CyberAI develops modular technologies that can be evaluated independently or integrated into a broader cloud, edge, IoT or trustworthy-AI architecture.
The visual overview above summarises the portfolio; the sections below retain the system boundaries, methods and evidence expectations for each capability.
We design analytical pipelines for anomaly detection, threat prioritisation and response support, with explicit confidence, evidence and policy context.
Combine distributed telemetry without hiding provenance.
Connect scores to observable evidence and policy.
Recommend bounded actions with reviewable conditions.
Architecture and monitoring components for APIs, containers, Kubernetes, cloud workloads, edge gateways and service dependencies.
Boundary-aware controls for services and cloud-native components.
Lightweight telemetry and risk logic for constrained infrastructure.
Map service relationships and security-relevant propagation paths.
Lightweight device and network signals are converted into risk indicators suitable for IoT, operational technology and cyber-physical environments.
Model normal operational patterns and bounded deviations.
Surface suspicious changes in device or actuator behaviour.
Translate risk evidence into proportionate response options.
Threat modelling and technical controls for poisoning, manipulation, model leakage, unsafe deployment and weak runtime oversight.
Assess training, update and deployment integrity risks.
Identify leakage paths and data-protection controls.
Keep decisions bounded, traceable and reviewable.
We convert defined research methods into controlled software modules, experiments and technical demonstrators that can be reviewed, tested and integrated.
Documented code, configuration and experiment logic aligned with the stated method.
Clear inputs, outputs, assumptions and integration boundaries for technical review.
A bounded prototype supported by test scenarios, metrics and known limitations.
CyberAI supports collaborative proposals with technically bounded work packages, prototypes, validation activities and evidence that can be integrated into a wider consortium plan.
Defined objectives, tasks, interfaces, risks, milestones and measurable outputs.
Alignment with partner responsibilities, shared architecture and proposal dependencies.
Validation plans, technical indicators and credible exploitation or deployment pathways.
System boundaries, trust assumptions, threat model and integration design.
Lightweight software module with controlled interfaces and reproducible configuration.
Test protocol, metrics, limitations and deployment recommendations.
CyberAI can contribute one focused component or a complete evidence-driven work package.
