Evaluation: Ensuring Governance, Security, and Enterprise Readiness for AI Models
We rigorously evaluate AI models for safety, privacy, and security, ensuring they are certified for enterprise use cases with robust governance and guardrails.
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What it is?
Model evaluation is a critical step in AI governance, focusing on testing models for safety, privacy, and security while setting up enterprise guardrails. At Zangoh, our evaluation process goes beyond domain knowledge testing and benchmarks models for governance using LLMs, HITL, and red teaming. The goal is to ensure your AI models meet enterprise standards, protecting sensitive data and preventing misuse in real-world applications.
Key Benefits
Governance-Focused Evaluation
We conduct governance-based benchmarks to ensure that your models are secure, safe, and compliant with enterprise requirements.
Red Teaming for Robustness
Experts attempt to jailbreak your model, generating valuable test data that helps refine datasets and improve model performance.
Security Testing
We perform standard security tests on your LLM to identify vulnerabilities and implement robust guardrails.
Certified for Enterprise Use
After rigorous testing, we certify models for governance, ensuring they are ready for enterprise applications.
Our Process: Rigorous Governance and Security Testing for AI Models
Zangoh’s evaluation process ensures that your models are thoroughly tested for governance, privacy, and security, preparing them for safe and responsible use in enterprise environments.
Governance Benchmarks: We run benchmarks focused on privacy, security, and governance using a combination of LLM as a judge, HITL, or a hybrid approach depending on the criticality of the use case. These tests ensure that the models follow enterprise guardrails and are secure for use.
Red Teaming: We bring in experts to attempt to jailbreak the model, intentionally trying to make it commit errors. This red teaming approach generates meaningful test data that helps us retrain and improve the model, making it more resilient.
Security Testing: We run standard LLM security tests to identify and fix any vulnerabilities, ensuring that the models adhere to security standards required for enterprise deployment.
Guardrail Implementation: After testing, we establish robust guardrails that prevent undesirable behaviors and safeguard against misuse.
Frequently Asked Questions
What is governance-based model evaluation?
Governance-based evaluation focuses on ensuring that AI models comply with safety, privacy, and security standards, making them suitable for enterprise use.
What is red teaming, and how does it benefit my model?
Red teaming involves experts attempting to jailbreak your model, forcing it to make mistakes. This helps identify vulnerabilities and generates valuable test data that improves model robustness.
How does Zangoh ensure the security of my AI models?
We run standard security tests on your LLM to identify vulnerabilities, implement robust guardrails, and ensure compliance with enterprise security requirements.
What are guardrails, and why are they important?
Guardrails are safety mechanisms built into the model to prevent undesirable behaviors, ensuring that the model operates within the bounds of privacy, security, and ethical guidelines.
What types of models benefit most from governance and security evaluation?
Models used in regulated industries like healthcare, finance, and legal benefit most from rigorous governance and security evaluations to ensure compliance and reliability.
What does it mean for a model to be certified for enterprise use?
After governance and security testing, we certify models that meet enterprise standards, ensuring they are safe, secure, and compliant with regulations.
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