Capability

AI Security Architecture

Secure-by-design architecture for enterprise AI — threat models, control baselines, trust boundaries, and runtime enforcement for LLM, RAG, and agentic systems.

Covers the full surface from identity and retrieval to tool governance and model-serving paths
Produces implementable standards and baselines, not checklist-only compliance artifacts
Designed for federated adoption: reusable defaults with governed local adaptation

Discuss your architecture

When to bring this in

This is typically needed when:

A production launch is blocked because security cannot sign off on AI-specific risks — and generic cloud controls are not enough.

Teams rely on prompt filters or post-generation checks instead of structurally isolated trust boundaries.

Retrieval, tool access, or model traffic is not governed by explicit, testable security baselines.

Security needs architecture — threat models, control layers, runtime enforcement — not another policy document or vendor demo.

Multiple teams are shipping AI patterns independently, and there is no common security baseline across them.

What the engagement covers

Scope

A principal-led engagement that produces the security architecture, standards, and adoption path for enterprise AI — from threat model through to runtime enforcement and evidence design.

Threat modeling across prompts, retrieval, tools, identities, and model-serving paths — using MITRE ATLAS, OWASP LLM and agentic AI guidance, and CSA MAESTRO

Security baselines per deployment pattern: LLM, RAG, agentic workflows, and pipeline components — with ownership per control

Deny-by-default gateway posture for model and tool traffic, with allowlisting, exception handling, and policy versioning

Retrieval security enforced before generation: eligibility, permissions, freshness, and provenance checks

Runtime guardrails for unsafe intents, grounding failures, and tool misuse — with controlled refusal paths

Observability and traceability: joinable traces across requests, retrieval, policy enforcement, and tool actions

Tool evaluation criteria for runtime protection, guardrails, LLM firewalls, and CI/CD controls — assessed against your risk model, not vendor positioning

Control evidence aligned to NIST AI RMF — release evidence, audit sampling, and incident investigation

Gap analysis and adoption roadmap aligned to ENISA guidelines and EU AI Act evidence expectations

Typical outputs

What the engagement produces

What changes afterwards

After this engagement

Teams move onto a common security baseline instead of reinventing controls per use case.

Production security reviews become faster and more predictable — the architecture defines what to check and who owns each control.

Retrieval and tool boundaries are explicit and testable, not implicit assumptions buried in application code.

Runtime enforcement produces usable evidence for audit, incident response, and release decisions.

New AI patterns are onboarded through a governed security path rather than bypassing controls through unmanaged deployments.

What this is not

✕A penetration test or red-team exercise

✕A hands-on engineering delivery team

✕A vendor selection exercise in disguise

✕A compliance paperwork exercise detached from runtime architecture

✕A generic AI strategy workshop

Common questions

AI Security Architecture

This is typically needed when:

Scope

What the engagement produces

Threat Model and Risk AnalysisAI-specific threat model across data flows, retrieval, tools, identities, and operational touchpoints — mapped to enforceable mitigations with clear ownership.

Security Standards and BaselinesTestable control requirements and secure configuration baselines per deployment pattern — LLM, RAG, orchestration, model access, vendor tooling, and delivery workflows.

Target Reference ArchitectureTrust boundaries, gateway policies, identity scoping, and control layers — documented as an implementable reference, not a conceptual diagram.

Tool Evaluation and Control FitStructured assessment of where runtime protection, guardrails, LLM firewalls, and CI/CD controls add genuine value — and where architecture should solve the problem first.

Gap Analysis and Security RoadmapCurrent-state gaps and a sequenced path for scaling AI security controls across teams, use cases, and platforms.

After this engagement

What this is not

FAQs

Is this a penetration test?

Do you implement the controls?

Does this cover standards, baselines, and roadmap priorities?

Do you evaluate AI security tools?

Does this apply beyond RAG?

How do you avoid blocking delivery?