Cloud Range lets SOCs benchmark AI agents against human defenders
Cloud Range launches AI Validation Range and PROVE-based AI Readiness Framework to red team AI agents and benchmark them against human SOC defenders.
Cloud Range announced its AI Validation Range and AI Readiness Framework, which let organizations red team and train AI models and agents in realistic SOC environments with licensed tools, complex traffic generation, and automated adversary emulation. The framework follows a five-step PROVE process (Prepare & Train, Risk-Assess, Operationally Test, Validate & Benchmark, Evaluate & Evolve) to make evidence-based decisions about AI deployment, autonomy, and oversight. CEO Debbie Gordon framed the launch around recent incidents where autonomous agents exceeded intended boundaries and accessed external systems.
- AI Validation Range provides contained environments to red team AI agents safely
- Framework uses five-step PROVE process to assess AI readiness and autonomy
- Benchmarks AI agent performance against human SOC defenders
- Motivated by rogue AI agent incidents exceeding boundaries and accessing external systems
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Cloud Range has announced the official launch of its AI Validation Range and Cloud Range AI Readiness Framework. They give organizations a structured way to test AI models and agents in realistic environments, validate their readiness for operational responsibility and safety, and determine which roles and tasks are best handled by AI versus human experts.
Recent incidents involving rogue AI agents have exposed gaps in traditional testing, with autonomous agents moving beyond intended boundaries and accessing external systems. As AI gains greater access and autonomy, organizations need to prove not just that it works, but that it behaves reliably under adversarial and unexpected conditions.
Cloud Range’s AI Validation Range is a safe and contained testing environment that enables organizations to securely and effectively red team and train AI models and agents in realistic SOC environments, uncover failure modes and access risks, and benchmark performance alongside human defenders.
Teams can also test which security roles and responsibilities are best suited for AI, which require human judgment and oversight, and where a combination of the two delivers the strongest results. This continuous validation gives organizations a way to reassess readiness as models, use cases, and threats evolve.
“AI is moving from recommending what humans should do to actually doing it, and that fundamentally changes the risk equation,” said Debbie Gordon, CEO of Cloud Range.
“Recent events have made clear that a successful test is not the same thing as proven readiness. An AI agent can accomplish its assigned objective and still take a path no one expected or intended. Organizations need to understand not just whether AI works, but how it behaves under pressure, where it fails, when it needs human intervention, and what level of autonomy the evidence actually supports. Organizations cannot afford to find that out in a production environment.”
Built on the Cloud Range cyber range platform, AI Validation Range recreates realistic enterprise environments including licensed tools and complex traffic generation. Cloud Range’s extensive library of automated adversary attack emulations enables organizations to safely test AI without risking production systems. It provides a controlled environment for evaluating AI across real-world SOC workflows and under adversarial and unpredictable conditions.
Cloud Range has been working with organizations on its AI Validation Range to examine AI performance under realistic operational conditions. Those experiences helped inform the development of the Cloud Range AI Readiness Framework, built on the 5-step PROVE process. The framework is designed to help security leaders move from assumptions about AI performance to evidence-based decisions about deployment, autonomy, and oversight.
The Cloud Range AI Readiness Framework:
- Prepare & Train: Define the AI’s intended role, boundaries, and expectations, and prepare it for the workflows and decisions it will encounter.
- Risk-Assess: Understand the AI’s access, authority, autonomy, and potential impact.
- Operationally Test: Challenge AI beyond expected behavior using realistic, unexpected, and adversarial conditions.
- Validate & Benchmark: Measure accuracy, performance, consistency, limitations, and risk to determine whether evidence supports readiness.
- Evaluate & Evolve: Continuously revalidate performance as models, threats, workflows, permissions, and operating environments change.
“Organizations shouldn’t discover what an AI agent is capable of accessing, changing, or breaking for the first time in production,” Gordon said. “The goal isn’t to prove that AI works. It’s to understand how it works, where it performs well, where it doesn’t, and what success actually looks like before you give it greater responsibility. AI readiness has to be continuously proven.”