Building AI systems that perform well is only half the challenge. Building AI systems that remain secure, trustworthy, and auditable under adversarial conditions requires deliberate architectural choices from the ground up. Netscylla's AI Architecture consultancy brings offensive security insight directly into your design process — helping engineering teams, security architects, and product owners make the right decisions before the wrong ones become expensive.
Modern AI deployments are increasingly agentic — LLMs that plan, call tools, read files, browse the web, send emails, write code, and orchestrate other agents. This shift from passive text generation to active system participation fundamentally changes the security calculus. A misconfigured agent is not just a chatbot that gives bad advice; it is an autonomous process with credentials, network access, and the ability to take irreversible actions.
Our consultants work with your teams to design agentic systems on a foundation of security principles:
Every AI agent should operate with the minimum permissions required to complete its task — and no more. We help you define permission scopes for each agent role, design fine-grained tool access controls, and implement credential isolation so that a compromised agent cannot pivot to systems outside its defined blast radius. We also advise on scoping agentic sessions: what an agent can read, what it can write, and what actions require human-in-the-loop confirmation before execution.
Multi-agent systems introduce inter-agent trust problems: when one agent instructs another, how does the receiving agent verify the legitimacy of those instructions? We design orchestration architectures that enforce agent identity and attestation, prevent prompt injection from propagating between agents, and apply layered validation at each handoff point. We consider the security implications of both centralised orchestrators (single-agent-of-agents) and decentralised peer-to-peer agent meshes.
AI agents that maintain long-running memory — through vector stores, session state, or external databases — must enforce strict boundaries around what each agent can recall and from whom. We architect memory systems with tenant-aware access controls, design retrieval pipelines that cannot be poisoned by untrusted document sources, and specify data retention policies that limit the accumulation of sensitive context over time. For multi-user deployments, we ensure that a user cannot craft inputs that surface another user's data from shared memory stores.
We apply structured threat modelling — adapted from industry frameworks such as STRIDE, PASTA, and OWASP's LLM Top 10 — to your AI system's architecture. We map data flows, trust boundaries, and agent interaction patterns to identify where adversarial influence can enter your system, how it propagates, and where controls should be placed. The output is a prioritised threat register and a set of architecture-level mitigations, expressed in terms that align with your existing security governance processes.
A layered defence strategy for AI outputs involves more than a single safety classifier. We design multi-stage output pipelines that combine model-level safety tuning, structured output schemas to constrain model responses to expected formats, semantic classifiers for detecting policy violations, and downstream validation before outputs are acted upon or displayed. We also advise on fail-safe defaults — what the system should do when a guardrail is triggered — to avoid silent failures or denial-of-service conditions.
AI systems operating in regulated industries must meet evolving requirements under frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001. We help you design the logging, traceability, and explainability capabilities required for compliance and incident investigation. This includes structured audit trails for agent actions and tool calls, model versioning and change management processes, human-oversight mechanisms for high-risk decisions, and red-line policies that define which tasks an AI system should never autonomously perform regardless of instruction.
Our AI architecture engagements are flexible and can be scoped to your current stage of AI adoption:
We review your proposed AI architecture at the design stage — before code is written — to identify security gaps and provide actionable recommendations. Suitable for teams planning new LLM integrations or agentic systems.
A detailed security review of an existing AI system — including data flows, trust boundaries, agent permissions, and control plane configuration. Delivered as a findings report with prioritised remediation guidance.
Ongoing security consultancy embedded within your AI engineering or platform team — providing real-time guidance on security decisions, threat modelling workshops, and review of design documents as the system evolves.