Data minimisation
We only ask for the context needed to assess fit, scope work, and deliver the engagement. Sensitive data is handled deliberately rather than collected by default.
Security
Security and data handling for AI-enabled workflows
AI systems only become useful when teams can trust how data, access, deployment, evidence, review and handover are handled from the beginning.
Principles
Practical safeguards before production
We keep security conversations concrete: what data is involved, who can access it, where the system runs, what gets logged, what evidence is needed, and what happens when AI output needs review.
Access control
Project access is limited to the people who need it for delivery. Production access, if required, is scoped and agreed with the client.
Cloud-aware architecture
We design with deployment environment, permissions, logging, cost, and operational ownership in mind from the start.
AI guardrails
For AI-enabled workflows and automation, we define data boundaries, review points, escalation paths, and quality checks before scale-up.
Client ownership
Engagement terms should make ownership, handover, repository access, credentials, and third-party service accounts explicit.
Incident pathway
Security questions and incident reports can be directed to security@cxsense.com.au for review and response.
AI systems
Guardrails before rollout
For AI-enabled automation
We define authentication, permissions, data flows, API exposure, monitoring needs, and deployment assumptions before production launch.
For AI workflows and operational review
We map what the AI can access, what it can decide, where human review is required, and how output quality should be evaluated.
Contact
Security questions
For security questions, data-handling review, or incident reports, contact security@cxsense.com.au.