Practical notes for people trying to govern AI without pretending a policy document can see what happens in production.
Requiring a person to approve every agent action feels safe until the queue grows and people start working round it. The fix is deciding which actions need approval, and recording each decision.
Read articleAn agent does something unusual. Someone has to explain whether it was authorised, who allowed it and what happened next. Logs that show it ran are not the same as evidence that it was allowed.
Access granted for one task or role often outlives it. When an AI agent acts on that access, the old permission keeps working. Here is the control question to ask, and what a record should show.
AI adoption projects end. The rules and permissions the project team set do not. If nobody inside can explain them, nobody can safely change them. A practical handover test, and what a record should show.
A documented incident response plan is a starting point, not a guarantee of readiness. This checklist covers the six domains regulated UK firms need to have in place before an incident happens, from governance and detection through to post-incident review.
Staff paste client data, legal documents, and financial records into ChatGPT and Copilot every day. Most firms have a policy that forbids it and no control that enforces it. Here is how to build something that actually holds.
Gemini for Google Workspace is safe at the platform level, but it inherits whatever sharing and permission sprawl already exists in your Drive and Gmail. Here is where the real data exposure risk sits, and what to examine before you let Gemini loose on your organisation.
Agentic AI systems can book meetings, send emails, query databases, and execute code without human sign-off at each step. That autonomy creates a security model most enterprise teams have not planned for. Here is where the risks sit and what controls actually work.
Most of the EU AI Act has applied since 2 August 2026, and enforcement has started for the rules already in application. The AI Omnibus moved the high-risk dates: 2 December 2027 for Annex III uses and 2 August 2028 for AI embedded in regulated products. Here is what applies now, what comes next and how UK firms can be in scope.
Microsoft 365 Copilot is safe by design at the platform level, but it inherits and amplifies whatever permission sprawl you already have. Here is how it reaches data, where the leakage risk really sits, and what to check before and after you switch it on.
An AI data leakage assessment shows you where staff can expose client, commercial, or regulated data through the AI tools they already use. Here is what one covers, what it usually turns up, and why regulated firms are booking them now.
Deploying a foundation model from a third-party provider exposes your organisation to supply chain risks that most AI governance frameworks have not caught up with yet.
Prompt injection turns your AI system's instruction-following capability into an attack vector. Most governance frameworks haven't caught up to what this means in practice.
When a supplier breach exposes your data, you inherit the incident. Here is what the first 72 hours actually look like and why clear ownership of the response shapes the outcome.
The EU AI Act isn't a GDPR sequel, it targets AI systems by risk level and some of the tools in your security stack are already in scope.
Every prompt sent to an AI model is a data transfer decision, and most organisations have no policy governing where that data goes.
When the threat comes from inside, the playbook most teams have prepared is almost useless.
Organisations can now have AI processing sensitive data across dozens of vendor tools they never explicitly approved for AI use, and traditional vendor risk frameworks were not built for this.
When your staff use a cloud-hosted LLM, that data is processed on infrastructure you do not own, in a jurisdiction you may not have approved. Most organisations have no visibility into this. Many do not yet realise it is a problem.
Shadow AI refers to AI tools used within an organisation without IT or security approval. Here is what it means, why it creates serious risk, and how to detect it before it causes damage.
An AI governance framework defines how an organisation controls, monitors, and accounts for AI use across its operations. This guide explains what one contains and how to build it without stalling adoption.
An AI audit trail is a tamper-evident log of every AI interaction that records what was asked, what the model returned, and what happened next. This article explains why these records are essential for compliance and accountability.
A multi-model AI strategy uses different AI models for different tasks based on capability, cost, and risk profile. This article explains why organisations are adopting this approach and what it takes to manage it well.
AI policy enforcement is the technical and operational process of ensuring that AI governance rules are applied consistently at runtime, not just documented in policy files. This article explains how to close the gap between written policy and actual practice.
Shadow AI is the new shadow IT. Without governance, every AI interaction is an unaudited decision. Here's why policy-first matters.
