Cloud Range launches AI agent validation range for SOC deployments

Cloud Range has introduced an AI Validation Range, a sandboxed cyber range environment designed to assess whether AI agents are ready for operational authority inside security operations centres. The Nashville-based cyber readiness vendor connects an organisation's own AI models through a secure API to a controlled, non-production environment that mirrors its real infrastructure, including IT, OT/ICS, cloud and hybrid configurations, before any live deployment takes place.

The move responds to what the International AI Safety Report 2026, chaired by Yoshua Bengio and authored by more than 100 AI experts, describes as an evaluation gap in general-purpose AI: performance on pre-deployment tests does not reliably predict real-world utility or risk. Cloud Range's framework attempts to close that gap by subjecting agents to attacks they have not previously encountered, rather than relying on synthetic benchmarks alone.

The Readiness Check framework

The company has published a six-question AI Agent Readiness Check to guide security leaders through the validation decision. The questions probe an agent's reliability when telemetry contradicts itself, its resistance to sensitive information extraction under adversarial conditions, the boundaries of its autonomous action, the cost of false positives at volume, its behaviour against unfamiliar attack patterns, and its performance relative to the human team it would augment or replace.

Debbie Gordon, founder and chief executive of Cloud Range, framed the approach by analogy: "Pilots and surgeons train in simulation because making judgment calls under pressure requires safe practice. The same standard applies to an AI agent making decisions in a security operations center."

The platform maps adversary behaviour to the MITRE ATT&CK framework at each stage of a multi-stage attack, so a security leader can report precisely which techniques an agent detected and which it missed. Human teams and AI agents run the same simulations scored on identical criteria, enabling a direct performance comparison across detection time, triage logic, response actions and failure modes.

Cloud Range reports that 95% of its customers say readiness improved after live-fire simulation programmes. Separately, the company cites reductions in mean time to detect of up to 66% and improvements in overall incident response time of up to 30% for human teams trained on its platform. It applies the same measurement methodology to AI agents.

Market context and competitive landscape

The question of how to validate AI agents before granting them authority over production security tooling is quickly becoming one of the more consequential unsolved problems in enterprise cybersecurity. Autonomous agents capable of triage, escalation and remediation are now being piloted by a growing number of security vendors, including those integrated with SIEM, SOAR and XDR platforms, yet formal validation frameworks remain scarce.

Cloud Range's positioning is notable because it targets the pre-deployment assurance gap rather than the agent's underlying model capability. Competitors in the cyber range market, including Cyberbit and SimSpace, have built simulation environments primarily for human analyst training. Extending that infrastructure to AI agent testing represents a logical adjacency, though Cloud Range appears to be among the earlier vendors to productise the concept with a named framework and API-based agent integration.

Regulatory pressure is likely to sharpen demand. The EU AI Act classifies AI systems used in critical infrastructure security contexts as high-risk, which means organisations operating in the EU will need documented conformity assessments. A repeatable, simulation-based validation record of the kind Cloud Range's platform could generate would be directly relevant to that compliance pathway, even if the company has not made that linkage explicit in its current release.

The near-term milestones to watch are named enterprise customer wins using the AI Validation Range specifically, third-party audit of the benchmark methodology, and whether the six-question Readiness Check framework achieves broader industry adoption or is absorbed into an emerging standards effort.