Industries / Operating context

Architecture for environments where enterprise systems must survive real scrutiny.

IKEVAR works with high-trust environments where data sensitivity, operational accountability, security review, governance obligations, and implementation constraints shape what enterprise systems are allowed to become.

Sensitive dataHuman reviewIdentity and authorizationAudit evidenceImplementation readiness

Industry architecture priorities

Start with the operating constraints, then design the enterprise system.

Healthcare and Behavioral Health

Secure AI architecture for clinical, behavioral health, telehealth, intake, charting, care operations, and sensitive-data workflows.

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Engage us when

AI is entering clinical or patient-adjacent workflows
Sensitive data may reach models, retrieval systems, or automation
Human review, traceability, and operational accountability are required

Architecture priorities

Separate AI-generated drafts from approved records
Constrain identity, retrieval, and sensitive-data access
Design explicit review, escalation, and exception paths
Capture evidence for architecture, security, and compliance review

Typical artifacts

Healthcare AI reference architecture
Workflow and trust-boundary diagrams
Threat model and data-flow review
Control and evidence map
Implementation readiness plan

Financial Services

Governance-ready AI and cloud architecture for financial institutions, fintech teams, risk operations, and high-trust data environments.

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Engage us when

AI decisions or outputs will influence regulated business processes
Risk, compliance, security, and engineering need a shared control model
Model, data, identity, and workflow evidence must withstand review

Architecture priorities

Map policy obligations to technical controls and accountable owners
Constrain model, data, and tool access by business context
Create evidence paths for high-impact decisions and exceptions
Separate experimentation from governed production patterns

Typical artifacts

AI governance control map
Identity and authorization model
Architecture decision records
Evidence and audit model
Production readiness roadmap

Technology and SaaS

Secure AI product and platform architecture for SaaS, cloud-native, platform engineering, and AI-enabled product organizations.

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Engage us when

GenAI or agents are becoming part of a customer-facing product
RAG, tool use, or MCP-style integrations increase authorization complexity
Teams need reusable security patterns without slowing delivery

Architecture priorities

Treat agents and AI services as governed workload identities
Authorize retrieval and tool use before context reaches the model
Define gateway, policy, secrets, and observability boundaries
Standardize secure patterns product teams can reuse

Typical artifacts

AI platform reference architecture
Agent and tool-use threat model
RAG authorization pattern
Gateway and policy design
Implementation ADR package

Retail and Customer Operations

Secure AI adoption for customer operations, workforce productivity, data workflows, personalization, and governed automation.

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Engage us when

AI pilots are moving toward customer or workforce production workflows
Business teams need speed while security teams need enforceable boundaries
Customer, workforce, or operational data requires controlled use

Architecture priorities

Classify use cases by data sensitivity and business impact
Define approved AI interaction and escalation patterns
Protect customer and operational data across integrations
Create reusable implementation and governance patterns

Typical artifacts

Secure AI adoption roadmap
Workflow risk classification
Cloud and data control model
Governed integration pattern
Operating model and handoff plan

Public Sector and Regulated Environments

Governance-first AI architecture for organizations operating under security, privacy, compliance, procurement, and audit pressure.

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Engage us when

AI adoption requires formal review across multiple stakeholders
Policy requirements must become implementation and evidence requirements
Cloud or AI modernization is constrained by assurance and procurement processes

Architecture priorities

Make control ownership and review paths explicit
Translate governance obligations into buildable architecture
Design evidence capture before production rollout
Create implementation patterns that survive operational handoff

Typical artifacts

Regulated AI reference architecture
Policy-to-control traceability
Evidence register and review model
Stakeholder decision package
Secure implementation roadmap

Architecture perspectives

What an enterprise architecture engagement can look like.

The scenarios below are illustrative architecture patterns, not customer case studies or claims. They show how IKEVAR approaches common enterprise design problems and the artifacts that support a decision.

Healthcare architecture perspective

AI-assisted documentation without giving the model authority over the record

Situation: A clinical team wants AI to summarize intake context and prepare documentation while preserving clinician accountability and sensitive-data boundaries.
Architecture response: Separate AI-generated drafts from approved records, authorize data retrieval before model context is assembled, require clinician review, and capture evidence for the draft-to-approval path.
Artifacts: Workflow diagram, trust boundaries, authorization model, threat model, review controls, evidence requirements, and implementation ADRs.
Decision supported: Leadership can decide whether the workflow is ready for a governed pilot and which controls must exist before broader deployment.

Financial services architecture perspective

Turning AI policy into an engineering control model

Situation: Security, risk, compliance, and platform teams agree on AI principles but do not yet share an implementation model for access, evidence, exceptions, and production approval.
Architecture response: Map policy statements to enforceable architecture controls, named owners, evidence sources, review gates, and production patterns that engineering teams can consume.
Artifacts: Control map, responsibility model, architecture decisions, evidence register, exception workflow, and production readiness criteria.
Decision supported: The organization can move from policy alignment to a buildable governance baseline with clear ownership and evidence expectations.

SaaS architecture perspective

Securing agent and tool use before it becomes product authority

Situation: A SaaS product is adding agentic workflows that can retrieve customer context and call internal or external tools.
Architecture response: Treat the agent as a governed workload identity, scope tools by policy and context, validate retrieval authorization, isolate secrets, and record tool calls and policy outcomes.
Artifacts: Agent trust model, tool authorization matrix, gateway design, prompt-injection threat model, observability requirements, and implementation patterns.
Decision supported: Product and security leaders can define the minimum control plane required before the agent is allowed to influence production workflows.

Architecture consultation

Bring us the constraint, workflow, or decision that is slowing the architecture down.

We can help frame the trust boundaries, controls, artifacts, engineering decisions, and rollout conditions required to move from enterprise ambition to a defensible architecture.

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