Tumeryk

Tumeryk integrates with Wiz to deliver unified visibility and real-time risk assessment for AI assets across cloud environments. In this integration, Wiz discovers misconfigured AI resources, while Tumeryk applies its AI Trust Score™ to evaluate risks like prompt injection, data leakage, and policy violations. Together, they enable secure, compliant, and scalable AI operations through automated policy enforcement, empowering organizations to confidently govern generative AI use across their cloud footprint without requiring agents or complex deployments.

Benefits of the integration

Tumeryk’s integration with Wiz provides:

• Quantify AI model risk instantly
Gain a real-time, objective Trust Score for every AI asset in your cloud—so you know which models are safe to use and which need attention.

• Eliminate AI blind spots
Get a unified view of GenAI risk across your cloud environment, making it easier to meet compliance requirements and catch gaps before they become incidents.

• Enforce AI security policies automatically
Ensure consistent, policy-based guardrails are applied to every model—reducing manual effort and preventing risky behavior at scale.

The better together story

Wiz already helps organizations discover and secure AI workloads across their cloud environments—from unmanaged models to misconfigured LLM endpoints. This integration with Tumeryk takes that a step further by adding model-level risk assessment, governance, and policy enforcement. Tumeryk applies its AI Trust Score™ across the AI assets discovered by Wiz, surfacing risks like prompt injection, data leakage, and behavioral safety issues. Customers can then enforce policy-driven guardrails and measure improvements over time—all within their existing Wiz workflows.

By combining Wiz’s cloud-native visibility with Tumeryk’s AI-specific insights, customers gain a unified view of infrastructure risk and model trustworthiness. This joint solution supports compliance with frameworks like ISO 42001, the EU AI Act, and NIST RMF 600.1, while enabling secure, scalable GenAI adoption. Whether models are in development or production, teams can continuously discover, assess, and govern AI assets—treating AI security with the same rigor and automation as any other cloud risk.

Together, Wiz and Tumeryk empower enterprises to embrace GenAI confidently—backed by real-time visibility, automated policy enforcement, and continuous validation across their cloud and AI environments.

An enterprise using Wiz to monitor its cloud environment identifies a newly deployed LLM hosted on Azure. Wiz detects the asset and flags misconfigurations such as overly permissive access settings or missing encryption. Through its integration with Tumeryk, this asset is passed to Tumeryk’s AI security engine for evaluation. Tumeryk applies its AI Trust Score™ framework to assess the model’s risk across vectors such as prompt injection, sensitive data leakage, and behavioral safety. The resulting Trust Score reveals that the model falls below the organization's acceptable trust threshold. Tumeryk then generates policy recommendations to mitigate the identified risks—such as enforcing input validation or restricting access—helping the enterprise secure its AI deployment and maintain compliance. 

Tumeryk’s AI Trust Score™ reveals when a model falls below an organization’s trust threshold—flagging issues like prompt injection risks or mishandling of sensitive data. Unlike manual audits, the Trust Score provides a clear, numerical indicator of an AI model’s security and governance posture across key dimensions like toxicity, data exposure, and behavioral safety.

This makes it easy for security teams to evaluate risk, track improvements, and guide deployment or compliance decisions. When issues are found, Tumeryk’s policy engine automatically applies guardrails—such as input sanitization or output redaction—aligned with frameworks like ISO 42001, the EU AI Act, and NIST RMF 600.1. The Trust Score is then regenerated to validate the fix, closing the loop from detection to remediation.

Throughout, teams use a unified dashboard to monitor both cloud and AI-specific risks—ensuring AI assets are continuously secured and governed as threats evolve.

Market challenge

As enterprises increasingly integrate LLMs into production systems, the corresponding attack surface and ethical considerations continue to grow. There is an opportunity to expand support for analyzing behavioral trust scores across LLMs, Guardrails as well as Private endpoints. Without standardized metadata for LLM trustworthiness, it becomes difficult to compare models, flag vulnerable ones, or enforce governance across multi-model, multi-provider environments.