Risk review
Map the AI tools in use, information flows, external services and potential misuse.
AI Security
AI security is the practice of protecting AI applications, the data they use and the systems they can reach. Umbrella Tech helps Australian organisations identify AI security risks, such as data leakage, prompt injection and over-permissioned agents, and implements controls across identity, data, integrations and monitoring. The outcome is AI you can use with known, managed risk and evidence to show for it.
The problem
The challenge we help you address
AI workflows can combine sensitive information and powerful tools in unexpected ways. Untrusted input, excessive permissions, unapproved AI tools and weak monitoring make those workflows hard to govern and hard to investigate when something goes wrong.
The outcome
What the engagement should deliver
You receive an AI security plan tied to your actual deployment, with controls you can implement and evidence that they work. AI risk is treated as part of your wider Cybersecurity programme, not a separate silo.
How we can help
Securing AI access
Limit data and tool access, use dedicated identities, define approvals and protect secrets.
AI data security
Classify sensitive information, apply labels and data loss prevention, and decide what may be sent to which AI services.
Testing and operation
Evaluate harmful inputs, failure cases, logging and incident responsibilities.
Common AI security risks
- Prompt injection: instructions hidden in documents, emails or web pages that try to make an AI tool ignore its rules or misuse its access.
- Oversharing: AI surfacing information that was already accessible to too many people.
- Unapproved AI tools: staff pasting business data into services the organisation has not assessed.
- Excessive agency: agents or integrations allowed to act without suitable limits or human approval.
Where you need a formal management system for AI, we connect these controls to ISO/IEC 42001: AI Management Systems.
Frequently asked questions
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What is AI security?
It covers the controls that keep AI applications from exposing data, being manipulated or acting beyond their authority. It draws on familiar security practice, such as identity, access control and logging, plus AI-specific testing for issues like prompt injection.
When should a business invest in AI security?
Before connecting AI to sensitive data or giving it the ability to act, and as soon as you know staff are using AI tools with business information. A short review early is easier than an investigation later.
Can an AI system be made completely risk-free?
No. Risk needs to be managed throughout the system lifecycle. We identify the relevant exposure, implement controls and make residual risks and review responsibilities visible.
What does implementation include?
A risk review of the AI tools and workflows in scope, control design, configuration of identity, data protection and logging, testing against misuse scenarios, and handover with monitoring and incident responsibilities.
How long does an AI security engagement take?
It depends on the number of AI tools and integrations, the sensitivity of the data involved and the controls already in place. We confirm scope and timing during discovery.
See where the risks sit
Tell us which AI tools are in use or planned and what data they touch, and we will help you see where the risks sit.