Robust Intelligence (Cisco) vs CalypsoAI
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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.
Enterprise AI security that defends prompts and outputs in real time, red teams LLM applications, and provides centralized policy controls for using AI safely across apps agents and data.
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
- Real time defense: Inspect prompts and outputs to stop data leakage jailbreaks and harmful content before reaching users
- Outcome analysis: Explain guardrail decisions to analysts so tuning remains transparent and fast during incidents
- Red teaming: Continuously exercise models apps and agents to uncover bypasses and prioritize mitigations with evidence
- Central policy: Apply rules across vendors models and apps with a control plane that integrates to SIEM and SOAR
- Audit trails: Log prompts responses and actions with metadata to support compliance and forensic investigations
- Model agnostic: Protect hosted SaaS and self hosted models to future proof guardrails as model portfolios evolve
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
- LLM guardrails: Enforce policies that prevent PII exfiltration IP leakage and unsafe actions in chat apps and copilots
- Agent safety: Inspect tool calls and outputs to block risky actions in autonomous or semi autonomous workflows
- Content safety: Filter toxic or disallowed material for consumer facing experiences and community platforms
- Regulatory readiness: Produce logs and reports that map to AI safety policies and data protection frameworks
- Incident response: Route alerts to SIEM or SOAR and provide evidence packages for faster triage and learning
- Vendor neutrality: Secure multiple model providers under one policy framework to avoid lock in and gaps
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
CISO offices ML platform teams risk leaders and product security groups that need centralized AI guardrails red teaming and auditability to deploy AI safely at scale
Capabilities
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