Which wards did this incident affect? Nobody could say quickly. A hospital network, before Ollandi: - IT measured health in technical terms: CPU, link utilisation, disk latency - Clinical leaders could not quickly tell which wards an incident affected - Shared workstations and shift changes made identity activity hard to read After Ollandi: - Every component mapped to the clinical services that depend on it - Repairs prioritised by their effect on patients - Access outside a normal shift, location or role raised for review How it runs: operating mode: Observe & Notify; reads: Infrastructure telemetry, identity events and network state, never clinical records; stays with: Care leadership, for any action that could affect clinical work. In production, our previous generation (PulseADT on ADT-4 Pro) recorded 97% accuracy separating legitimate automation from attacker behaviour. Results depend on the telemetry available. Read the full story at glemad.com, and request an executive briefing at ollandi.com. #Ollandi #CyberSecurity #AI #Healthcare #HealthIT
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