Robust Intelligence, now part of Cisco following its acquisition in 2024, is an AI security and validation platform that helps organizations protect their AI applications from adversarial attacks, data integrity issues, and model failures. Founded in 2019 by Yaron Singer, a professor of computer science at Harvard University, and Kojin Oshiba, and originally headquartered in San Francisco, California, the company developed technology to systematically test and secure AI models throughout their lifecycle. The platform's core product is the AI Firewall, which provides real-time protection for AI models in production by detecting and blocking adversarial inputs, prompt injections, data poisoning, and other attacks designed to manipulate model behavior. The AI Firewall inspects inputs to and outputs from AI models, applying validation rules and adversarial detection algorithms to prevent harmful or manipulated data from affecting model predictions. Robust Intelligence also provides automated AI testing through its Stress Testing product, which runs hundreds of configurable tests across categories including adversarial robustness, data integrity, bias and fairness, and model performance degradation. These tests can be integrated into CI/CD pipelines to validate models before deployment, acting as a quality gate for AI systems. The platform supports both traditional machine learning models and large language models, with specific capabilities for testing LLM applications including prompt injection detection, hallucination testing, and output safety validation. As part of Cisco's security portfolio, Robust Intelligence's technology is being integrated into Cisco's broader AI security and networking offerings. The platform is delivered as a SaaS solution and also supports on-premises deployment for organizations with strict data residency requirements. Pricing is enterprise-focused with custom contracts.

AI Bias Detection

Robust Intelligence includes bias and fairness testing as a core component of its AI validation platform. Its automated testing framework evaluates models for demographic biases, disparate impact, and fairness violations across protected attributes, helping organizations identify and address bias issues before AI models are deployed into production.

AI Content Moderation

Robust Intelligence's AI Firewall provides output validation for language models, detecting and filtering harmful, toxic, or policy-violating content generated by AI systems. Its real-time inspection capabilities help organizations ensure that AI-generated outputs comply with safety policies and content guidelines before reaching end users.

AI Cybersecurity

Robust Intelligence's AI Firewall provides real-time protection for AI models in production, detecting and blocking adversarial inputs, prompt injections, data poisoning, and other attacks designed to manipulate model behavior. It inspects inputs and outputs of AI systems to prevent adversarial exploitation, serving as a security layer specifically designed for AI applications.

AI Safety Tools

Robust Intelligence provides comprehensive AI safety validation through automated stress testing that evaluates models across adversarial robustness, data integrity, bias, and fairness. Its testing framework runs hundreds of configurable tests on AI models before deployment, acting as a quality gate that ensures AI systems meet safety and reliability standards.

AI Testing Tools

Robust Intelligence automates AI model testing through its Stress Testing product, which runs comprehensive test suites covering adversarial robustness, data integrity, bias detection, and performance degradation. These tests integrate into CI/CD pipelines, enabling organizations to validate models systematically before deployment and catch issues during development.