Robust Intelligence (Cisco) vs Arthur AI
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Robust Intelligence, now part of Cisco, is an AI application security platform positioned around algorithmic red teaming and an AI Firewall concept for safeguarding AI applications, with a focus on managing AI risk and providing end to end AI security capabilities under Cisco AI Defense.
Model and agent evaluation and monitoring platform with dashboards, alerts, guardrails and a transparent Premium plan for small teams plus enterprise options.
Feature Tags Comparison
Key Features
- Algorithmic red teaming: Cisco highlights algorithmic red teaming as a core innovation for systematically testing AI failure modes
- AI Firewall concept: Cisco states the product introduced the industrys first AI Firewall framing runtime protection for AI apps
- AI risk management: The Cisco positioning emphasizes managing AI risk across development and usage of AI applications
- Enterprise alignment: The product is described as foundational to Cisco AI Defense which targets enterprise AI security programs
- Security research base: Cisco cites ongoing research on jailbreaks and data extraction which informs practical threat models
- Demo led adoption: Cisco provides request a demo and how to buy paths rather than self serve signup and pricing
- Dashboards for model and agent KPIs with version comparison
- Custom metrics and slices to track drift and fairness
- Real time alerts via webhooks email and chat
- Agent traces showing tool calls outcomes and errors
- Guardrails and policy checks for safer responses
- Free, Premium, and Enterprise deployment options
Use Cases
- LLM jailbreak testing: Run systematic red team style tests on chatbots to identify prompt injection and unsafe output paths
- RAG leakage assessment: Evaluate retrieval systems for data leakage and tool misuse under adversarial user input
- Policy enforcement layer: Place controls around AI endpoints to block disallowed content and reduce harmful outputs
- Release gate for AI: Use security validation as a pre release checkpoint for new model versions and prompt changes
- Security operations workflow: Feed findings into SOC processes so AI incidents are tracked like other security events
- Compliance reporting: Generate evidence that AI systems are tested and monitored for risk in regulated contexts
- Track LLM answer quality and escalate low confidence cases
- Monitor drift and fairness for credit or risk models
- Alert ops when agent tool calls fail or exceed latency
- Compare model or prompt versions before full rollout
- Export reports for audits and leadership reviews
- Correlate traffic spikes with error clusters to triage
Perfect For
CISOs, security architects, AI governance leads, ML platform teams, risk and compliance teams, SOC analysts, product leaders deploying LLM apps, enterprises adopting Cisco AI Defense
MLOps leaders, platform teams, and product owners who need evaluation, monitoring, and governance to scale models and agents responsibly
Capabilities
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