Enterprise Architecture Patterns for Secure and Regulated Environments
Reusable integration, identity, network, monitoring and governance patterns derived from real-world enterprise systems and architecture practice.
These patterns represent architectural decisions repeatedly applied across different applications and environments. They focus on security, operational clarity and long-term maintainability in complex enterprise landscapes.
Reverse Proxy–Centric Identity Pattern
What it is
Authentication and authorisation are handled at the reverse proxy/access layer, allowing applications to focus on business logic while enforcing consistent controls.
When to use
When onboarding applications into an enterprise identity landscape with strict security and audit requirements.
Why it matters
Centralised identity enforcement reduces implementation errors, simplifies audits and improves long-term maintainability.
Related case studies
Contract-first API delivery
What it is
API contracts (OpenAPI/WSDL) are treated as the source of truth, enabling early validation, testing and cross-team alignment.
When to use
When integrating multiple teams, vendors or legacy systems across organisational boundaries.
Why it matters
Contract-first delivery reduces integration risk and prevents late-stage architectural changes.
Network-First Application Onboarding Pattern
What it is
Network constraints, port mappings and routing paths are analysed and documented before application-level changes are implemented.
When to use
In segmented environments with proxies and strict firewall rules.
Why it matters
Early network validation prevents deployment delays and security exceptions later in the delivery lifecycle.
Related case studies
Operational Monitoring Baseline Pattern
What it is
A monitoring baseline combining infrastructure signals, API-based checks and synthetic HTTP probes to cover connectivity, authentication and integration health.
When to use
For hybrid cloud/on-prem environments relying on external SaaS and critical integrations.
Why it matters
Clear separation of failure domains improves incident response and operational confidence.
AI Governance Boundary
What it is
A central governance platform between internal applications and external LLM providers. All AI traffic flows through a single boundary that enforces authentication, RBAC, DLP scanning, rate limiting, audit logging, and compliance controls. Governance policies are managed through a self-service admin UI with approval workflows — not config files or code deployments.
When to use
When an organisation needs governed AI adoption: multiple applications accessing LLM services, regulatory requirements (GDPR, EU AI Act) demanding audit trails and data protection, and non-technical stakeholders needing to manage security policies without developer involvement.
Why it matters
Direct LLM integration distributes credentials, duplicates controls, and makes usage invisible. But a governance platform goes beyond a proxy — it makes AI policy manageable as a product, with role-based access, change approval, and compliance dashboards that satisfy both security teams and regulators.
Related case studies