Robust Intelligence
Enhances AI security, automates threat detection, supports major platforms.
Key Takeaways
- What it is
- Enhances AI security, automates threat detection, supports major platforms.
- Best for
- AI Validation
- AI Protection
- Automated Threat Intelligence Updates
- Rating
- 4.5/5
What is Robust Intelligence?
Robust Intelligence is a sophisticated AI security platform designed to enhance the security and reliability of machine learning (ML) applications. Targeted at enterprises dealing with the complexities of AI, Robust Intelligence offers an end-to-end solution to prevent failures and mitigate risks associated with AI deployments, from development through to production.
Robust Intelligence distinguishes itself with a comprehensive approach to AI security that spans from model validation to ongoing protection. The platform's ability to automate threat detection and integrate continuous updates ensures that security measures remain current and effective against emerging threats.
Robust Intelligence excels in providing a comprehensive AI security solution, crucial for enterprises deploying AI applications. Its standout features, particularly the automated threat intelligence updates and extensive security measures, offer significant advantages in maintaining the integrity and compliance of AI systems. By addressing both immediate and evolving threats, Robust Intelligence supports safe and efficient AI operations.
Who is Using Robust Intelligence?
Financial Services
Protecting AI-driven financial operations and transactions.
Government & Defense
Securing AI applications used in sensitive and critical national infrastructures.
Technology Companies
Ensuring the integrity and security of various AI projects and products.
Healthcare Providers
Safeguarding AI applications in healthcare to protect against data breaches and misuse.
Uncommon Use Cases
Academic researchers and AI startups use it to ensure the security of their AI innovations and research.
Key Features
Rating Breakdown
Pricing Plans
Custom Pricing
- Robust Intelligence provides tailored pricing based on the specific needs and scale of enterprise deployments
Which Robust Intelligence plan should you pick?
Start with RIME Cloud for the managed deployment; choose RIME Enterprise for a customer VPC, or RIME Enterprise (Local) for a local Kubernetes evaluation.
RIME Cloud
Good for teams that want the lightweight managed cloud product without managing the Kubernetes cluster.
RIME Enterprise
Good for organizations that need a Robust Intelligence Kubernetes cluster deployed in their own VPC with DevOps involvement.
RIME Enterprise (Local)
Good for customers conducting local or trial evaluations using a customer-managed installation.
Robust Intelligence Use Cases
Foundation models
Evaluate and protect foundation models, including models modified through fine-tuning or purpose-built models.
Outcome: Improved security and safety of foundation-model applications.
RAG applications
Apply AI security controls to retrieval-augmented generation systems that enrich language models with supplemental context.
Outcome: Reduced risk from data leakage, prompt injection, and unsafe responses.
AI chatbots and agents
Protect chatbots and autonomous agents that generate responses or take actions on behalf of users.
Outcome: Safer production interactions and agent behavior.
Pre-production model validation
Run stress tests against models and reference or evaluation datasets before deployment.
Outcome: Earlier discovery of robustness, fairness, adversarial, and data-quality weaknesses.
Production model monitoring
Run Continuous Testing against incremental data and real-time predictions after deployment.
Outcome: Ongoing visibility into model risk and broken inputs.
Workflow Ideas with Robust Intelligence
Validate a model before production
- 1Register the model and reference/evaluation datasets
- 2Run an AI Stress Test using the SDK or REST API
- 3Review weaknesses and test results in the web client
Monitor a production model continuously
- 1Connect a model-testing agent to a workspace
- 2Activate a Continuous Testing pipeline
- 3Analyze recurring results and failing data points
Configure model-specific AI protection
- 1Run AI Validation to identify model vulnerabilities
- 2Generate guardrails tailored to those vulnerabilities
- 3Deploy AI Firewall protection to the AI application
How Robust Intelligence fits into your stack
Broad Platform Support
Compatible with major AI development environments and platforms, enhancing versatility.
API Access
Provides extensive API support for seamless integration into existing enterprise systems.
Cloud Compatibility
Fully supports cloud-based deployments, offering flexibility in how AI applications are hosted and managed.
Data Privacy Compliance
Adheres to global data protection regulations, providing assurance of legal compliance.
Pros
- +Enhanced Security: Provides robust measures to defend against emerging AI vulnerabilities, including zero-day threats.
- +Time Efficiency: Streamlines the process of deploying AI applications securely, reducing deployment time.
- +Cost-Effective: Optimizes resource usage by minimizing the need for ongoing model fine-tuning, which can save on GPU and operational costs.
- +Compliance and Standards: Aligns with major security standards such as NIST and OWASP LLM Top 10, helping ensure regulatory compliance.
Cons
- −Complexity in Integration: Initial setup and integration can be complex, particularly for large and intricate enterprise systems.
- −Learning Curve: The extensive feature set requires a period of adjustment for teams to fully leverage the platform’s capabilities.
- −Resource Intensity: While it reduces GPU resource consumption over time, the initial implementation phase may require substantial computational power.
Limitations & when to consider alternatives
Known limitations
- Enterprise-oriented deployment. The documented product is delivered through RIME Cloud, customer-managed Kubernetes/VPC deployments, or local Kubernetes installations rather than a consumer desktop or mobile app.
- Requires model and data infrastructure. Testing requires models, datasets or prediction logs, an API token, and a Robust Intelligence service domain or deployment.
- Legacy product documentation. The legacy RIME documentation contains versioned releases and deprecations, while the official site states Robust Intelligence became part of Cisco in October 2024.
- No public self-serve pricing shown. Official materials direct prospective users toward demos or trials rather than publishing public dollar-based plan pricing.
How to Use Robust Intelligence
- 1
Robust Intelligence offers a wealth of documentation and tutorials on their website, covering everything from basic setup to advanced security configurations.
Who is Robust Intelligence For?
Data scientists
Use AI Stress Testing and Continuous Testing to find model weaknesses, monitor production behavior, and analyze test results.
AI risk officers
Evaluate model robustness, adversarial vulnerability, fairness, and security before models reach production.
Enterprise security teams
Use AI Firewall and threat detection to protect production AI applications from prompt injection, data leakage, jailbreaks, and other threats.
DevOps teams
Deploy RIME Enterprise in a customer-managed Kubernetes cluster or VPC and connect agents to workspaces.
Organizations building foundation models, RAG applications, chatbots, and agents
Apply AI validation and protection to the AI application types specifically named by Robust Intelligence.
Frequently Asked Questions
What does Robust Intelligence do?
It validates AI models before deployment and protects AI applications in production against security, safety, and reliability threats.
Does Robust Intelligence provide an API?
Yes. The documentation describes API tokens, REST endpoints, and a Python SDK for creating projects, registering models, and running tests.
How is Robust Intelligence deployed?
RIME Cloud is managed by Robust Intelligence; RIME Enterprise can run in a customer VPC or locally on Kubernetes.
What security controls are documented?
The security center documents AES-256 encryption at rest, TLS in transit, OIDC SSO, RBAC, workspaces, and audit logs.
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