AI security architecture for LLM applications and agents, built by people who ship them
We design AI security architecture for LLM applications and agents: threat models, prompt injection and data leakage controls, identity for agents, and red teaming mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Governance, risk and security
Why LLMs break familiar security assumptions
Traditional application security assumes code and data are separate. Large language models blur that line: any text the model reads, from a user message to a retrieved web page or an email in an inbox, can act as an instruction. Prompt injection is not a bug to be patched once. It is a property of how these systems work, and the architecture has to contain it.
The attack surface also grows with capability. A retrieval system can leak documents across permission boundaries. An agent with tool access can be steered into sending data out or taking actions on someone's behalf. Model outputs flow into browsers, databases and other systems, where they can carry injected code. Third-party models, plugins and MCP servers add supply chain risk.
Most security teams know these risks exist but lack patterns to review AI designs or controls that fit them. AI security architecture closes that gap: a threat model specific to your AI systems, controls in the right layers, and adversarial testing that shows whether those controls actually hold against real attack techniques.
What you get
AI threat model
A threat model for each priority AI system covering prompt injection, data leakage, excessive agency, insecure output handling, supply chain and model abuse, mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS techniques.
AI security reference architecture
The control design across gateway, retrieval, agent, tool and output layers: input and output filtering, context isolation, data loss prevention, secrets handling and logging, with decision records your security architects can review and reuse.
Identity and access design for agents
How agents and AI services authenticate, which workload identities they use, how user authority is delegated and constrained, and how credentials for tools and MCP servers are scoped, rotated and revoked.
AI red team and test plan
Adversarial testing of your AI applications: direct and indirect prompt injection, data exfiltration paths, jailbreaks, tool misuse and permission bypass, with findings prioritized by impact and retested after fixes.
AI security review standard
A checklist and review process your security team uses for every new AI system and third-party AI feature, integrated with existing architecture review and vendor risk processes.
The target architecture for securing AI systems
No single filter stops prompt injection. Security comes from layered controls that limit what a compromised model can reach and make misuse visible. These are the layers we design.
- LLM gateway with policy enforcement
- A central point for all model traffic that authenticates callers, applies input and output policies, redacts sensitive data, enforces rate and spend limits and logs prompts and responses for detection.
- Context isolation
- Clear separation between trusted instructions and untrusted content such as retrieved documents, emails and web pages, with provenance tags so downstream controls know which text came from where.
- Permission-enforced retrieval
- Retrieval that filters content by the caller's identity before it reaches the model, so a successful injection cannot surface documents the user was never entitled to read.
- Agent identity and tool authorization
- Distinct workload identities for agents, least-privilege scopes per tool, validation of every tool call's arguments and human approval for high-impact actions.
- Output handling and data loss prevention
- Encoding and validation of model output before it reaches browsers, databases or other systems, plus checks that block sensitive data from leaving through responses or tool calls.
- Detection and response
- AI-specific telemetry feeding your security operations tooling, with detection rules for injection attempts, unusual tool use and data exfiltration, and playbooks for containing an AI incident.
How we deliver it
Assess
We inventory AI systems and integrations, review their data flows, identities and tool access, and threat model the highest-exposure ones. We test a sample for prompt injection and data leakage to ground the findings in evidence.
Blueprint
We design the AI security architecture: control points in each layer, identity model for agents, logging and detection requirements, and a secure pattern library, so teams build on approved designs rather than reinventing controls.
Build
We work with your platform and security engineers to implement the controls, including gateway policies, retrieval filtering, tool validation, output handling and detection rules, then red team the result and fix what we find.
Govern
We establish recurring red teaming, AI-specific detection and incident response playbooks, security review for new AI systems and vendors, and tracking of emerging attack techniques so controls keep pace.
What changes
- Your security team has a threat model and review standard for every AI system it is asked to approve.
- A successful prompt injection is contained by permissions and approvals rather than relying on a filter.
- Agents operate under their own identities with scoped, revocable access to tools and data.
- Red team findings are tracked, fixed and retested as part of the normal release process.
Related services
- Agentic AI ArchitectureArchitecture for AI agents that use tools and take actions: identity, MCP, orchestration, guardrails and human-in-the-loop controls.
- AI Governance and RiskAI governance aligned to NIST AI RMF, ISO/IEC 42001 and the EU AI Act: inventory, risk tiers, model risk and evaluation that work in practice.
- Generative AI Platform ArchitectureLLM gateway, model routing, evaluations and cost controls that let teams use multiple models safely, observably and within budget.
AI Security Architecture: common questions
What is AI security architecture?
AI security architecture is the design of controls that protect AI systems and the data and tools they touch. For LLM applications and agents it covers threat modeling, prompt injection containment, permission-enforced retrieval, agent identity and tool authorization, output handling, data loss prevention, logging and red teaming, layered so no single control has to be perfect.
How do you prevent prompt injection?
You cannot fully prevent prompt injection today, so you contain it. Separate untrusted content from instructions, filter inputs, and above all limit what the model can do: enforce permissions outside the model, validate every tool call, require approval for high-impact actions and monitor for anomalies. Design so a successful injection reaches nothing valuable.
What is the OWASP Top 10 for LLM Applications?
The OWASP Top 10 for LLM Applications is a community-maintained list of the most critical security risks in applications built on large language models, including prompt injection, sensitive information disclosure, supply chain risks, improper output handling and excessive agency. Security teams use it as a checklist for threat modeling and reviewing AI designs.
What is MITRE ATLAS?
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversary tactics and techniques against AI systems, modeled on MITRE ATT&CK and drawn from real incidents and research. Security teams use it to structure threat models, plan red team exercises and describe AI attacks in terms their operations teams already know.
How should AI agents be given access to enterprise systems?
Give each agent its own workload identity with least-privilege, short-lived credentials scoped to the tools its task needs. When an agent acts for a user, use delegated authorization that never exceeds that user's rights. Validate tool arguments, log every action, and require human approval for irreversible or high-impact operations. Never share a human's standing credentials with an agent.
What does an AI red team test?
An AI red team tests how an AI system fails under attack: direct and indirect prompt injection, jailbreaks, extraction of system prompts or sensitive data, permission bypass in retrieval, misuse of agent tools and harmful outputs. Good exercises target the real application with its data and tools connected, and produce prioritized findings that are retested after fixes.
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