The Robust Intelligence platform

Protect what you test. Automated model assessments and guardrails for safe and secure AI applications.
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Comprehensive security for your AI applications

The Robust Intelligence platform automates testing for security and safety vulnerabilities of AI models in development and their protection in production. The platform includes an engine for detecting and assessing model vulnerabilities as well as the necessary guardrails to deploy safely in production. This consists of two complementary components, which can be used independently but are best when paired together:

AI Validation

Detects and assesses model vulnerabilities to various attack techniques and safety concerns through automated testing and provides the recommended guardrails required to deploy safely in production.
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AI Protection

Protect applications against attacks and undesired responses in real time with guardrails that are tailored to the specific vulnerabilities identified during model assessment.
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Protection against a wide range of threats

Robust Intelligence protects AI applications against a host of security and safety threats. These can be the result of malicious actions, such as prompt injection and data poisoning, or unintentional outcomes generated by the model. There are four primary failure categories:

Abuse Failures

Toxicity, bias, hate speech, violence, sexual content, malicious use, malicious code generation, disinformation

Privacy Failures

PII leakage, data loss, model information leakage, privacy infringement

Integrity Failures

Factual inconsistency, hallucination, off-topic responses

Availability Failures

Denial of service, increased computational cost
Learn more about individual AI risks, including how they map to standards from MITRE ATLAS and OWASP, in our AI security taxonomy.

Operationalize AI security standards across your organization

The Robust Intelligence platform is easily added into your existing workflows, automatically working in the background to protect your AI applications from development to production.

AI Validation

AI models, data, and files are automatically scanned and tested to assess security and safety vulnerabilities before usage and deployment.
SIMPLE TO USE
  • AI Platform - automate model validation by integrating it into your CI/CD pipeline, connecting your preferred model registry with a simple API
  • AI Teams - incorporate validation independently within your model development environment via our SDK
Robust Intelligence AI Validation Graphic showing vulnerabilities
A dashboard showing 4 graphics:
- Tests flagged on input
- Tests flagged on output
- Prompt injection flags on input
- Toxicity flags on output
Continuous Validation:

Post-Deployment

Evaluate production models over time

Identify and remediate vulnerabilities early for all model types
Enforce model standards across your organization
Supplement our robust test suite with custom tests to meet your needs
Simplify model governance and compliance with auto-generated reports

AI Protection

AI applications are secured by guardrails that are automatically configured to protect against the security and safety vulnerabilities detected in AI Validation.
SEAMLESS INTEGRATIONS
  • AI Application -  integrate AI Firewall guardrails into your application using a single line of code with a simple API
  • Security Teams - use WAF / WAAS integrations  that allow users to configure policies for AI Firewall and automatically block threats

Any Model

Any ML Platform

Any SIEM

Platform capabilities

It’s simple to get started with our API-based service. Just point at a model endpoint to initiate the assessment and generate specific guardrails custom-fit to your model.

Advanced detection and protection

Proprietary threat intelligence, algorithmic AI red teaming, and state-of-the art AI threat classification models power the Robust Intelligence vulnerability assessment engine that continuously improves our assessment and mitigation capabilities.

Broad coverage of attack techniques

Attack techniques detected include prompt injection, jailbreaking, role playing, Tree of Attacks with Pruning (TAP), Greedy Coordinate Gradient (GCG), instruction override, Base64 encoding attack, style injection, data poisoning, deserialization attacks, denial of service, and more. Our detections provide broad coverage against the latest attack methods and are regularly updated from threat intelligence.

Satisfy all major standards and regulations

Tests are mapped to industry and regulatory standards such as OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST Adversarial Machine Learning Taxonomy, EU AI Act, and the White House Executive Order on AI. This makes it easy to enforce your AI security policy and achieve compliance.

Integrate with security workflows

Integrations with observability and SIEM platforms such as Crowdstrike, Datadog, Splunk, and AppDynamics make it simple to share data with security and DevOps teams.

Enterprise-ready privacy and security

Enterprise features include SOC 2 compliance and security features such as data encryption at REST, TLS for communication, user authentication, role-based access control (RBAC), and secrets management. See our Trust Center to make passing InfoSec a breeze.

Seamless scalability

Seamless scalability to process production workloads on the order of billions of data points and hundreds of models. Secure your high-traffic AI applications without interruption.

Simple deployment. Broad security coverage.

Robust Intelligence makes it easy to comply with AI security standards, including the OWASP Top 10 for LLM Applications.
Top 10 for LLM ApplicationsAI Validation CoverageAI Firewall Coverage
LLM 01: Prompt injection attacks
LLM 02: Insecure output handlingNot applicable
LLM 03: Data poisoning checksNot applicable
LLM 04: Model denial of service
LLM 05: Supply chain vulnerabilitiesNot applicable
LLM 06: Sensitive information disclosure
LLM 07: Insecure plug-in designNot applicable
LLM 08: Excessive agencyNot applicable
LLM 09: Overeliance
LLM 10: Model theftNot applicable
OWASP Top 10 for LLM Applications
LLM 01: Prompt injection attacksAI Validation CoverageAI Firewall Coverage
LLM 02: Insecure output handlingAI Validation CoverageAI Firewall CoverageNot applicable
LLM 03: Data poisoning checksAI Validation CoverageAI Firewall Coverage
LLM 04: Model denial of serviceAI Validation CoverageAI Firewall Coverage
LLM 05: Supply chain vulnerabilitiesAI Validation CoverageAI Firewall Coverage
LLM 06: Sensitive information disclosureAI Validation CoverageAI Firewall Coverage
LLM 07: Insecure plug-in designAI Validation CoverageAI Firewall CoverageNot applicableNot applicable
LLM 08: Excessive agencyAI Validation CoverageAI Firewall CoverageNot applicable
LLM 09: OverelianceAI Validation CoverageAI Firewall Coverage
LLM 10: Model theftAI Validation CoverageAI Firewall CoverageNot applicable

Deployment options to fit your specifications

The Robust Intelligence platform can be hosted in our environment or yours. Either way, it’s enterprise-grade.
SaaS Cloud
Dedicated or multi-tenant solution
Supports both SDK and REST API-based management APIs
Available in multiple regions
SaaS Cloud Agent
SaaS control plane
Supports both SDK and REST API-based management APIs
Deployed in customer VPC (AWS/Azure/GCP)

Partnering for more Secure AI

See all our Partners

Technology

Standards

Robust Intelligence is collaborating with the National Institute of Standards and Technology (NIST) in the Artificial Intelligence Safety Institute Consortium to develop science-based and empirically backed guidelines and standards for AI measurement and policy, laying the foundation for AI safety across the world. This will help ready the U.S. to address the capabilities of the next generation of AI models or systems, from frontier models to new applications and approaches, with appropriate risk management strategies. NIST does not evaluate commercial products under this Consortium and does not endorse any product or service used. Additional information on this Consortium can be found here.

Services

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Robust Intelligence is collaborating with the National Institute of Standards and Technology (NIST) in the Artificial Intelligence Safety Institute Consortium to develop science-based and empirically backed guidelines and standards for AI measurement and policy, laying the foundation for AI safety across the world. This will help ready the U.S. to address the capabilities of the next generation of AI models or systems, from frontier models to new applications and approaches, with appropriate risk management strategies. NIST does not evaluate commercial products under this Consortium and does not endorse any product or service used. Additional information on this Consortium can be found here.