AI Governance, Privacy & AI Security
Tested AI applications, prioritised findings with fixes, and controls that keep prompt injection and data leakage in check.
The problem
AI applications are attacked through language, not only code. A hidden instruction in an email, a web page or a shared document can make a copilot or agent leak data or take actions nobody intended.
Agents now read mailboxes, query databases and call APIs with real permissions. RAG applications can return documents the user should never see. Classic penetration tests do not cover these risks, and most AI applications go live without a dedicated test.
What we do
We map each AI application: models, prompts, data sources, tools, identities and users, and identify how it can be attacked and what an attacker could reach.
We check agent identities, connector permissions, retrieval access control and output handling against least privilege and the OWASP Top 10 for LLM Applications.
We attack the application with direct and indirect prompt injection, jailbreaks, system prompt extraction, data leakage and attempts to misuse tools and agent actions.
We test whether retrieval respects user permissions, whether poisoned documents change answers and whether sensitive data leaks through the index.
We prioritise findings by risk, help your teams fix them and retest, so you know the fix works.
We set up input and output filters, human approval for high-impact agent actions, logging and monitoring for the applications that need them.
How we deliver
Our security consultants model the threats, review the architecture and advise on secure design for copilots, agents and AI applications.
Our testers run the red teaming in your environment, and our engineers implement fixes, guardrails and permission changes with your teams.
We retest AI applications after changes and on a schedule, and monitor guardrails and agent permissions as a managed service.
Frameworks
Platform
From December 2026 the Fidempli platform applies the guardrails live: every prompt and agent action is checked before it reaches the model, and high-impact actions wait for a person.
Explore the platform
A penetration test looks at infrastructure and application code. AI red teaming also attacks the model's behaviour, its prompts, its data sources and the actions agents can take. We combine both where it makes sense.
Yes. We test how Copilot and Copilot Studio agents handle indirect prompt injection, oversharing and data from connected sources, within the limits of Microsoft's terms for testing.
We prefer a test environment that mirrors production. Where we test in production, we agree scope, test accounts and limits with you in advance.
A widely used list of the most important security risks for applications built on large language models, from prompt injection to excessive agency. We use it to structure tests and findings.
For high-risk AI systems, the Act requires an appropriate level of accuracy, robustness and cybersecurity. Independent of regulation, any AI with access to data or systems should be tested before it goes live.
A senior consultant replies within one business day. Or join the 3–5 design partners shaping the platform before launch.
