AI Security
AI agents act with only the access they need, and every action can be traced.
Why It Matters
AI agents now read documents, call tools and move data on their own. Many hold long-lived keys with broad rights, and nobody reviews what they do.
A crafted document can make an agent leak data, and a stolen key can run up a bill or open the systems behind it.
What We Do
Identity and Credentials for AI Agents
Each agent acts under its own identity, with only the access its task needs.
AI Red-Teaming
We attack the model and its tools to find what can be made to fail.
Prompt-Injection Defence
We test and block instructions hidden in documents, pages and tool descriptions.
Data-Leak Prevention
A model cannot reveal what the person asking may not see.
Model Supply-Chain Audits
We check where a model, its weights and its data came from, and whether they changed.
What Changes for You
Agents With Limited Access
Each agent gets its own identity and short-lived credentials for one task.
Attacks Found First
We attack your models and agents in a controlled way and fix what we find.
Data Stays In
Sensitive data cannot leave through an answer, a log or a stolen key.
How the Work Runs
We start with the agents you run today. You name the model owners, the data each agent can reach and the actions it may take.
List Models, Agents and Tools
We record each agent, its owner, its model, its tools and its data. Your teams confirm what each one is allowed to do. The output is an agent register with owners, models, tools and data.
Map the Paths an Attacker Would Use
We follow inputs, tool calls and keys to find where an attacker could take control. You decide which actions need human approval. The output is a threat model for each agent and the controls it needs.
Test Within Agreed Rules
We try prompt injection, tool poisoning, key theft and data extraction in a controlled setting. Every finding is reproduced and ranked. The output is evidence for each finding and a ranked fix list.
Add Identity and Limits
We give each agent an identity, short-lived credentials, guardrails and usage limits. Your teams keep control of model choice and product behaviour. The output is a working identity, guardrail and logging setup per agent.
Watch and Retest
We log agent actions, alert on unusual use and retest after each change. You receive the evidence and the open risks. The output is a monthly view of agent use and open risks.
Team and Engagement Options
Who tests your agents and models, and what your team keeps.
A Team That Tests Agents
A lead, an AI security engineer and a tester work through your agents and models.
Work Beside Your Product Team
We join your design reviews and test each change before it ships.
A Clean Handover
You receive the agent register, guardrail rules and test evidence, each with a named owner.
Platforms and Technologies
We work with the model platforms and agent protocols your teams use. Azure OpenAI, Mistral and Jais are the model platforms we secure. An agent protocol is the agreed way one agent calls another agent or a tool. MCP connects a model to tools and data, and A2A lets agents from different systems work together.
- AI and machine learning
- Azure OpenAI
- Mistral
- Jais
- Model Context Protocol (MCP)
- Agent2Agent (A2A)
- Cloud
- NVIDIA GPUs
- Security and cryptography
- HashiCorp Vault
Naming a product does not mean a partnership with its vendor.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Healthcare
Patient apps, scheduling and billing, and clinical data integrations, engineered for health-data protection.
Public Sector
Citizen portals, case management, payments and integrations with government registries, designed to recognised accessibility guidelines.
Questions
What product and security leaders ask before an AI agent goes live.
What drives the cost?
The number of agents and models, their data sensitivity, where they run, and the depth of testing. Fixes and monitoring add work.
How do we start?
With one agent or one model in a controlled setting. The test shows the real paths before you change the wider estate.
What does AI security work not cover?
We do not build the model, write your AI product or accept the business risk for an answer. We also do not replace legal advice on how the system may be used.
Who owns the agent and the data?
You do. We work under your access rules and hand over the tests, guardrails and logs. Our access is removed when the work ends.
Can you secure agents and models we did not build?
Yes. Identity, guardrails and monitoring sit around the agent, so they work with in-house, supplier or hosted systems. We need the configuration and the connections, not the model weights.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.