AI Security

AI systems have a different attack surface than traditional software. The threats are newer, the tooling is less mature, and most security teams haven’t had to think about things like prompt injection or training data poisoning before. We help organisations get ahead of these problems before they turn into incidents.

AI Security Audits

We review your AI stack end-to-end. Model selection and configuration, data pipelines, training and fine-tuning processes, deployment infrastructure, access controls, and how your models interact with external tools and data sources.

The output is a security assessment with concrete findings and a prioritised remediation roadmap. Not a generic checklist, but an evaluation specific to your architecture, your threat model, and the regulatory environment you operate in. We cover OWASP Top 10 for LLMs, NIST AI RMF, and the EU AI Act where applicable.

Inference Monitoring and Observability

Once your models are in production, you need to know what’s going into them and what’s coming out. We build monitoring systems that watch inference traffic for anomalies: prompt injection attempts, unusual input patterns, data exfiltration through model outputs, and model behaviour drift over time.

This isn’t just logging. We set up alerting pipelines that flag suspicious activity in real time, so your team can respond before an attacker gets what they’re after. We also track output quality metrics so you know when your model starts giving worse answers, whether that’s from data drift, adversarial input, or a degraded retrieval pipeline.

Prompt Injection and LLM Attack Testing

We test your AI systems against the attacks that actually matter. Direct and indirect prompt injection, jailbreak techniques, system prompt extraction, training data extraction, and tool-use abuse. If your agents have access to external tools (APIs, databases, file systems), we test whether those tools can be weaponised through the model.

This goes beyond running a list of known jailbreaks. We craft attacks specific to your system’s architecture, its system prompts, its tool integrations, and the data it has access to. The goal is to find the gaps that a motivated attacker would find, and help you close them before that happens.

Access Control and Authentication for AI Systems

AI systems often end up with more access than they need. A model connected to your internal APIs, a RAG pipeline indexing sensitive documents, an agent with write access to production databases. We help you design and implement access control frameworks that enforce least privilege across your AI infrastructure.

This includes authentication between services, scoped API keys for tool-using agents, role-based access to model endpoints, and data classification so your retrieval pipeline doesn’t surface confidential documents to users who shouldn’t see them.

MCP and Plugin Governance

If you’re using Model Context Protocol (MCP) servers or third-party plugins with your AI systems, you’re extending your trust boundary every time you add one. We assess the security posture of your MCP integrations, review plugin permissions, and help you build governance processes for vetting new extensions before they get access to your data.