Problems We Address
Security recommendations create value only when they can be implemented reliably. AI-enabled applications frequently span orchestration logic, business APIs, identity providers, retrieval systems, observability pipelines, and human review interfaces. Weakness in any of those layers can undermine an otherwise sound design.
Orbyntis provides focused engineering support for secure AI applications and the systems around them. This can include new workflow implementation, integration hardening, approval and policy controls, observability instrumentation, and remediation following architecture or red-team findings.
Engagement Approach
We begin with explicit requirements and acceptance criteria. Changes are designed to fit the existing stack and operating constraints, with attention to least privilege, safe failure, testability, audit evidence, and maintainability. Security-sensitive behavior is verified through targeted tests rather than assumed from configuration alone.
Enterprise Scenarios
- Implementing controlled tool access and human approval paths
- Hardening model-to-system integrations and credential handling
- Adding security telemetry and traceable decision evidence
- Remediating risks found during an agent assessment or red-team exercise
- Building a production pilot with bounded permissions and rollback paths
The emphasis is dependable software engineering: controls that are understandable, testable, and supportable by the teams that will operate them.