Architecture
System architecture, data flows, integrations, interfaces, scalability, and multi-tenancy
AAM-002
AI Architecture
Review
Is your AI architecture built to last?
Modern AI systems are more than a model. Models, data sources, RAG, agents, APIs, identities, cloud platforms, and business applications form a complex system.
The AI Architecture Review creates transparency across components, data flows, integrations, and dependencies – and shows where architectural decisions affect security, robustness, scalability, and trustworthiness.
Discuss the AI Architecture Review →01
Why
Every additional AI component expands what a solution can do – and at the same time increases its complexity, dependencies, and potential attack surface. Insecure interfaces, unclear trust boundaries, vendor dependencies, or poorly understood data flows can create long-term technical and organisational risks.
A resilient architecture provides the foundation for security by design, scalability, resilience, and sustainable operations. The review helps identify risks early, before they become embedded in production systems.
Trust does not begin in production. It begins with architectural decisions.
02
What
Five perspectives on AI architecture.
Models
Model selection, model strategy, hosting, model changes, and vendor dependencies
Data & Knowledge
Data sources, data quality, embeddings, vector databases, RAG, and knowledge management
Security
AI security architecture, identities, access controls, API security, secrets, segmentation, and supply chain
Governance
Responsibilities, architecture principles, lifecycle management, documentation, and change management
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How
We combine architecture workshops, document analysis, and technical reviews. We examine not only individual components, but also how they interact, where relevant trust boundaries lie, and which dependencies are critical.
Understand
Understand objectives, use context, and the existing AI landscape
Map
Map components, data flows, integrations, and dependencies
Review
Review the architecture for security, robustness, and scalability
Challenge
Challenge risks, trust boundaries, and critical dependencies
Improve
Prioritise architectural improvements and support further development
04
Knowledge
After the AI Architecture Review, you have a much clearer understanding of your technical foundation:
How your AI architecture is actually structured and which components interact
Where critical data flows, trust boundaries, and external dependencies exist
Which technical risks and single points of failure matter
Which architectural decisions affect security, robustness, and scalability
Which improvements should be prioritised and how the architecture can evolve
Typical deliverablesArchitecture Review ReportArchitecture OverviewRisk OverviewPrioritised RecommendationsArchitecture Improvements
05
scip AG
An AI architecture must do more than work.
scip combines long-standing experience in reviewing complex IT, cloud, and security architectures with our own research into Trustworthy AI. This allows us to examine AI systems beyond individual models or technologies, especially where established architecture principles meet new AI-specific dependencies.
Wir hinterfragen Datenflüsse, Schnittstellen, Identitäten, Trust Boundaries, externe Services und Betriebsmodelle mit dem Ziel, creating architectures that not only work today, but remain secure and understandable as they evolve.
20+
Security
AI Research
06
Perspective
AAM is not a linear process. Depending on the context, other questions may become relevant and different capabilities can be applied where they add value.
07
Insights
Our Architecture Reviews combine established security and architecture practice with ongoing research into AI Security and Trustworthy AI.
Legacy Technology – Forgotten Systems, Hidden Risks →ISO/IEC 23053:2022 — Framework for AI Systems Using Machine Learning →Decision Security – From Secure Information to Trustworthy Decisions →ISO/IEC 5338:2023 — AI System Life Cycle Processes →AI Assurance & Trustworthy AI →08
Build AI on an architecture
you can trust.
A resilient AI architecture does not happen by accident. Let us examine which architectural decisions hold up today – and which should be challenged before the next step.
Discuss with scip →