— Demo Request

See Inside Your AI Security Posture.

Most organizations don't know what their models have learned, whether their detections would catch an AI-specific attack, or what their deployed agents are doing right now.

In 30 minutes, we'll show you exactly where your AI security gaps are and what covering them looks like in practice.

— AI Interpretability

AI Interpretability as a Security Primitive

Most AI security tools watch what models do. Starseer examines what they've learned. By applying interpretability techniques to the security problem — tracing internal circuits, probing learned representations, and inspecting the mechanisms behind model decisions — organizations gain assurance that goes beyond acceptable outputs to verifiable model integrity.

This matters because the most damaging threats produce no anomalous output signal. Backdoors, misaligned representations, and hidden capabilities all pass behavioral evaluation and surface only when exploited. Output monitoring cannot find them. Interpretability can.

The result is AI security grounded in evidence rather than inference — and assurance defensible enough to demonstrate to regulators, auditors, and boards.

— Full Lifecycle Coverage

Securing the AI Lifecycle — Before, During, and After Deployment

AI security failures don't start at runtime. They start with what a model learned, compound through undetected attack patterns, and surface as incidents that were preventable at every stage.

Starseer covers the full lifecycle. Model Validation surfaces backdoors and misaligned representations before deployment. AI-DE engineers and validates the detection logic that catches threats at runtime. AI-EDR monitors live models and agents continuously, tracing decision paths and containing threats before they cause damage.

When an incident occurs, the answer to "why did this happen" is already there — in the validation record, the detection history, and the forensic telemetry. Root cause becomes a lookup, not an investigation.