Robust Intelligence (Cisco)
What is Robust Intelligence (Cisco)?
Discover how Robust Intelligence (Cisco) can enhance your workflow
Key Capabilities
What makes Robust Intelligence (Cisco) powerful
Algorithmic red teaming
Systematically test AI applications for adversarial failure modes using algorithmic red teaming concepts, then turn discovered issues into repeatable regression tests for ongoing model and prompt changes.
AI firewall controls
Apply an AI Firewall style protection layer to enforce policies and reduce harmful outcomes at runtime, balancing safety with latency so user experience stays acceptable under real traffic.
Risk reporting and audit
Produce governance friendly outputs such as risk findings and evidence artifacts that can support internal audit and compliance reporting for AI systems, aligning security results with enterprise accountability needs.
AI Defense integration
Integrate into a broader Cisco AI Defense posture by mapping controls to LLM apps, agents, and RAG endpoints. Validate deployment topology, logging, and incident workflows so protections are operational not only theoretical.
Key Features
What makes Robust Intelligence (Cisco) stand out
- 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
- Policy and governance: Fit is strongest where organizations need governance for LLMs agents and RAG apps at scale
- Integration evaluation: Teams should validate how controls sit in front of models and how logs feed audits and incident response
Use Cases
How Robust Intelligence (Cisco) can help you
- 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
- Agent tool safety: Test AI agents that call tools for privilege escalation and unintended actions before rollout
- Third party model oversight: Apply consistent security checks when teams use external foundation models in apps
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
Quick Information
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Frequently Asked Questions
Is Robust Intelligence pricing public?
What legal and risk topics should teams plan for?
What systems does it need to fit technically?
Does it integrate with Cisco AI Defense?
How does it compare to basic guardrails in apps?
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