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Private AI
Security.
Audit the security posture of your private LLM — data isolation, access controls, model safety, and infrastructure hardening.
Capabilities
What It Covers
A private AI deployment is only as secure as its weakest layer. Data isolation failures, inadequate access controls, and model instructions that can be manipulated through crafted inputs are the most common risks. This review examines every layer — from infrastructure to model behaviour.
Process
How It Works
Review deployment architecture
We document the infrastructure configuration, access model, data flows, and model instructions.
Test data isolation, access controls, and model behaviour
We attempt to extract data beyond intended scope, escalate access, and test model instructions against crafted inputs.
Deliver findings with remediation plan
A structured report covering each risk layer with specific remediation steps and compliance mapping.
Who Benefits
Use Cases
Businesses deploying private LLMs on-premise or in private cloud
Owning the infrastructure means owning the security. This review confirms the deployment is configured correctly at every layer.
Organisations handling sensitive data with AI
When AI has access to health records, financial data, or customer information, data isolation and access controls must be verified — not assumed.
Regulated industries implementing AI
Finance, healthcare, and legal organisations face specific data handling requirements. This review maps findings directly to applicable regulations.
Common Questions
What People Ask
Common risks include inadequate data isolation (the model accessing data it shouldn't), weak access controls (unauthorised users querying the model), insufficient logging, and model instructions that can be manipulated by crafted inputs.
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Verify Your Private AI Is Secure at Every Layer.
Request a private AI security review. We'll assess your deployment architecture, data isolation, and model safety.