Robust Intelligence (Cisco)
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.
Robust Intelligence (Cisco) has been discontinued. The product was shut down, absorbed into another company, or now operates under a different brand. The information below is kept for reference and may describe the tool as it was.
Overview
Robust Intelligence is presented as an AI security offering that has been integrated into Cisco after acquisition. Cisco states Robust Intelligence pioneered the AI security category with research and product innovation, including algorithmic red teaming and the industrys first AI Firewall. Cisco also describes it as foundational to the development of Cisco AI Defense and Cisco Foundation AI.
In practice, this positions the product as a security layer around AI applications rather than a model building toolkit. The core value is identifying and preventing bad outcomes from AI systems, such as jailbreaks, unsafe outputs, and failures caused by model behavior under adversarial prompts or shifting inputs. Algorithmic red teaming implies systematic testing for failure modes, while an AI Firewall framing implies policy enforcement and runtime protection.
Pricing is not published as a self serve tier. As a Cisco aligned enterprise security product, adoption typically follows sales led procurement, scoped deployments, and security governance reviews. Teams should validate integration points with their AI stack, such as model gateways, RAG systems, chatbot front ends, and monitoring pipelines.
A strong evaluation includes a threat model for LLM and agent use, a test suite that covers jailbreak and data leakage risks, and a rollout plan that maps findings into remediation and policy. Also assess audit needs, reporting, and how protections affect latency and user experience. For regulated organizations, confirm how the solution aligns to risk management frameworks and incident response processes for AI applications.
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
- 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
Best for
- 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
Capabilities
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.
Frequently Asked Questions
Is Robust Intelligence pricing public?
No public self serve pricing is shown on the official site. Cisco positions Robust Intelligence as part of Cisco AI Defense with a request a demo and how to buy flow, so pricing is typically handled by quote based enterprise procurement.
What legal and risk topics should teams plan for?
AI security work often touches sensitive prompts, user data, and model outputs. Define a threat model, document acceptable use, and ensure logging and testing do not capture regulated content without controls. Align findings to remediation ownership and risk acceptance.
What systems does it need to fit technically?
It is intended to secure AI applications rather than train models. Validate fit at model gateways, chatbot endpoints, and RAG pipelines, and confirm how policy decisions are enforced in runtime requests without breaking your application logic.
Does it integrate with Cisco AI Defense?
Cisco states Robust Intelligence is foundational to Cisco AI Defense and Cisco Foundation AI. In evaluation, confirm integration points for telemetry and enforcement, and ensure your team can operationalize alerts and findings in existing security tooling.
How does it compare to basic guardrails in apps?
Basic guardrails are often prompt based and fragile. Robust Intelligence is positioned around systematic red teaming and an AI Firewall concept, so compare it on coverage of adversarial behaviors, repeatability, governance reporting, and operational integration.
