AugurySimSpace Builds a More Lifelike Cyber Range to Train Security Teams and Test AI Defenses
This article was written by the Augury Times
Real attacks, without the damage: SimSpace’s push to close the training gap
SimSpace has rolled out a more realistic cyber range meant to give security teams and AI models a closer look at what real threats feel like. The company says the upgrade brings richer network behavior, simulated users and industrial equipment, and tools to test autonomous security agents — all in an environment that won’t break a production system.
That matters because many security drills still run on toy networks or old threat libraries. Those exercises can teach teams to spot textbook attacks, but they do little for subtle, noisy threats or for machine-learning systems that need diverse, labeled data to learn from. SimSpace’s pitch is simple: make practice look as much like the real world as possible, so defenders and the tools they use perform the same way under pressure.
What the upgraded range actually does: a closer look under the hood
The heart of SimSpace’s work is simulation fidelity — the degree to which the range behaves like a real enterprise environment. That starts with layered network models that mimic cloud services, on-prem servers, laptops, industrial control systems and the normal chatter users create. Those elements run together so attacks don’t look like isolated anomalies but like pieces of a functioning business.
The platform generates synthetic users and background traffic, which helps hide or reveal attack activity the way it would in a real office or factory. That makes tests harder and more useful: defenders must separate malicious activity from normal noise rather than chasing obvious indicators that only exist in contrived drills.
Another major piece is a deep library of named and custom threats. SimSpace lets operators replay known malware, ransomware and nation-state tactics, and it also stitches those behaviors into longer campaigns so teams can practice detection and response over days or weeks. The library is modular, so organizations can tune scenarios to their industry and likely adversaries.
For organizations building AI tools, the range offers labeled telemetry and replay capabilities. That means security vendors and in-house teams can feed consistent, repeatable streams of data into machine-learning models, measure how the models behave, and then try new attacks to see whether the tools regress. SimSpace also supports testing “agentic” or autonomous defenses — software that takes actions on its own — by giving those agents a safe place to act and fail without risking live systems.
Behind the scenes, the system captures detailed logs and scores performance. Customers can compare detection rates, response times and policy effects across runs. The platform’s emphasis on repeatability and labeling tackles a common AI problem: messy or inconsistent training data.
Who stands to gain from a more realistic test bed
Security operations centers (SOCs) get the most immediate benefit. The upgraded range helps SOC teams practice hunting and escalations against complex, blended attacks. Instead of learning in a calm lab, they learn with real-world distraction and uncertainty.
Red and blue teams — the offensive and defensive testers inside many organizations — can run longer, more meaningful exercises. Red teams can chain behaviors that would normally span days, while blue teams see how those campaigns unfold and refine playbooks for containment and recovery.
AI researchers and vendors benefit from the labeled data and repeatable scenarios. For tools that rely on supervised learning, consistent training feeds and validation runs make model performance easier to measure and improve. Large enterprises that operate industrial control systems or critical infrastructure can test how cyber incidents interact with physical processes without risking safety.
Where SimSpace fits in the market and how it compares to the usual options
Cyber ranges are an established segment of the security market, but quality varies. Many products focus on classroom-style exercises, or on penetration-testing sandboxes that don’t simulate regular business activity. SimSpace is positioning itself toward the middle: higher fidelity than training-only platforms, but easier to deploy than full-scale, bespoke lab builds.
The firm’s selling points are repeatability, telemetry for AI work, and the ability to run long, blended scenarios. That makes it attractive to organizations that need more than a single red-team exercise but can’t afford to build or constantly refresh a costly internal emulation environment.
Strengths and caveats: what this approach does well — and where it can mislead
A realistic range tightens the gap between practice and reality, which is a clear strength. It reduces surprise and helps teams learn processes that actually apply during real incidents. The labeled data and replay features also solve a practical problem for machine learning: obtaining consistent, high-quality security data.
But higher fidelity brings higher cost and complexity. Running and maintaining a lifelike simulation requires engineering effort, and smaller teams may find the investment hard to justify. There’s also a risk of false confidence: a well-crafted simulation can still miss the cleverness or resourcefulness of a human adversary, and overly scripted scenarios can teach predictable responses rather than flexible thinking. Finally, using realistic telemetry raises privacy and compliance questions that customers must manage carefully.
When you might hear more about this — rollout and next steps
SimSpace says the upgraded range is available now and is rolling out to existing customers, with trials for new buyers. The company plans partnerships with integrators and research groups to expand scenario libraries and to validate autonomous defenses in different industries.
For organizations considering a trial, the sensible checkpoints are clear: check how closely the platform mimics your environment, whether it can replay and label data for model testing, and how it scores and reports results. Those features will determine whether the range is a useful tool or an expensive exercise in theorycraft.
Photo: cottonbro studio / Pexels
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