Cloud Range launches AI Validation Range to safely test and secure AI before deployment

Cloud Range has introduced its AI Validation Range, a secure, contained virtual cyber range that enables organizations to test, train, and validate AI models, applications, and autonomous agents without risking exposure of sensitive production data.

AI adoption is accelerating faster than most organizations can meaningfully validate its security. Security teams are asked to integrate and defend AI systems that they didn’t design and can’t safely evaluate in production.

With AI Validation Range, organizations can verify AI performance and reliability before deployment by testing and measuring how models respond to real adversarial inputs and uncertainty. For organizations integrating agentic AI into SOC, cyber defense, and offensive security workflows, AI Validation Range supports training agents on real systems and observing how they interact with live infrastructure and security controls.

This controlled approach to developing AI agents and assessing models is a critical step before those systems become part of daily security operations. It gives security and engineering teams concrete insight into AI reliability, decision logic, and failure modes, allowing them to establish guardrails, refine oversight, and reduce risk.

Using Cloud Range’s catalog of real-world attack simulations and suite of licensed security tools, organizations can safely test AI models for data leakage, logging behavior, and unintended outputs within realistic IT and OT/ICS environments. They can also train agents on offensive security objectives, such as vulnerability discovery to scan networks, find exposures, and validate real threats, as well as defensive measures, including identifying malicious behaviors, threat detection, and faster alerts.

Key capabilities of Cloud Range’s AI Validation Range:

• Adversarial AI testing: Simulate real-world cyber attacks to evaluate how AI models and agents detect, respond, and adapt under hostile conditions.
• Agentic SOC training: Condition AI agents on how to defend against real cyberattacks, workflows, and response actions in a safe, non-production environment.
• Operational readiness validation: Measure AI performance and implement security controls to determine production readiness and identify gaps before deployment.
• Governed, repeatable experiments: Support controlled testing and repeatable scenarios to enable consistent validation, tuning, and improvement over time.
• Secure, isolated range environment: Protect production systems and model data integrity while enabling high-fidelity simulations and training exercises.

“For years, Cloud Range has helped organizations know how to perform under real attack conditions. Applying that same simulation rigor to AI allows organizations to measure how AI agents and models perform side by side with human defenders, using the same scenarios, tools, and pressures,” said Cloud Range CEO Debbie Gordon. “That comparison is critical to understanding where AI truly strengthens security and where human judgment still matters most.”

By grounding AI evaluation in the same environments used for live-fire cyber training, Cloud Range helps organizations 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.