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/ SECURE AI ARCHITECTURE
Secure AI Architecture
The security of an AI system is decided at design time, not after rollout. We help you design and review LLM, RAG and agent-based solutions where data, models, tools and permissions are protected from the foundations up.
[ 01 ] / WHAT IT IS
What is secure AI architecture, and how does it differ from traditional application security?
In short
Secure AI architecture is the security design and review of AI-based systems: large language models, RAG, AI agents and ML pipelines. Threat modeling exposes the attack surface, and we design controls so that data, models, integrations and agent permissions stay protected across the whole lifecycle.
AI systems open up a new attack surface. A language model can treat input as instructions, so prompt injection is not merely a bug but a risk that comes with how the technology works. RAG systems reach into company data, so weak access control leads to data leakage. Agents call tools and take actions, so a successful manipulation can have real consequences. And the model and data supply chain (pre-trained models, datasets, libraries) raises new questions of trust.
Defense therefore needs a layered architecture: filtering inputs and outputs is only one element. Just as important are least privilege for agents and tools, document-level access control in RAG, isolation and sandboxing for code execution, human approval for risky actions, and logging and detection.
The service builds on standard and community frameworks: the OWASP LLM Top 10 and OWASP material on agentic threats, MITRE ATLAS (a knowledge base of attack techniques against AI systems) and NIST AI 100-2 (a taxonomy of adversarial machine learning). A reviewed design can then be verified in practice with an LLM penetration test and AI Red Teaming.
Frameworks and guidelines
- OWASP Top 10 for LLM Applications
- OWASP agentic threat guidance
- MITRE ATLAS
- NIST AI 100-2
- NIST AI RMF
- ISO/IEC 42001
- Google SAIF
- NCSC and CISA guidelines for secure AI system development
[ 02 ] / WHO IT'S FOR
Who benefits from a secure AI architecture review?
Anyone building on artificial intelligence who wants to get security right at design time rather than patch it afterwards.
Enterprises rolling out internal copilots or RAG
When a model is connected to company documents, access control and protection against data leakage are the key questions.
Builders of agentic systems
Where AI calls tools and takes actions, designing permissions, approvals and guardrails is fundamental.
Financial, healthcare and industrial organizations
Sensitive data, regulated processes and high consequences: design mistakes are most expensive here.
Security architects and CISOs
People who have to approve AI projects and need a sound, verifiable yardstick to measure proposals against.
System integrators and software houses
The security design of AI solutions delivered to customers is a contractual and reputational matter.
[ 03 ] / WHAT'S INCLUDED
What secure AI architecture work covers
We tailor scope to the type and maturity of the system. The following elements are typical.
01
Use cases and data flows
We understand what the system is for, what data it reaches, who uses it and where the trust boundaries lie.
02
AI-specific threat modeling
We extend traditional methods (such as STRIDE) with OWASP LLM Top 10 and MITRE ATLAS perspectives, so attacks specific to models, data and agents come into view.
03
RAG and data access security
Document-level permissions, vector database protection, preventing index poisoning and data leakage, and tenant isolation.
04
Agents and tool use
Least privilege, a narrow toolset, human approval for risky actions, sandboxing for code execution and identity management for agents.
05
Layered defense against prompt injection
Input and output checks, separating instructions from data, output handling and protection of connected systems, without relying on filters alone.
06
Model and data supply chain
Vetting external models, datasets and libraries, signing and integrity, an AI bill of materials (AIBOM), a model registry and access control.
07
MLOps and CI/CD pipeline security
Permissions in training and deployment pipelines, secrets management, separating environments and traceability of changes.
08
Logging, detection and operations
Logging designed with privacy in mind, abuse and anomaly monitoring, cost and availability protection, and an AI incident response plan.
[ 04 ] / HOW IT WORKS
How an AI architecture review runs
We work closely with your AI and development teams and fit into your existing design process.
Discovery and scope
We clarify the system's purpose, data, integrations and regulatory context.
Threat modeling
In a joint workshop we map the attack surface, attacker goals and the most likely attack paths.
Architecture design or review
We assess the design from documents, diagrams and configurations, and propose a target architecture.
Control catalog and priorities
We provide concrete controls, ranked by risk and written so developers can implement them.
Implementation support
Through consultation, reference patterns and design reviews, we help the team put the controls in place.
Verification
We prove the finished system with a practical attacker-style test and refine the design based on the findings.
[ 05 ] / WHAT IT SOLVES
What secure AI architecture solves
Design flaws are cheapest to fix at the start of development. That is especially true of AI systems: permissions, data flows and agent guardrails are hard and costly to change later. The service prevents problems rather than patching them afterwards.
It also gives customers, auditors and regulators credible evidence that the AI system was designed with security in mind and documented properly, which supports the expectations of AI governance as well.
- the AI-specific attack surface uncovered early
- protection against data leakage in RAG and connected systems
- agent permissions and limits designed on solid ground
- a verified model and data supply chain
- a documented, auditable security design
What you receive
- an AI-specific threat model
- a target architecture and trust boundary diagram
- a control catalog prioritized by risk
- an agent and RAG permissions plan
- an executive summary
- an implementation roadmap and verification recommendation
[ 06 ] / WHY QYNTAR
Why work with Qyntar on AI architecture?
Because we know AI systems as both attackers and builders, so we see not only the flaw but also how to solve it well.
Hands-on attack experience
Members of our team attack AI systems themselves, so they know real techniques, not just theory.
Engineers and security specialists together
Security specialists and engineers work as one team, so recommendations fit real development practice.
A standards-based method
We build on OWASP, MITRE ATLAS and NIST frameworks, so the result supports compliance work too.
Verification is in-house
We then prove the design with an attacker-style test. See AI Red Teaming.
[ 07 ] / FAQ
Frequently asked questions: Secure AI Architecture
What is the difference between an AI architecture review and an AI penetration test?
A review assesses the design before or during implementation, so flaws are cheap to fix. A penetration test attacks the running system. They complement each other. See LLM penetration testing.
Why aren't prompt filters or guardrails enough?
Filters can be bypassed, and not every attack shows up in the prompt (for example instructions hidden in documents). Security has to be layered: permissions, isolation, human approval and monitoring are needed as well.
What does AI agent security mean?
Agents call tools and take actions, so the key question is what they can do if someone manipulates them. The answer is narrow permissions, a limited toolset, human approval for risky steps, and logging.
How do you secure a RAG system?
The essentials are making sure the model only reaches documents the asking user is entitled to, and not treating retrieved content as trusted instructions. Protecting the index, separating tenants and checking output matter too.
Is it safer to run our own model than to use an external API?
Not automatically. Self-hosting gives more control over data but also more responsibility (operations, updates, supply chain). The decision depends on data sensitivity, regulation and capability, and a review helps weigh exactly that.
How long does a review take?
A focused review and threat modeling session usually takes a few days to a few weeks, depending on the system's complexity. We give a firm estimate once we know the scope.
Do you help with implementation too?
Yes. We support the recommendations through consultation, reference patterns and review of the implementation, then verify the result with a practical test if you wish.
[ 08 ] / RELATED SERVICES
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[ + ] / CONTACT
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