ResilienceOS — Autonomous Digital Twin for Critical Infrastructure

The Problem

Critical infrastructure does not operate in isolation. Hospitals depend on pharmaceutical suppliers, warehouses, transport networks, electricity, cloud services, and telecommunications. A disruption in one dependency can cascade into failures across an entire operational network.

Today, organizations often monitor these systems separately. Individual monitoring tools may detect an outage or a delayed shipment, but they do not necessarily explain how the incident affects other dependent systems, which facilities are most exposed, or which recovery strategy minimizes risk.

For a hospital network, a power disruption could threaten temperature-sensitive medicines, while a simultaneous cloud outage could affect access to operational systems. If the backup supplier shares the same transport or telecommunications dependencies, the apparent alternative may fail as well.

The challenge is not simply detecting a disruption. It is understanding its consequences and selecting a safe, practical recovery action before the disruption spreads.

Our Solution

ResilienceOS is an AI-native resilience platform designed to connect infrastructure dependencies, identify cascading risks, compare recovery strategies, and coordinate verified responses.

Its initial focus is pharmaceutical supply-chain resilience for hospital networks. The platform models hospitals, suppliers, warehouses, transport routes, inventory, and critical digital dependencies as a connected system.

Rather than presenting isolated alerts, ResilienceOS aims to answer five operational questions:

What has been disrupted?
Which downstream facilities, medicines, and services are at risk?
How could the disruption propagate through the dependency network?
Which available intervention offers the best balance of safety, cost, and recovery time?
Has the selected recovery action actually worked?

How It Works

1. Dependency digital twin

Infrastructure assets and operational relationships are represented as a graph. This makes dependencies and potential single points of failure visible rather than treating every asset as an isolated component.

2. Event and threat analysis

The platform processes operational events such as severe weather alerts, supplier delays, road closures, inventory shortages, temperature excursions, and cyber incidents. The initial prototype can use controlled scenario data to demonstrate these events without requiring access to sensitive enterprise systems.

3. Cascading-failure analysis

Graph-based analysis traces the possible consequences of a disruption through connected assets. The objective is to identify affected hospitals, vulnerable inventory, critical dependencies, and potential bottlenecks.

4. Recovery simulation

The system compares alternative interventions, such as transferring stock between hospitals, selecting another supplier, rerouting deliveries, or arranging emergency procurement. Each option can be evaluated using explicit assumptions about cost, delivery time, available inventory, and operational impact.

5. Agent-assisted response

Specialized AI agents can explain the incident, summarize affected dependencies, propose recovery strategies, and generate actionable response plans. High-impact decisions remain subject to human approval. The design separates AI recommendations from deterministic checks and authorized execution.

6. Verification and learning

A recovery action should not be considered successful merely because an agent reports completion. ResilienceOS is designed to verify outcomes using available evidence, update the dependency model, recalculate risk, and preserve an incident record for future analysis.

NVIDIA Nemotron and Nebius

NVIDIA Nemotron is intended to provide the reasoning layer for structured incident analysis, scenario interpretation, and response planning. Nebius infrastructure can provide hosted model inference and compute for workloads that exceed local development capacity.

The architecture separates language-model reasoning from deterministic graph algorithms, optimization, and verification. This is important because a plausible AI-generated explanation is not sufficient evidence that a recovery plan is safe or feasible.

The final implementation and evaluation will document the specific Nemotron model, the Nebius services actually used, the deployment configuration, and the observed results.

Safety and Governance

ResilienceOS is designed around controlled autonomy rather than unrestricted execution.

Human approval is required for high-impact operational decisions.
Recovery recommendations should include their assumptions and supporting evidence.
Permissions must restrict agents to authorized tools and actions.
Verification should be independent of an agent's claim that an action succeeded.
Unavailable, stale, or incomplete data should be identified rather than silently treated as reliable.
Incident records should make decisions and outcomes auditable.

The prototype is a decision-support system, not a replacement for clinical judgment, infrastructure operators, or established emergency procedures.

What Makes ResilienceOS Different?

A conventional monitoring dashboard shows events. A chatbot explains information. ResilienceOS is designed to connect events to dependencies, estimate downstream consequences, compare interventions, and verify recovery outcomes.

Its long-term differentiation would come from accurate dependency models, integrations with operational systems, historical incident evidence, validated simulation methods, and reliable recovery workflows—not merely from using a particular AI model.

Development Scope

The initial prototype concentrates on a hospital pharmaceutical supply chain rather than attempting to model every category of global infrastructure.

The competition demonstration should make one complete scenario understandable: introduce a disruption, visualize the affected dependency chain, compare feasible recovery options, generate a response plan, and show how the result is verified.

Any simulated inputs, estimated outcomes, and unimplemented integrations will be identified explicitly. Production deployment would require validated data sources, customer-specific integrations, security testing, and operational approval procedures.

Future Direction

ResilienceOS could expand into data centers, manufacturing, ports, telecommunications, and other critical infrastructure. Future work includes richer geospatial data, live telemetry integrations, uncertainty-aware forecasting, validated optimization, and secure coordination across enterprise systems.

The long-term goal is to help organizations move from reacting to isolated failures toward understanding, preparing for, and recovering from interconnected disruptions.