Cloud Range Introduces AI Validation Range for Secure AI Deployment
A New Cybersecurity Solution for AI Systems
A new cybersecurity solution has been launched to help organizations safely test and secure artificial intelligence (AI) systems before deployment.
The AI Validation Range
The AI Validation Range, developed by Cloud Range, is a secure, virtual environment that enables the testing, training, and validation of AI models, applications, and autonomous agents without exposing sensitive production data.
The rapid adoption of AI has created new challenges for security teams, who are often asked to integrate and defend AI systems that they did not design and cannot safely evaluate in production.
Addressing the Issue
The AI Validation Range addresses this issue by providing a controlled environment where organizations can verify AI performance and reliability before deployment.
Using real-world attack simulations and licensed security tools, organizations can test AI models for data leakage, logging behavior, and unintended outputs within realistic IT and operational technology (OT)/industrial control systems (ICS) environments.
The solution also enables the training of AI agents on offensive security objectives, such as vulnerability discovery and threat detection.
Key Features
- Adversarial AI testing
- Agentic security operations center (SOC) training
- Operational readiness validation
- Secure, isolated range environment
These capabilities enable organizations to measure AI performance, implement security controls, and identify gaps before deployment.
According to Cloud Range CEO Debbie Gordon, “By applying the same simulation rigor to AI that we have to live-fire cyber training, organizations can measure how AI agents and models perform side by side with human defenders, using the same scenarios, tools, and pressures.”
This comparison is critical to understanding where AI strengthens security and where human judgment is still required.
By grounding AI evaluation in real-world environments, organizations can move beyond theoretical risk assessments to evidence-based decision-making.
Security leaders gain clarity on how AI systems perform within existing processes, where safeguards are required, and how responsibility should be shared between automated systems and human teams.
This enables organizations to operationalize AI with confidence, aligning innovation, security, and accountability before AI becomes embedded in mission-critical workflows.
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