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Cisco AI Defense: AI Model and Application Validation

Identify model vulnerabilities with AI Validation

Automatically test AI models for security and safety risks.

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Trust that your models are safe and secure

AI model and application validation performs an automated, algorithmic assessment of a model's safety and security vulnerabilities, continuously updated through AI Threat Research teams. You can then understand your application's susceptibility to emerging threats and protect against them, enforced by AI runtime guardrails.

Protect against AI supply chain attacks

Developers download models and data from public repositories, including Hugging Face and GitHub, inadvertently exposing your organization to considerable risks. AI Validation automatically scans open-source models, data, and files to block supply chain threats, such as malicious model files that can allow for arbitrary code execution in your environment. When a new model is entered into your registry, an assessment can be initiated using a simple API call.

Discover model vulnerabilities

The models selected to power your applications have safety and security implications. AI Validation tests models using algorithmically generated prompts across 200 categories, which finds susceptibility to malicious actions, such as prompt injection and data poisoning, or unintentional outcomes. This benefit extends to models in production, enabling the automatic discovery and patching of new vulnerabilities in existing models.

Create model-specific guardrails

The use of third-party guardrails protects your AI applications from learning on bad data, responding to malicious requests, and sharing unintended information. AI Validation automatically generates guardrails tailored to the specific vulnerabilities found in each model, thereby improving their effectiveness. These rules can be further modified to fit a company's industry, use case, or preferences.

Automatically enforce AI security standards across your organization

Identify the validation status of models

AI Cloud Visibility automatically discovers which models in your environment need to be validated, allowing you to initiate AI Validation directly from the dashboard.

Automate AI security across the model lifecycle

Once an initial model assessment is completed, AI Validation carries out additional processes to help ensure that your models are used securely and safely.

Simplify compliance with automated reporting

Automatically generate vulnerability reports that translate test results into an easy-to-read report that is mapped to industry and regulatory standards.

Achieve AI security excellence in your organization

AI Defense makes it easy to comply with AI security standards, including the OWASP Top 10 for LLM Applications. Learn more about individual AI risks, including how they map to standards from MITRE, NIST, and OWASP, in our AI security taxonomy.

Explore AI safety and security taxonomy

Test the models that power your application

Foundation models

Foundation models are at the core of most AI applications today, either modified with fine-tuning or purpose-built. Learn what challenges need to be addressed to keep models safe and secure.

Explore foundation models

RAG applications

Retrieval-augmented generation (RAG) is quickly becoming a standard to add rich context to LLM applications. Learn about the specific security and safety implications of RAG.

Explore RAG applications

AI chatbots and agents

Chatbots are a popular LLM application, and autonomous agents that take actions on behalf of users are starting to emerge. Learn about their security and safety risks.

Explore AI chatbots and agents

Additional resources

AI safety and security taxonomy

Understand the generative AI threat landscape with definitions, mitigations, and standards classifications.

Explore taxonomy

Fine-tuning LLMs breaks their safety and security alignment

Our research shows that fine-tuning makes models three times more susceptible to jailbreaks and over 22 times more likely to produce a harmful response.

Read blogs

AI security reference architectures

Secure design patterns and practices for teams developing LLM-powered applications.

See guide

Cisco's responsible AI principles

Cisco is dedicated to securing artificial intelligence and emerging technologies.

See principles

The enterprise choice for AI security

Close the AI security gap and unblock your AI transformation and gain comprehensive protection across your environment.

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