Lakera Guard vs Robust Intelligence (Cisco)
Compare security AI Tools
LLM security layer that blocks prompt injection data leaks and jailbreaks with a simple API policies dashboards and community to production tiers.
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.
Feature Tags Comparison
Key Features
- Single API call to detect injection leaks and jailbreaks
- Policies per application route to tailor risk tolerance
- Dashboards with attack analytics for compliance needs
- Low latency design to protect real time assistants
- Custom rules and allow lists for domain specifics
- SSO alerting and SLAs on paid production plans
- 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
Use Cases
- Protect a public chatbot from injection and jailbreak attempts
- Shield agents that browse tools and APIs from exfiltration
- Meet compliance by logging and reporting blocked risks
- Tune policies to reduce false positives in key paths
- Create allow lists for approved actions or domains
- Alert security teams with webhooks when threats spike
- 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
Perfect For
security engineers platform teams AI product owners compliance and risk leaders responsible for safe LLM deployments in production
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
Capabilities
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