AI systems are only as secure as the architecture they run on.
OrbitWorks designs the security architecture, governance controls, and operational foundations that AI-enabled systems need before they reach production. CISSP-certified. Fortune 150 infrastructure experience. Principal-led from assessment through handoff.
This is the work that makes everything else defensible.
What goes wrong when security is not part of the architecture
These are the patterns we see in organizations that built first and asked security questions later. Every one of them is more expensive to fix after the fact than to design correctly from the start.
Security bolted on after the build
Most AI-enabled systems are built for capability first and security never. By the time governance questions arrive, the architecture has already hardened around assumptions that are expensive to change. Retrofitting security into a system that was not designed for it costs more than doing it right the first time.
Compliance theater instead of control design
Mapping a framework is not the same as implementing controls. Organizations that treat compliance as a documentation exercise end up with evidence gaps when the audit arrives. Real compliance alignment means designing systems so controls are inherent in how they operate, not described in how they should.
AI governance treated as someone else's problem
Agent systems that access tools, retrieve data, and take actions create security surfaces that traditional application security models do not cover. Without explicit governance architecture, AI-enabled workflows inherit risk that no one has been asked to own.
Operational readiness assumed, not designed
A system that works in development but has no incident response path, no operational runbooks, and no monitoring strategy is not production-ready. Operational governance is architecture work. It belongs in the design phase, not the post-launch scramble.
Security and operational architecture designed for AI-enabled systems
Six capability areas that cover the security and governance surface of modern AI deployments, from architecture design through operational handoff.
Security Architecture & Trust-Boundary Design
We define the security architecture that AI-enabled systems need before implementation begins. Trust boundaries, data flow controls, network segmentation, identity strategy, and access models are documented and validated as part of the design, not discovered during the pen test.
Threat Modeling for AI-Enabled Systems
Traditional threat models miss the attack surfaces that agentic and AI-integrated systems introduce. We model threats specific to agent workflows, retrieval systems, tool-access chains, and model interactions using OWASP Agentic Security guidance alongside established frameworks.
Compliance Alignment & Control Mapping
We align architecture decisions to the frameworks your organization operates under: NIST AI RMF, NIST SSDF, CIS Controls v8.1, ISO/IEC 27001:2022, GDPR, and CMMC readiness. Control mapping is tied to actual system behavior, not aspirational documentation.
Identity, Access & Secrets Management
Access design for AI-enabled systems requires more than user RBAC. We design identity models that cover agent-to-service authentication, least-privilege tool access, secrets lifecycle management, and workload identity across cloud and hybrid environments.
Operational Governance & Incident Response
We design the operational framework: monitoring strategy, alerting thresholds, runbooks, escalation paths, and incident response procedures. Systems that cannot be operated safely in production are not production-ready, regardless of what they can do in a demo.
Audit-Ready Documentation & Evidence Practices
Every engagement produces architecture decision records, control documentation, and governance artifacts designed to support internal review, client assurance, and regulatory inquiry. Evidence generation is built into the delivery process, not created retroactively.
Framework and standards alignment
Our security architecture work is informed by established frameworks applied practically, not aspirationally. Alignment is tied to actual system behavior and control implementation.
What a Security & Operations engagement delivers
Depending on scope, deliverables typically include:
- Security architecture documentation with trust boundaries and data flow analysis
- Threat model covering AI-specific and traditional attack surfaces
- Control mapping aligned to applicable regulatory and framework requirements
- Identity and access design for users, agents, services, and automation
- Secrets management strategy and implementation guidance
- Operational governance framework with monitoring, alerting, and incident response
- Audit-ready evidence packages and architecture decision records
- Risk register with prioritized remediation recommendations
- Operational runbooks covering normal operations and exception handling
Why security architecture comes first
The other two OrbitWorks practice areas. AI Agent Architecture and Infrastructure Automation, are built on the security and governance foundation defined here.
Agent workflows need trust boundaries before they need orchestration. Infrastructure needs access models before it needs deployment pipelines. Security architecture is not a separate workstream. It is the prerequisite.
The cost of fixing security after the build is always higher than designing it in.
Start with a security architecture review. We identify risk, map controls to your compliance requirements, and define the governance foundation your AI systems need before implementation begins.
Discuss a Security Engagement