AI security and governance: How to stay in vontrol.

What AI safety and governance entail.

AI safety refers to the set of technical measures that ensure an AI system does what it is supposed to do and nothing more. AI governance is the layer surrounding it: who is authorized to do what, how do you document decisions, and how do you maintain control when a system operates autonomously. The difference from regular software is that AI is not entirely predictable. The same model can react slightly differently to similar input. That’s why security here doesn’t function as an after-the-fact safeguard, but as a design choice from the very beginning.

Why AI Requires Its Own Security Measures.

With traditional software, you know exactly what will happen in advance: the same input produces the same output. An AI model operates on probability, can react unexpectedly, and can even produce convincing-sounding nonsense. Furthermore, AI often has access to data and actions, and it is precisely this combination of unpredictability and access that requires special measures. You must control not only the code but also the behavior.

What if things do go wrong after all?

Prevention covers most things, but not everything. A system that is demonstrably under control also has a response when something goes wrong: monitoring flags unusual behavior, an agent halts the process, and, where necessary, you can roll back an action. The audit trail shows exactly what happened afterward, so you can recover and prevent it from happening again. A well-known example: without a cost cap, costs can skyrocket if a system gets stuck in a loop. That’s why that limit is built into our systems by default.

Here's how to get started with AI safely today.

Getting started safely with AI is mainly a matter of sequence. Start small, with a single, well-defined process that uses clear data and has a measurable outcome. Define in advance what the system is allowed to do and where a human makes the decision, and set up logging and monitoring before going live. Keep a human in the loop at the critical points, and only scale up once you’ve demonstrated on a small scale that it works and is manageable. This way, you build trust without taking unnecessary risks, and the foundation will already be in place by the time laws and regulations require it.

AI Safety and the EU AI Act.

Security practices and legislation reinforce each other. The EU AI Act requires demonstrability: logging, documentation, human oversight, and risk management. That is exactly what good AI security delivers. Those who set up security properly from the start have already done the hardest work when it comes to compliance. The legal assessment, such as determining whether your system is high-risk, remains the job of a lawyer.

Data: Cloud or self-hosted—a conscious choice.

The most important question regarding the safe use of AI is where your data goes. There are two paths.

1. If you opt for cloud models, you accept that provider’s data processing terms, and the key is to consciously choose enterprise versions that explicitly state your data will not be used to train models.

2. If you opt for a self-hosted solution, you keep everything within your own infrastructure, at the cost of more management and setup. Neither option is inherently right or wrong; it depends on your privacy requirements and the sensitivity of your data.

We discuss this decision with you explicitly in advance, rather than letting the choice be made implicitly. For organizations with strict requirements (such as healthcare or government agencies), self-hosted is often the answer. For most others, a well-configured enterprise cloud is a sound choice.

15+ years of experience: Cube as your digital partner.

We don’t just apply this in theory. For more than 15 years, Cube has been developing software for organizations that can’t afford to come to a standstill, and is ISO 27001 - and NEN 7510-certified. We bring that experience to bear whenever AI comes into play. Our AI governance relies on role-based access via MCP, with an audit trail for every AI action and a deliberate model selection for each use case. We address risks such as rising costs, tool call hallucinations, and prompt injection from the very beginning, rather than after the fact.