AI Security & Agentic Development
Secure AI systems and multi-agent architectures
Build intelligent agentic systems with security at the core. We design AI architectures, implement guardrails, and defend against prompt injection and data leakage — secure by design from orchestration to deployment.
What's included
Engagements start with a threat model specific to your system — identifying data-leakage, prompt-injection, tool-abuse, and model-misuse risks — move into secure design with least-privilege tool access and human-in-the-loop checkpoints where needed, and finish with red-team validation of prompts, agents, and integrations before production launch. We work with teams shipping LLM-powered products or internal AI tools, companies adopting agentic workflows with sensitive data, startups that need security credibility for their AI features, and enterprises evaluating AI risk before a wider rollout.
Design and implement multi-agent workflows with clear boundaries, tool access controls, and observability.
Input validation, output filtering, and policy enforcement to keep AI behaviour within bounds.
Safe API usage, secret management, and data handling for OpenAI, Anthropic, and open-source models.
Testing and hardening against adversarial prompts, indirect injection, and tool abuse.
Who this is for
How we engage
Threat model
We identify AI-specific risks: data leakage, prompt injection, tool abuse, and model misuse.
Secure design
Architecture with guardrails, least-privilege tool access, and human-in-the-loop where needed.
Validate
Red-team testing of prompts, agents, and integrations before production launch.
Frequently asked questions
Can you help us build AI agents, not just secure them?
Yes. We design and implement agentic systems ourselves — agent orchestration, tool access controls, and observability — with security built in from the start rather than reviewing someone else's build after the fact. The same team that threat-models your system is also the one architecting it, so guardrails are part of the design instead of an afterthought.
Do you test for prompt injection?
Yes. We run structured adversarial testing against your prompts, RAG pipelines, and agent tool chains — covering both direct prompt injection and indirect injection via retrieved documents or tool outputs. Testing happens before production launch as part of our validation step, and again after any significant change to your prompts, tools, or model provider.
Which AI platforms do you support?
We work across major LLM providers — including OpenAI and Anthropic — as well as self-hosted and open-source models, with a focus on secure integration patterns (safe API usage, secret management, data handling) that transfer across platforms rather than one-off fixes tied to a specific vendor.
What are the main security risks of LLM-based agents?
The recurring ones are prompt injection (direct, or indirect through documents and tool output), sensitive data disclosure, insecure handling of model output by downstream systems, and excessive agency — an agent given more tool access or autonomy than its task needs. The OWASP Top 10 for LLM Applications is a useful public reference for these. Our threat model works through them against your specific system.
How do you limit what an AI agent can do?
By applying least privilege to its tools and data: scoping credentials narrowly, separating read from write actions, validating and constraining tool inputs and outputs, and putting a human approval step in front of high-impact actions such as payments, deletions or external messages. The aim is that a manipulated agent still cannot cause serious harm, instead of relying on the model to always behave.
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Ready to talk it through?
Book a free 30-minute consultation. No obligation — just a frank conversation about your situation and whether we can help.
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