning	End-to-end security for the AI and machine-learning supply chain	AI Firewall and automated model validation to secure AI from build to production
Category	Red-Teaming & AI Security	Red-Teaming & AI Security
Frameworks	NIST AI RMF, SOC 2	NIST AI RMF
Deployment	SaaS, On-prem, Open-source, API	SaaS, On-prem, API
Built for	Security, Data Science / ML, Risk	Security, Data Science / ML, Risk
Founded	2022	2019
Headquarters	Seattle, Washington, USA	San Francisco, California, USA
Ownership	Acquired by Palo Alto Networks (announced April 2025, ~$700M); part of Prisma AIRS	Acquired by Cisco (2024, reported ~$400M); now part of Cisco AI Defense
Funding	$60M Series B (2024) at ~$400M valuation prior to acquisition	~$44M raised prior to acquisition; valued above $200M
Pricing	Enterprise licensing; open-source tools (ModelScan, LLM Guard) free	Enterprise licensing (now sold within Cisco AI Defense)
Key capabilities	Guardian model-scanning security gateway ModelScan open-source model scanning LLM Guard runtime input/output scanners Recon automated red teaming (450+ attacks) huntr AI/ML vulnerability database and bug bounty MLSecOps supply-chain visibility	AI Firewall runtime input/output protection Algorithmic red teaming Automated model validation and stress-testing Prompt-injection and jailbreak defense OWASP LLM and MITRE ATLAS mapping Continuous production monitoring
Integrations	Amazon Bedrock, Hugging Face, MLflow / CI-CD pipelines, Major LLM providers, Palo Alto Prisma AIRS	Cisco Security Cloud / AI Defense, Major LLM providers, ML pipelines and model registries
Notable customers	None published	None published
Best for	Security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle	Enterprise security and ML risk teams wanting adversarial testing plus runtime protection mapped to AI-security standards
Limitations	Now integrated into Palo Alto Networks, so standalone products may converge into Prisma AIRS; breadth means some components (runtime guardrails vs. model scanning) are stronger than others	Absorbed into Cisco, so it is increasingly delivered as Cisco AI Defense rather than a standalone product; enterprise-oriented, less accessible to small teams

Which should you shortlist?

Choose Protect AI if security and ML teams needing to scan and defend models, pipelines, and LLM apps across the AI lifecycle

Choose Robust Intelligence if enterprise security and ML risk teams wanting adversarial testing plus runtime protection mapped to AI-security standards

Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.

Full Protect AI profile →Full Robust Intelligence profile →

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