LLM application review
Threat modelling of prompts, context, retrieval, tools and output handling, informed by the OWASP LLM risk catalogue.
AI Security & Shadow AI
The AI attack surface your existing security stack does not see
LLMs, copilots, agents, plugins and connectors introduce paths that traditional controls were not designed to cover. We map them and put controls in place.
How is AI security different from traditional cybersecurity?
Traditional cybersecurity protects systems, networks and data against defined attack patterns. AI security additionally addresses risks arising from the model's behaviour: prompt injection and instruction hijacking, training and retrieval data poisoning, sensitive data leaving the organisation through prompts, excessive agency in autonomous agents, insecure tool and plugin integrations, and outputs that are wrong but convincing. The controls are different because the attack surface is the model's context and permissions, not only the infrastructure.
Focus areas
Agentic AI security
Permission scoping, tool authorisation, action approval, isolation and logging for autonomous and semi-autonomous agents.
Data leakage control
Preventing confidential data from reaching public AI tools, through policy, gateways, DLP and monitoring with governance.
AI gateway design
Centralised access to approved models with authentication, logging, redaction, rate control and usage visibility.
Frequently asked questions
What is prompt injection?
Prompt injection is an attack in which instructions embedded in content the model reads — a document, web page, email or tool response — override or subvert the instructions given by the application. It matters most when the model has access to tools, data or actions, because the injected instruction can then cause real effects rather than just unwanted text.
Can we simply block public AI tools?
Blocking alone tends to push usage further underground. The effective pattern is a sanctioned path — approved models through a controlled gateway, with clear rules on what data may be used — combined with visibility over unsanctioned use and proportionate technical enforcement.
Assess your AI attack surface
A technical review of your LLM applications, agents, integrations and AI data flows.