Data boundary
Define what may leave the secure environment, what must stay local, what can be synthesized, and what requires human approval before model use.
Secure AI Engineering UAE
Use AI for engineering speed without sending sensitive data to external models.
Mirogate designs AI-assisted engineering workflows for high-security environments where code can be accelerated, but production data, government data, user records, logs, secrets, and private documents need a stricter boundary.
Model Routing
Fast coding, separate review, local sensitive-data handling.
- Frontier coding models work on code, architecture, tests, refactors, and sanitized engineering context.
- A second reviewer model checks generated patches, assumptions, and implementation risks from another angle.
- Local models handle sensitive data interaction, production-log inspection, and mock generation when real rows cannot leave the environment.
- Private repo and document indexes send only the minimal context needed for the engineering task.
- Security review uses cascaded skills for auth, data handling, secrets, dependencies, cloud deployment, and AI-agent risks.
Synthetic mocks
Preserve the failure shape while replacing production rows, private logs, user records, screenshots, and documents with safe debugging fixtures.
Generated-code review
Review AI-generated patches through secure-code modules covering auth, input handling, secrets, data flow, dependencies, deployment, and prompt-injection boundaries.
Proof
Public work behind the service.
Mirogate published secure-ai-engineering-framework and secure-code-skill-cascade to make the AI engineering approach inspectable.
The goal is not to claim that AI makes systems automatically secure. The goal is to use AI where it improves engineering speed while keeping sensitive data, policy decisions, and security review explicit.