Questions About CyberActive AI.

CyberActive AI is designed as the autonomous security and resilience layer for AI workloads and GPU infrastructure. It connects threat detection with infrastructure dependency intelligence, impact-aware decision-making, governed response, and verified recovery.

No. It is designed to complement them. Existing tools provide detection, visibility, and automation; CyberActive AI adds infrastructure context, propagation and impact analysis, and governed decisions on top of their signals.

Advanced AI models are making threat discovery and analysis faster. CyberActive AI is not a foundation-model provider and does not compete with them. Its architecture is designed to take intelligence from many sources, including such models where integrated and authorized, and carry each finding through infrastructure context, impact analysis, governed response, and recovery.

Only within boundaries the customer approves. In Observe mode it recommends only. In Approve mode a named person authorizes each action. In Autonomous mode it executes only action classes the customer has pre-approved, and a human can override any step. Disruptive actions always require human approval.

AI workloads, GPU infrastructure, AI data centers and GPU cloud, and enterprise and private AI. The same architecture also applies to mission-critical environments where disruption carries serious consequence.

The core platform architecture and infrastructure dependency modeling are developed, and the patented technology foundation is in place. AI model training, decision intelligence, and governance modes are in validation; GPU infrastructure integrations and autonomous recovery orchestration are on the roadmap. See the capability status on the Technology page.

Three approved patents cover Adaptive Zero-Day Detection, Risk Propagation Intelligence, and Business Impact Decision Intelligence. Official details are available to qualified parties on request.

No. Every interactive visual, including the telemetry radar and the nine-stage simulation, is an illustrative simulation in a hypothetical environment, not live production telemetry or measured results.

Through a structured process: discovery, requirements review, a demonstration tailored to your environment, readiness assessment, and a scoped pilot with agreed success criteria. Request a private demo to start.

We are open to discussing integration, OEM and licensing, GPU cloud and data center collaborations, joint technical validation, and channel models. These are opportunities, not existing agreements.