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Customer proof

Evidence for the moment workforce AI becomes a security question.

Published customer context, measured outcomes and the details that make each result useful in a real evaluation.

Published stories

The context behind the headline.

Each story retains the customer’s stated scope, quote and result. Outcomes are not promises for other organisations.

Healthcare

Healthcare organisation, Australia

Customer story

Australian Healthcare Organisation

How an Australian healthcare organisation used platform controls and pre-submission guardrails to reduce Shadow AI prompts by 92.8% in 30 days.

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Key outcome

92.8%

fewer Shadow AI prompts in 30 days

Measured after Aona’s guardrails were introduced

Aona gave us visibility into which AI platforms were being accessed across the organisation and helped us proactively discourage use of unapproved tools while reinforcing Copilot as our approved option. It has been easy to deploy, lightweight for end users, and a valuable addition to our AI policy.
Senior systems and security administrator, Australian healthcare organisation
Shadow AI prompts after guardrails
446 → 32
unapproved AI platforms contained
7 → 2
raw prompt or file contents retained
0
Customer context and approach

The challenge

An Australian healthcare organisation had approved Microsoft Copilot, but staff were also using consumer AI tools to draft emails, summarise documents and search. Aona surfaced 446 Shadow AI prompts submitted outside the approved tool, alongside activity on more than seven unapproved AI platforms.

The approach

The organisation deployed Aona's browser extension and governance portal to detect AI-site visits and intercept prompts and file uploads before submission. It blocked unwanted platforms and subscriptions, applied real-time guardrails to authorised platforms and guided staff back to Microsoft Copilot. Prompt and file contents were processed transiently in Australia and not retained; only high-level analytics were stored in Australia.

  • Shadow AI Detection
  • Browser Extension
  • Real-time Guardrails
  • Healthcare

Workforce Technology

Australian workforce technology

MyGig

Customer story

MyGig

How MyGig moved from AI experimentation to secure, operational workflows supporting 70,000+ workers across 126 modern awards.

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Key outcome

5

core functions using AI

Live workflows across MyGig’s operating model

We interpret 126 modern awards and pay 70,000+ workers. That maths only works if AI is doing real operational load, not sitting in a browser tab. Today agents handle client intake, prospecting and internal workflows within guardrails I actually trust, which is the only reason they were allowed near production.
Enguerrand Vidor, CEO, MyGig
hourly workers supported
70K+
modern awards managed
126
live agents in production
Multiple
Customer context and approach

The challenge

MyGig was experimenting with AI, but isolated tools and inconsistent practices were not producing a dependable operating model. The lean team needed clear guardrails, shared patterns and a practical route from experiments to live workflows without adding unnecessary risk.

The approach

Aona helped MyGig establish security expectations and approved patterns, train the team across five functions, and connect agents to real internal systems and workflows spanning client interaction, sales, HR, operations and support.

  • AI Guardrails
  • Team Enablement
  • Operational AI
  • Workforce Technology
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Aona AI Customer Case Studies | Workforce AI Security