Secure AI Deployment& Testing
Run AI where your data is safe, connect it to your systems properly, and test it the way an attacker would before it goes live.
Discuss Your AI DeploymentPrivate & Hybrid Options
Models on your own infrastructure, in Australian-region cloud services, or a mix of both, chosen for your data.
Production-Grade Controls
Single sign-on, centrally managed credentials and an AI gateway that logs and limits every call.
Tested Like an Attacker Would
Security testing against the OWASP Top 10 for LLM Applications, including prompt injection and agent misuse.
Built on Recognised Guidance
Most AI Prototypes Aren't Ready for Real Data
Building an AI prototype now takes days. Someone connects a model to a spreadsheet or a shared folder, it works, and people start relying on it. The hard questions come later. Who can sign in? Whose credentials does it run on? What can it reach? What happens when someone feeds it a malicious document?
We take AI that works in a demo and make it safe to run for real. That means deciding where the model runs, putting the app under proper source control and identity, and testing it against the attacks that only AI systems face.
Scenario:
An operations team has built several useful AI prototypes, each on someone's personal account with its own API key. The best of them move into one source-controlled application. Staff sign in through Microsoft Entra ID, and every call goes through an AI gateway using centrally managed credentials, so the business can see usage, cap cost and rotate keys in one place. Testing before launch finds that an uploaded document can carry hidden instructions to the model, and the team fixes it before anyone outside the project sees it.
Choose Where Your AI Runs
No single option suits everyone. The right choice depends on how sensitive the data is, which models you need and how much you want to operate yourself.
Private / On-Premise
Open-weight models running on your own hardware or private cloud. Data stays in your environment, and you take on running and patching it.
Managed Cloud (AU Region)
Enterprise AI services hosted in Australian regions. You get the most capable models with less to operate, provided the residency and retention terms suit you.
Hybrid
Sensitive workloads run locally and general tasks use the cloud, with both behind one gateway and one set of controls.
| Factor | Private / On-Premise | Managed Cloud (AU Region) |
|---|---|---|
| Data location | Your environment | Provider data centres in the region you choose |
| Data residency | Fully in your control | Depends on provider terms and region settings |
| Model capability | Limited to models you can host | Access to the largest commercial models |
| Operating effort | You run, patch and scale it | The provider runs the infrastructure |
| Cost profile | Hardware up front, then predictable | Usage based, so it needs limits and monitoring |
| Security responsibility | Entirely yours | Shared with the provider |
Six Steps From Prototype to Production
Prototype Review
What the prototype does, who uses it and what data it touches.
Source-Controlled App
The prototype becomes a proper application in your repository, with code review and versioning.
Secure Backend
Sign-in through your identity provider, centrally managed API credentials, and an AI gateway for audit, cost control and key rotation.
Security Testing
Testing against the OWASP Top 10 for LLM Applications before real users or real data arrive.
Central Deployment
One pipeline and one security model for every AI app, instead of one per team.
Monitoring & Support
Usage, cost and error monitoring, plus a plan for model updates and incidents.
Testing AI the Way Attackers Do
A standard penetration test doesn't cover how AI fails. Language models follow instructions hidden in documents, emails and web pages, and agents with broad permissions can be talked into actions nobody intended. We test for these problems using the OWASP Top 10 for LLM Applications (2025) as the baseline.
Prompt Injection
Direct attempts to override instructions, and indirect ones hidden in files, emails or web pages the model reads.
Sensitive Information Disclosure
Whether the model can be coaxed into revealing personal data, credentials or other users' content.
Improper Output Handling
Model output passed unchecked into browsers, databases or system commands.
Excessive Agency
Agents with more tools, permissions or autonomy than the task needs, and missing human approval points.
System Prompt Leakage
Secrets or security logic placed in prompts, where an attacker can extract them.
Unbounded Consumption
Token floods and runaway usage that drive up cost or take the service down.
For AI agents, we also check the controls in "Careful adoption of agentic AI services", the guidance ASD's ACSC published with its international partners. That includes least privilege, just-in-time credentials for high-impact actions, and human approval before an agent does anything consequential.
Every engagement ends with severity-rated findings, steps to reproduce each one, recommended fixes, and a retest once you've made changes.
Deliverables & Business Impact
Key Deliverables
Business Benefits
Ready to Put AI Into Production Safely?
Start with a free initial consultation about the AI you're running or planning. We'll tell you what it would take to deploy and test it properly.