How we secure AI deployments — the full picture.

All data — at rest, in transit, and during processing — remains within Australian-hosted infrastructure. This applies to training data, user inputs, model outputs, logs, and any intermediate processing artefacts.

We architect solutions on Australian cloud regions (AWS Sydney, Azure Australia East/Southeast, GCP Sydney) and explicitly prohibit data routing through offshore nodes.

Where AI models require API calls, we ensure those endpoints resolve within Australian boundaries or deploy models locally within your environment.

Every deployment environment is hardened in line with the ASD Essential Eight mitigation strategies at the maturity level your organisation is targeting (typically Maturity Level 2 or 3 for government).

Application control — Only approved applications and scripts can execute. We whitelist inference engines, orchestration tools, and supporting services.

Patch applications and operating systems — Automated patching pipelines for the AI stack including OS, container runtime, ML frameworks, and dependent libraries.

Configure Microsoft Office macro settings — Where Office integration exists, macros are disabled or restricted to signed, vetted code only.

User application hardening — Browsers, PDF readers, and user-facing components are configured to block ads, Java, Flash, and unnecessary web content.

Restrict administrative privileges — Dedicated admin accounts, just-in-time elevation, no shared credentials, no standing privileges.

Multi-factor authentication — All access to AI management consoles, model registries, data stores, and deployment pipelines requires MFA. No exceptions.

Regular backups — Model artefacts, configuration, and system state are backed up with tested recovery procedures and immutable retention.

We structure AI deployments so they support — rather than complicate — your IRAP assessment. ISM controls are baked into the architecture from the start.

Network segmentation — AI workloads are isolated in dedicated network zones with strict ingress/egress rules. Model serving, data processing, and management interfaces sit in separate segments.

Cryptography — All data in transit uses TLS 1.2+ (TLS 1.3 preferred). Data at rest uses AES-256 encryption with customer-managed keys where required. Key rotation is automated.

Access control — Role-based access control (RBAC) with the principle of least privilege. Access reviews are scheduled and documented. Privileged access is logged and alertable.

System hardening — Base images are CIS-benchmarked. Unnecessary services are removed. Default credentials are eliminated. Container images are scanned before deployment.

Audit logging — All system events, data access, model invocations, and administrative actions generate immutable audit logs shipped to a centralised SIEM.

Data minimisation — AI models receive only the data fields required for their specific function. We strip, mask, or tokenise personal identifiers before they enter the processing pipeline wherever possible.

Purpose limitation — Data used for one function cannot be repurposed for another without explicit authorisation. This is enforced through access controls and pipeline design, not just documentation.

Consent management — Where AI processes data requiring consent, we integrate with your existing consent management systems and ensure the AI respects opt-out and withdrawal signals.

Retention controls — Processed data, model inputs, and outputs follow defined retention schedules. We automate deletion at the end of retention periods and log when it happens.

Privacy impact support — We provide the technical inputs your privacy team needs to complete Privacy Impact Assessments (PIAs) for AI-related processing activities.

Prompt injection defence — Input validation, prompt sandboxing, and output filtering to prevent adversarial inputs from manipulating model behaviour. System prompts are isolated from user inputs.

Output guardrails — Models are constrained to their intended domain. Output classifiers, content filters, and boundary checks prevent hallucinated, harmful, or out-of-scope responses.

Model access control — Tiered access so sensitive functions (data retrieval, system actions) are restricted to authorised roles.

Red-teaming and adversarial testing — Structured adversarial testing before go-live to identify jailbreaks, data leakage, bias, and unintended behaviours.

Model supply chain security — We verify the provenance of pre-trained models and third-party components. Models are scanned for known vulnerabilities and backdoors.

Rate limiting and abuse prevention — API endpoints are rate-limited and monitored for anomalous usage patterns that could indicate automated abuse or extraction attempts.

Structured logging — Every model invocation, data access event, administrative action, and system change generates a structured log entry shipped to your SIEM in real time.

Real-time monitoring — Alerts for anomalous model behaviour, unexpected data access patterns, authentication failures, and configuration changes. Your security operations team gets visibility from day one.

Drift detection — Infrastructure-as-code and policy-as-code baselines are monitored continuously. If a configuration deviates from the approved baseline, an alert fires and the change is flagged.

Compliance dashboards — Dashboards mapping your AI deployment’s current state against the relevant control framework (ISM, Essential Eight, or your internal standard).

Incident response integration — AI-related security events feed into your existing incident response process. We document AI-specific response procedures (model isolation, rollback to known-good state).