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Ridge Security describes RidgeOne as an on-premises appliance for autonomous penetration testing: its RidgeGen platform and an open-weight AI model run locally on NVIDIA DGX Spark hardware. The company says test data does not leave the appliance during a test. That is a vendor claim, not an independently verified security assessment.
What RidgeOne is
RidgeOne combines three components, according to Ridge Security’s product page: RidgeGen, its autonomous penetration-testing platform; a locally run open-weight model; and NVIDIA DGX Spark hardware. The models named by Ridge Security are NVIDIA Nemotron 3 Nano, NVIDIA Nemotron 3 Super, and GPT-OSS. The company presents the setup as an appliance for on-premises penetration testing and red teaming.
This differs from RidgeGen’s broader deployment options. Ridge Security says RidgeGen can be deployed on-premises with supported frontier-model providers or self-hosted open-source models. RidgeOne is specifically presented as a package with local open-weight model execution. The published pages do not give a complete configuration matrix by model, license, or region, so confirm the current configuration with the vendor.
What “data stays inside” means
Ridge Security says RidgeOne’s model runs locally, so a test does not need a connection to Ridge Security or an AI provider. The company’s FAQ answers the question “Does any data leave the appliance during a test?” with “No.” This describes the vendor’s stated design; the product pages do not independently establish that every deployment or operating condition meets that boundary.
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Credentials and evidence
Ridge Security says credentials are kept in a protected SafeBox, used in memory for a test, not persisted in RidgeGen, and not passed to third parties, including model vendors. It also says disk writes are encrypted and retained evidence is limited to high-level structure or metadata, rather than actual credential values or database contents. These are vendor descriptions of its controls, not independently audited findings.
Operator controls and safeguards
The vendor says operators choose which systems are in scope and set the testing posture. It says secrets remain outside the AI’s reach and that actions are recorded in a local audit trail. RidgeGen’s product page describes safety policies as operating outside the model’s discretion, with three modes:
- Safe: described as production-oriented; blocks write, modify, and delete actions against targets.
- Supervised: allows limited temporary actions to demonstrate proof while blocking destructive changes.
- Authorized-Aggressive: permits more impactful actions in non-production settings when authorized.
Those mode descriptions explain the controls Ridge Security says it provides; they do not substitute for validating configuration, permissions, and safeguards in the environment where testing will run.
What RidgeOne can test
Ridge Security lists IT infrastructure, networks, web applications, APIs, Active Directory, hosts, and IoT/OT environments as RidgeOne target categories. RidgeGen’s page also lists AI systems. These are vendor-listed categories, not a guarantee that every product, configuration, or environment is supported.
Ridge Security says findings include evidence, attack paths, and remediation guidance mapped to OWASP Top 10 and MITRE ATT&CK. The company’s launch demonstration was reported by Yahoo Finance: Ridge Security said RidgeOne completed an autonomous test of OWASP Juice Shop without operator involvement. This is a report of a vendor-provided demonstration, not an independent benchmark or assessment. The available product materials do not establish independently measured performance figures.
Hardware, license, and cost
Ridge Security says a RidgeOne setup requires NVIDIA DGX Spark hardware and a separate RidgeGen license. DGX Spark is a component of the setup, not by itself a RidgeOne license or turnkey penetration-testing product. The company says its sales representative can confirm licensing and credits, help size a deployment, and scope a proof of value.
Ridge Security says there are no AI token costs, with the hardware and RidgeGen license making up the investment. It does not publish a price or total cost of ownership on the reviewed product page. Buyers should therefore confirm license terms, credits, hardware availability, deployment needs, and ongoing costs directly with the vendor before budgeting.
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What to verify before deployment
- Confirm the exact hardware, local model, RidgeGen license, and any credits included in the proposed configuration.
- Ask which systems and technology versions are supported for your intended scope, especially for specialized IoT/OT or AI targets.
- Review the data-flow boundary and retention behavior for your deployment, including audit logs, evidence, encrypted disk writes, and any operational or support connections.
- Agree on scope, authorization, testing mode, and rules of engagement. Validate that the selected mode fits the production or non-production environment.
- Request a proof of value and define what success means for your team; the published pages provide no independent performance benchmark or comparative measurement.
How to assess the data-residency claim
RidgeOne’s main appeal is its stated local execution model: the test can run without sending data to Ridge Security or an AI provider. The practical security question is whether the product’s actual configuration, data handling, and operational connections match that claim in your environment. Treat the vendor’s descriptions as a starting point for technical and procurement review, rather than as independent proof.
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