Secure AI Architecture
Technical Security Design for AI Systems — Reference Architectures, Threat Modeling, Agent Authorization, and Adversarial Testing Frameworks
AI Governance Program Build defines the policies and oversight. Secure AI Architecture designs the technical controls that enforce them. This is the engineering complement — reference architectures, threat models, authorization frameworks, and testing specifications that make AI systems secure by design.
The engagement produces secure reference architectures for up to 3 AI patterns (e.g., RAG pipeline, multi-agent system, customer-facing chatbot), STRIDE threat modeling adapted for AI-specific trust boundaries (model inputs, tool calls, memory systems, inter-agent communication), agent authorization frameworks, data protection design (PII redaction pipelines, RAG access control models), and runtime guardrail specifications.
An adversarial testing framework covering 15 scenarios is included — the test plan your team will use to validate security controls during development and before release. Model supply chain verification processes ensure that the models you deploy are the models you evaluated.
Who This Is For
Ideal clients for this engagement.
The Problem
What this engagement addresses.
AI-Specific Trust Boundaries
Traditional application security trust boundaries do not capture AI-specific risks. Model inputs, tool invocations, memory systems, retrieval pipelines, and inter-agent communication each introduce trust boundaries that require explicit security design.
Agent Authorization Complexity
AI agents that invoke tools, access databases, send emails, or call APIs need authorization frameworks that constrain their actions to intended scope. Without explicit authorization design, agents inherit excessive permissions and become vectors for unintended actions.
Data Protection in RAG Pipelines
Retrieval-augmented generation combines data from multiple sources with varying access levels. Without access control at the retrieval layer, users access information through the AI system that they cannot access through the source system.
No Adversarial Testing Methodology
Most organizations lack a structured approach to testing AI systems for security failures. Standard QA and functional testing do not cover prompt injection, jailbreaking, data extraction, or agent misbehavior. A testing framework is needed before the first security assessment.
Model Supply Chain Risk
Fine-tuned models, open-source model weights, and model marketplace offerings introduce supply chain risk. Without verification, you cannot confirm that the model in production matches the model that was evaluated and approved.
Deliverables
What you receive.
Secure Reference Architectures
Up to 3 secure reference architectures for your AI patterns (RAG, multi-agent, chatbot, code generation, etc.). Each architecture includes security controls, trust boundaries, authentication/authorization points, and data flow with classification.
AI Threat Models
STRIDE threat modeling adapted for AI trust boundaries. Data flow diagrams with AI-specific components (model inputs, tool calls, memory, retrieval, inter-agent communication). Threat library covering AI-specific attack vectors with countermeasures.
Agent Authorization Framework
Authorization model for AI agents: tool-level permissions, parameter constraints, action scope limits, human-in-the-loop requirements, and escalation triggers. Designed for the specific agent patterns in your architecture.
Data Protection Design
PII redaction pipeline specifications for model inputs and outputs. RAG access control model design ensuring retrieval respects source system authorization. Data classification and handling requirements for training, fine-tuning, and inference data.
Runtime Guardrail Specifications
Input validation, output filtering, content safety, and behavioral boundary specifications for runtime AI guardrails. Implementation-ready specifications for your engineering team.
Adversarial Testing Framework
15-scenario adversarial testing framework covering prompt injection, jailbreaking, data extraction, agent misbehavior, authorization bypass, and supply chain attacks. Test procedures, expected results, and pass/fail criteria for each scenario.
Model Supply Chain Verification
Processes for model provenance verification, integrity checking, and secure deployment. Covers self-hosted models, fine-tuned models, and third-party model marketplace offerings.
Methodology
How the engagement works.
Architecture Discovery & Threat Modeling
Weeks 1 – 2
- AI system architecture review and pattern classification
- Trust boundary identification for AI-specific components
- STRIDE threat modeling for AI patterns
- Data flow mapping with classification and access requirements
Security Design
Weeks 2 – 4
- Secure reference architecture development (up to 3 patterns)
- Agent authorization framework design
- Data protection design (PII redaction, RAG access control)
- Runtime guardrail specification
- Model supply chain verification process
Testing Framework & Handoff
Weeks 4 – 5
- Adversarial testing framework development (15 scenarios)
- Architecture review with engineering team
- Implementation guidance and priority sequencing
- Knowledge transfer and handoff
Engagement Tiers
Scoped to your architecture.
Focused
Single AI pattern — one reference architecture, threat model, and testing framework. For teams building one AI system that needs security design guidance.
- 1 secure reference architecture
- STRIDE threat model for the AI system
- Adversarial testing framework (15 scenarios)
- Runtime guardrail specifications
Standard
Up to 3 AI patterns with agent authorization, data protection design, and model supply chain verification. For organizations with multiple AI systems or complex architectures.
- Everything in Focused
- Up to 3 secure reference architectures
- Agent authorization framework
- Data protection design (PII redaction, RAG access control)
- Model supply chain verification
Platform
AI platform-level security architecture covering shared infrastructure, multi-team patterns, and centralized guardrail services. For organizations building AI platforms that multiple teams build on.
- Everything in Standard
- Platform-level security architecture
- Centralized guardrail service design
- Multi-team pattern library
- Security review process for new AI use cases
Prerequisites
- AI system architecture documentation or diagrams (even informal)
- Access to engineering team building the AI systems
- Description of AI use cases, data sources, and integration points
- AI governance policies (if available — the AI Governance Program Build deliverables, if completed)
Frequently Asked Questions
Common questions.
Do we need the AI Governance Program Build before this engagement?
No — the engagements are complementary but not sequential. If you have governance policies, the architecture is designed to enforce them. If you do not, the architecture stands on its own as technical security design, and governance can be layered on afterward. Many organizations run both engagements in parallel.
What AI patterns do you have experience with?
RAG pipelines, multi-agent systems, customer-facing chatbots, internal knowledge assistants, code generation tools, autonomous agents with tool use, fine-tuned models, and AI-powered workflow automation. The reference architectures are customized to your specific patterns — they are not generic templates.
What is included in the 15-scenario adversarial testing framework?
Scenarios span prompt injection (direct and indirect), jailbreaking, system prompt extraction, data extraction via conversation, tool authorization bypass, agent misbehavior, cross-tenant data leakage through RAG, output injection, and model supply chain attacks. Each scenario has test procedures, expected results, and pass/fail criteria. The framework is designed to be reused by your team during development and before every release.
Related Offerings
Often paired with this engagement.
AI Governance Program Build
Governance complement — the policies, risk management, and oversight structures that this architecture enforces technically.
LLM Application Security Assessment
Adversarial testing of the LLM applications built on these architectures — validates that design-level controls are effective in practice.
Threat Modeling Workshops
Facilitated threat modeling sessions that can extend the AI-specific threat models to new systems as your AI portfolio grows.
AppSec Program Design
Broader application security program that provides the SDLC framework for AI application development alongside traditional software.
Ready to discuss this engagement?
30-minute discovery call. We will discuss your application architecture, your specific concerns, and whether this assessment is the right fit.