Robust Intelligence is an AI security platform that protects machine learning models and AI applications throughout their lifecycle — from development and validation through production deployment — against adversarial attacks that traditional cybersecurity tools cannot detect. Founded by former Harvard professor Yaron Singer and Kojin Oshiba, the company built what the industry now recognizes as the first AI Firewall: a runtime defense system that monitors AI inputs and outputs in real time to block prompt injection, jailbreaking, and data poisoning attacks.
Cisco acquired Robust Intelligence for approximately $400 million in 2024, integrating the platform into Cisco Security Cloud and recognizing AI model security as a top-tier enterprise priority rather than a niche concern. Gartner named Robust Intelligence a 2024 Cool Vendor for AI Security — validating its approach to the emerging discipline of AI trust, risk, and security management (AI TRiSM).
What Robust Intelligence Does: Securing AI Models from Development to Production
Robust Intelligence addresses a fundamental gap in enterprise security: most organizations deploy AI models using security practices designed for traditional software, missing the AI-specific vulnerabilities that emerge from training data manipulation, adversarial inputs, and model architecture exploitation. The platform closes this gap with a continuous security lifecycle covering three distinct phases.
The AI Security Lifecycle Problem
AI models face attack vectors at every stage of their lifecycle. During development, data poisoning introduces corrupted or mislabeled training examples that cause models to learn exploitable biases or hidden backdoors — subtle manipulations that survive into production undetected. During integration, open-source model components sourced from repositories like Hugging Face may contain embedded malicious code or pre-poisoned weights. During production, deployed models face real-time adversarial attacks: prompt injection attempts to override system instructions, jailbreaking exploits model reasoning patterns to bypass safety guardrails, and model evasion crafts inputs specifically designed to cause misclassification.
Traditional firewalls and intrusion detection systems are not designed to identify these attacks because they operate on network traffic and signatures, not on the semantic content of AI inputs and outputs. Robust Intelligence was purpose-built to detect and block attacks at the model layer — the layer where these threats actually materialize.
Algorithmic Red Teaming
At the core of the platform is proprietary algorithmic red teaming technology that automates security testing against hundreds of attack techniques and threat categories mapped to industry standards including OWASP’s Top 10 for LLM Applications and MITRE ATLAS — the adversarial threat landscape framework specifically developed for AI/ML systems. Rather than requiring security teams to manually craft attack scenarios, the platform generates adversarial test cases algorithmically and evaluates model responses, producing a prioritized vulnerability report before any model goes to production.
Core Platform Features: Model Scanning, AI Validation, and AI Firewall
Robust Intelligence organizes its capabilities into three integrated components, each addressing a specific phase of the AI security lifecycle.
Model File Scanning
Before an AI model is deployed, the platform scans model files — including open-source components sourced from Hugging Face and similar repositories — for embedded security vulnerabilities, malicious insertions, and supply chain risks. This addresses a growing attack vector: threat actors increasingly target popular open-source model repositories, embedding malicious payloads into model weights that propagate to every organization that downloads and deploys the compromised model. Model File Scanning intercepts these threats before they enter the production environment.
AI Validation
The AI Validation module automates safety and security testing during model development and pre-deployment. Using algorithmic red teaming, it evaluates a model’s susceptibility to adversarial attacks across hundreds of attack scenarios, providing security teams with a comprehensive risk assessment without requiring deep machine learning expertise. The validation results map to OWASP, MITRE ATLAS, and NIST AI RMF frameworks, enabling compliance reporting alongside technical findings.
AI Firewall (AI Protection)
Once deployed, models are protected by the AI Firewall — Robust Intelligence’s runtime defense system and the innovation the company is best known for. The firewall monitors all inputs and outputs in real time, applying adaptive policy enforcement that blocks prompt injections, jailbreaking attempts, and malicious content generation without interfering with legitimate use. Unlike static allow/block rules, the firewall uses context-aware detection that evolves as attack techniques change, providing protection against zero-day adversarial techniques that have not been seen before.
Cisco Acquisition, Gartner Recognition, and Enterprise Integration
Cisco’s $400 million acquisition of Robust Intelligence in 2024 marked one of the largest transactions specifically focused on AI security — a signal that enterprise security vendors have moved from treating AI security as a future concern to treating it as an immediate procurement priority.
Integration into Cisco Security Cloud
Following the acquisition, Robust Intelligence’s technology was integrated into Cisco Security Cloud, extending Cisco’s AI security capabilities to enterprise customers already using Cisco’s broader security portfolio. Cisco formed Foundation AI — a dedicated team of AI and security experts assembled from the Robust Intelligence acquisition — to continue developing AI-native security technology and released the first open-source reasoning model built specifically for security applications.
Gartner Cool Vendor Recognition
Gartner named Robust Intelligence a 2024 Cool Vendor for AI Security, recognizing “innovative ways of securing AI applications, supporting AI trust, risk, and security management capabilities.” The Cool Vendor designation is Gartner’s recognition that a company has differentiated technology in an emerging category — in this case, the rapidly growing AI TRiSM market that Gartner projects will become a standard component of enterprise security architecture through 2026 and beyond.
Pricing and Access
Robust Intelligence / Cisco AI Defense pricing is not publicly listed. Organizations interested in deployment contact Cisco sales for custom quotes based on deployment scale, number of AI models, and integration requirements with existing Cisco Security Cloud infrastructure. For existing Cisco customers, AI Defense capabilities are accessible through the Security Cloud platform with incremental licensing.
Frequently Asked Questions
What is Robust Intelligence used for?
Robust Intelligence is an AI security platform used to protect machine learning models from adversarial attacks including prompt injection, data poisoning, jailbreaking, and model evasion. It provides three core functions: scanning AI model files for supply chain vulnerabilities, automating security testing before deployment, and providing real-time AI Firewall protection in production.
Who acquired Robust Intelligence?
Cisco acquired Robust Intelligence for approximately $400 million in 2024. The technology has been integrated into Cisco Security Cloud as Cisco AI Defense, and the Robust Intelligence team formed the foundation of Cisco’s new Foundation AI research group focused on AI-native security.
What is an AI Firewall?
An AI Firewall is a runtime security system that monitors the inputs and outputs of deployed AI models in real time, blocking adversarial attacks such as prompt injection and jailbreaking before they affect model behavior. Robust Intelligence pioneered the first commercial AI Firewall — a category now adopted by multiple vendors as AI deployment has scaled across enterprises.
How does Robust Intelligence differ from traditional security tools?
Traditional security tools — firewalls, IDS/IPS, endpoint detection — operate at the network and application layer, monitoring traffic and code for known signatures. Robust Intelligence operates at the model layer, analyzing the semantic content of AI inputs and outputs to detect attacks that exploit model reasoning, training data, and architecture rather than network vulnerabilities.
The emergence of Robust Intelligence as an AI security category leader — validated by Cisco’s $400M acquisition and Gartner’s Cool Vendor recognition — reflects a broader industry shift: AI models are now treated as critical security assets requiring dedicated protection, not just functional assets requiring performance optimization. As enterprises scale their AI deployments and attackers develop increasingly sophisticated techniques for manipulating model behavior, platforms like Robust Intelligence/Cisco AI Defense represent the foundational security layer that AI-enabled operations require to function reliably and safely at enterprise scale.
For security teams evaluating AI security posture, the key questions Robust Intelligence addresses are: Which deployed models are vulnerable to adversarial inputs today? Which open-source model components in the development pipeline carry supply chain risk? And does the organization have real-time visibility into adversarial attacks targeting production AI systems? These are not questions traditional security tooling can answer — and they represent the operational gap that purpose-built AI security platforms exist to close.
Organizations in regulated sectors — financial services, healthcare, critical infrastructure — face additional pressure to deploy AI security tooling as regulatory frameworks increasingly address AI risk. The NIST AI Risk Management Framework (AI RMF) and emerging EU AI Act requirements create compliance obligations that map directly to the kinds of testing, validation, and monitoring Robust Intelligence/Cisco AI Defense provides. For compliance-driven procurement, the platform’s alignment with OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF provides a structured audit trail that demonstrates due diligence in AI security governance.
