ITL-10 CONSULTING SERVICE
AI Security & Governance
Generative AI is already inside your company — with or without approval. The question is not whether to govern it, but whether you govern it before the first incident.
The problem
Teams connect language models to internal data, create agents with access to systems, and adopt AI tools on their own, while security is still debating whether to allow it. The risks are new and concrete: injection of malicious instructions through untrusted content, agents with excessive permissions, leakage of sensitive data in context, and unvetted AI vendors. Blocking everything doesn't work; releasing everything without governance works even less.
How we work
AI and MCP governance
A governance framework for corporate use of language models and agents, including the open protocol that connects models to tools and data (MCP), with an explicit RACI matrix across security, identity management, and technology governance — who approves a connector, who reviews a permission, who answers for an incident.
Agent and integration risk assessment
Analysis of agent architectures and AI integrations: prompt injection surfaces, credential and permission scope (especially machine identities), data isolation, and exfiltration paths.
Usage policy and shadow AI
An acceptable AI use policy people can actually follow, with approved tracks that remove the incentive for uncontrolled parallel use.
AI vendor assessment
Objective due diligence criteria for AI tools and providers: data handling, retention, training on customer data, and contractual controls.
Applied experience
An AI and MCP governance framework, with a RACI matrix across the cybersecurity, identity, and technology governance teams, developed and led at a large financial institution — practical experience in a field where most of the market is still in theory.
Frequently asked questions
What is MCP and why does it need its own governance?
It's an open standard that connects AI models to corporate tools and data — and each connector is, in practice, a machine credential with permissions over real systems. Without governance, connectors proliferate the way integration keys proliferated in the past decade: with no owner, no review, and too much privilege.
Is prompt injection a real or theoretical risk?
Real and demonstrated: malicious instructions embedded in emails, documents, or pages that an agent processes can lead it to execute unauthorized actions. The mitigation is architectural — limiting the agent's permissions, treating all external content as untrusted, and requiring human confirmation for sensitive actions — and that is exactly what the assessment covers.
Should we simply block AI tools?
Total blocking creates shadow AI: people use personal tools, outside any control, with company data. The approach that works combines approved, secure tracks with a clear policy and monitoring — steering the use, not pretending it doesn't exist.
Need a conversation about AI Security & Governance?
Describe the scenario in two lines. We'll answer with an honest read — including if the answer is that you don't need us.