Connect AI, security and cloud
Plan AI, security, and cloud as a cohesive solution.
An AI pilot needs suitable data, permissions and operations before it can support daily work. A cloud migration depends on applications, security rules and responsibilities. We plan these aspects together so individual decisions contribute to a workable overall solution.
From your requirements to practical results
Many projects stall at handover points: data cannot be used as planned, a pilot has no operational owner or a new platform complicates approvals. A shared architecture makes these dependencies visible. We plan the first use case with the responsible business, data, security and operations teams and document the decisions.
From benefits to technical decisions
A specific workflow determines the capabilities needed. We identify users, data and desired outcomes. These inform requirements for models, applications, platforms and operations.
Align data access and identities
Sources, permissions and technical identities must fit the use case. We also consider permission changes, logging and information sharing. Data owners are involved in planning.
Choose a suitable runtime environment
On-premises infrastructure, OpenShift, Azure, AWS, Oracle Cloud, Alibaba Cloud and Google Cloud provide possible platform contexts. Selection considers data location, integration, existing expertise and operational effort. Specific services are chosen after their suitability has been assessed.
Build security controls into the workflow
Threat modeling, access policies, secrets management and approvals are tailored to the application. Planned controls are tested in the pilot. Documented limitations support an informed decision about production use.
Monitor quality, cost and operations
A solution needs clear signals for errors, quality and resource use. We plan evaluation, alert handling and responsibilities together. Thresholds and escalation paths reflect the use case and agreed operating model.
Choose suitable platform components
Tools such as Argo CD, Keycloak, OpenBao, Prometheus and Grafana can support relevant technical tasks. We choose components according to requirements and the existing environment. The resulting combination stays manageable and documented.
A clear path to deployment
Make dependencies visible
We look at a specific use case across business domain, data, security, and platform. Open decisions are prioritized based on impact on the initiative.
Test the components together
A bounded workflow connects the necessary components. Tests cover benefits, access, failure behavior, observability and intended operations.
Agree on a common approach
Results are incorporated into a coordinated architecture and an expansion plan. Responsibilities, decision points and handovers are documented.
What you receive
- A common view of benefits, architecture and technical dependencies.
- A pilot that tests how the essential components work together.
- A prioritized expansion plan with responsibilities and operational requirements.
Three ways to get started
Architecture and dependency assessment
For a shared target design: use case, data flows, security requirements and platform options.
Discuss your project: Architecture and dependency assessmentIntegrated solution pilot
For testable integration: a bounded workflow, real access scenarios, quality checks and operational signals.
Discuss your project: Integrated solution pilotImplementation and expansion planning
For coordinated delivery: prioritized components, decisions, deployment and operational handover.
Discuss your project: Implementation and expansion planningQuestions before you decide
Why should AI, security, and cloud be planned together?
The topics share data, identities, interfaces and operational tasks. If these dependencies are clarified at an early stage, technical and organizational hurdles can be dealt with in a more targeted manner. Joint planning focuses on the selected use case.
Does this require a major transformation program?
No. A single, well-defined process is sufficient as an introduction. It should map the most important dependencies and have clear evaluation criteria. Scope and participants are selected in such a way that decisions can be made promptly.
Can sensitive data remain in our own environment?
This can be an architecture requirement. We assess processing, storage, logs and external services across the entire data flow. Whether the required boundary can be maintained depends on the solution and its capabilities.
Which cloud platform is best?
Selection follows your data, integration, operational and cost requirements. Existing contracts and skills are considered. A comparison explains suitable options and their implications before specific services are selected.
Do we need Kubernetes for every solution?
No. A container platform can make sense if applications and operational requirements fit it. For other tasks, managed services or a simpler runtime environment may be more suitable. The additional operational overhead is factored into the decision.
How is an AI pilot prepared for production use?
Alongside task quality, we review data rights, permissions, failure cases, cost and operational responsibility. Open issues receive owners and decision criteria. These findings determine an appropriate production scope.
Can existing tools continue to be used?
Yes. We first assess which tasks your existing tools already support. Additional components are introduced where needed and integrated into workflows. Administration, interfaces and long-term maintenance are considered in the evaluation.
What do we get as a basis for the next investment?
The agreed scope can include an architecture overview, pilot findings, risks, and a prioritized action plan. Assumptions, dependencies, and expected operational tasks are identified. This allows you to specifically commission the next step.
Where do your AI, security, and platform questions intertwine?
Describe the planned workflow and the main open decisions. We help define a coordinated assessment or pilot with clear deliverables.
Discuss your project