DRM for edge AI
Model encryption, hardware binding and per-device personalisation — preventing extraction, cloning or reuse on uncredentialed devices.
Helmet
Securing AI at the edge and in the data centre — from model build to deployed inference
Trusted by organisations that cannot afford to get it wrong
Helmet explained
Standard MLOps tooling was never built for environments where models can be stolen, inference queries leak intent, and training data carries re-identification risk.
Helmet is a toolkit for these problems: security controls, build tools and data-management capabilities for workloads where the model, the data and the queries are all sensitive — computer-vision inference on sensor data, natural-language interfaces over restricted datasets, decision-support automation in operational systems.
Models on the device
Built for UxVs, remote sensors, and field devices—where compute is limited, connectivity is unreliable, and hardware can be lost or captured.
Edge AI CI/CD
Automated build and rendering pipelines for edge accelerators (NPUs, GPUs, MCUs), with signing and integrity verification throughout.
Adversarial input controls
Runtime defences against sensor spoofing, adversarial perturbations and input manipulation.
Small-LLM deployment
Secure packaging and delivery of compact LLMs and inference models to constrained edge nodes.
Models in the cloud
For LLMs and larger inference systems in private, sovereign and multi-tenant cloud environments.
Confidential LLM deployment
Controls preventing leakage of queries, memory and responses.
Prompt-abuse prevention
Detection and mitigation of model manipulation via adversarial prompting and instruction injection.
Training-data management
Dataset versioning, provenance tracking and governance controls.
Statistical security
De-anonymisation prevention and membership-inference controls for sensitive datasets.
Start with the problem you are solving
Request a briefing. We will tell you whether the platform addresses your problem before we show you how.