Data types
Which documents, fields, images, messages, or databases the solution needs to access.
Security
Security is designed before AI connects to operations.
Aprex designs AI systems, automation, and software with clear data boundaries, access control, logging, and human control points. The goal is useful operational software without unclear data flow, responsibility, or risk.
Last updated: September 16, 2026
- 01
Data boundaries before model choice
An AI project should start with what data the solution actually needs, who can see it, where it is processed, and what the system must not access. Model choice comes after this. Aprex separates public content, internal documents, personal data, confidential information, and production data.
- 02
Access control and roles
When a solution is used by several people or departments, it should have clear roles. Some users may read, others may approve, and some actions should require extra control. This matters in AI agents, document flow, field operations, and integrations with business systems.
- 03
Logging and traceability
Production-near systems should show what happened, when it happened, and which system or user triggered the action. Logging matters for debugging, quality, internal control, and responsible automation.
- 04
Human responsibility
AI can suggest, summarize, and prepare work, but it should not hide responsibility. Where decisions affect customers, finance, safety, legal assessments, or operations, human review and approval must be clear.
Overview
What gets clarified in a project
The security level should fit the workflow risk. These are points Aprex typically clarifies before a solution is used near production.
Providers
Which models, APIs, hosting environments, and third-party services are part of the solution.
Integrations
Which systems may read, write, notify, or trigger actions through APIs and webhooks.
Failure modes
What happens if the model is wrong, data is missing, an integration fails, or a user does something unexpected.
Operations
Who owns the solution, how it is monitored, and how changes are handled after launch.
Privacy
Whether the solution processes personal data, which purposes apply, and which agreements are needed.
Read also
FAQ
Security FAQ
- Can sensitive data be used in AI systems?
- Only when data handling, providers, access, purpose, and risk are clarified. A first pilot should often use anonymized, synthetic, or limited data.
- Do all AI actions need human approval?
- No, but risky actions should have control points. Low-risk tasks may be automated more directly, while responsible decisions should have human approval.
- What should not be sent in the first request?
- Do not send passwords, API keys, secrets, customer data, or sensitive personal data. Describe the data type and need at a high level instead.
- Is this legal advice?
- No. This page describes technical and practical principles. Legal assessments, DPAs, and compliance requirements must be clarified with responsible advisors when the project requires it.
Practical security before large frameworks
For many pilots, the most important step is making data access, integrations, and responsibility concrete early. Aprex should not build systems with broad privileges before the task, failure modes, and risk ownership are clear.
Have an AI or automation workflow with risk?
Send a short description of the data, systems, users, and actions the solution should support, and Aprex can assess the right first scope.
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