Real-world attack data underrepresents zero-days, nation-state TTPs, and OT intrusions by orders of magnitude. Forgen uses NVIDIA Omniverse and Cosmos to generate synthetic attack chains at GPU scale, covering the blind spots that real data cannot fill — and provides a live simulation environment where autonomous response can be tested safely before it runs on production infrastructure.
AI detection models trained on observed attack data have structural blind spots. Zero-day exploitation chains, APT implants using legitimate tools, and OT-specific attacks are rare enough in real telemetry that models trained on it will fail silently when they encounter them. The only way to close those gaps is synthetic data generated at the level of realism the model needs to generalize.
Forgen generates it. At GPU scale. Continuously.
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Omniverse models the enterprise or OT environment: network topology, user population, services, industrial assets. Physics-accurate.
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Cosmos adversary model executes novel attack chains. NeMo ATT&CK library selects techniques. CUDA produces synthetic SIEM, EDR, and OT telemetry artifacts.
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RAPIDS generates labeled training datasets at million-example scale. Output is in Splunk, Sentinel, or EDR format — ready for model training pipelines.
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Run Triago and Huntra against simulated attacks in the live harness. Measure detection rate, MTTR, and false-positive rate. Tune before production deployment.
Weekly report showing which MITRE ATT&CK techniques your current training data covers and which it doesn't, by technique and sub-technique. Forgen schedules targeted simulation runs to fill the identified gaps.
Full ATT&CK matrix · 2,000+ techniques
Physics-accurate industrial plant simulation. Test autonomous OT containment actions in the twin before they touch live SCADA equipment.
Gymnasium-compatible RL environment. Triago investigators train against a Cosmos adversary agent. The loop produces better investigators without a single real breach.
SIEM-format logs, EDR telemetry, and network flow exports for integration testing, detection rule validation, and model evaluation. Your eval harness runs against simulated attacks, not a static golden dataset.
Run your full Triago and Huntra configuration against live simulated attacks. Get a real detection rate, false-positive rate, and MTTR number before you sign off on any autonomous response policy change.
Enterprise network and OT environment simulation. Physics-accurate 3D models for cyber-physical attack scenarios. Isaac Sim handles robotics and industrial control system environments specifically. There is no CPU path for real-time physics at the environment complexity required.
Generative adversary behavior model adapted for cyber attack chains. Produces novel, realistic attack sequences from high-level TTP specifications without scripting. Each generated chain is a valid training example with full telemetry artifacts.
Generating millions of labeled attack-chain examples per day requires sustained multi-GPU throughput. DGX is the right-sized platform. Forgen runs on reserved DGX capacity for steady-state simulation and on demand for burst coverage gap filling.
GPU-accelerated synthetic telemetry generation. Custom CUDA kernels produce realistic network traffic patterns, log format artifacts, and EDR event sequences. CPU generation produces output 100x slower at quality the models cannot generalize from.
RL investigators trained against Forgen's Cosmos adversary agent. Response policies validated in the defender harness before any production deployment.
Hunt hypothesis models pre-trained on synthetic hunt scenarios covering the rare TTPs that real telemetry cannot provide.
Edge distillate models trained on Forgen OT attack simulations in Isaac Sim — before any Jetson appliance is deployed to a real facility.
A Forgen assessment produces an ATT&CK coverage gap report and a detection rate measurement against synthetic attack scenarios — both based on your actual Triago configuration, not theoretical benchmarks.