Governments and security services are increasingly deploying artificial intelligence to analyze large amounts of data. In Canada, an independent watchdog is now explicitly examining how intelligence agencies use AI, what data they collect, and how far they are allowed to go in doing so. This concerns sensitive issues such as privacy, transparency, and the misuse of technology – but also efficiency and better protection of citizens.
That discussion might seem far removed from your company, but it isn't. The questions people ask security services are the same ones you will have to ask your own AI solutions sooner or later: what do we use AI for, who is monitoring it, what boundaries do we draw, and how do we explain this clearly to customers and employees? In this blog, we translate these major themes into practical lessons for Flemish and European SMEs.
What exactly is going on
In Canada, the independent intelligence watchdog has launched an investigation into the use of artificial intelligence by various security and espionage agencies. The core of that investigation: which AI tools are being used, on what data they run, for what purposes, and whether all of this is taking place within existing legal and ethical boundaries.
The supervisor examines, among other things:
- How AI is used to search and analyze large amounts of information.
- Whether the collection and processing of data is proportionate and legally limited.
- Which internal controls, documentation, and audits exist to prevent abuse or derailment.
The question is not whether AI may be used, but how. The intention is to create clarity and trust: citizens must be able to know that powerful technology is subject to democratic oversight and cannot be deployed without restraint.
Impact on people and society
When security services use AI, it touches directly on fundamental questions in our society: privacy, trust in the government, equal treatment, and the boundary between security and surveillance.
On the positive side, AI can help to work in a more targeted manner: instead of collecting data en masse, better algorithms allow you to filter out irrelevant information faster and focus on real risks. That *can* lead to fewer arbitrary checks and more efficient investigative work.
At the same time, the need for clear rules is growing. Citizens and organizations want to know:
- Which data about them is processed.
- How long they are kept.
- Whether decisions that affect them (for example, inspections, investigations) are influenced by algorithms.
For organizations outside the security world, this serves as a useful mirror. The way we view AI within government today will also set the standard tomorrow for how companies handle customer and employee data. Transparency, explainability, and proportionate use will become the new norm.
Ethical and sustainable considerations
The Canadian example touches on multiple themes that are also relevant to your AI projects.
Ethics and honesty
AI in a safety context often works with risk scores, pattern recognition, and predictions. If the underlying data contains a bias (for example, against certain groups), the AI can distort and amplify it. The regulator's investigation therefore also revolves around the question: are the decisions made with the aid of AI fair and proportionate?
For your company, this means: think in advance about the impact of AI decisions on customers and employees. Do not let IT or data scientists decide alone, but also involve legal, operational, and HR perspectives.
Transparency and clarity
The supervisor checks whether sufficient documentation and explanation exist regarding the AI systems used: which models, which data, and which objectives. Without that clarity, responsible supervision is impossible.
The same applies in companies: if you have to explain to a customer or regulator tomorrow how your AI decisions are arrived at, is that feasible? Clear documentation and a simple story are crucial.
Sustainability, energy consumption and environment
Large AI models consume a lot of energy, especially in high-intensity environments such as national security. Although this is often less central to reporting, it is part of responsible use: which models do you choose, how often do you retrain them, and can it be done more lightly?
For SMEs, this is an opportunity: you don't always have to use the heaviest, most 'hyped' models. Smaller-scale, targeted models are often more energy-efficient, cheaper, and easier to monitor – and therefore more sustainable.
Safety
AI systems in the security sector must themselves be well secured. Insufficiently secured models, datasets, or infrastructure can become a new vulnerability: from data breaches to manipulation of outcomes.
This applies directly to the corporate and SME context: any AI system that impacts processes, customers, or infrastructure must be approached as a critical asset, not as a non-committal experiment.
Safety and risk dimension
The research into AI at intelligence services touches upon a range of concrete risks that are also relevant to companies.
Hacking and data leaks
AI systems are only as strong as their weakest link: often, this is data storage or integration with other systems. In a security context, this involves highly sensitive information, but in an SME, too, this can concern customer data, financial records, or trade secrets.
It is important that AI applications receive the same security approach by default as other critical systems: encryption, access control, logging, and regular audits.
Privacy and unwanted surveillance
AI is making it technically increasingly easy to monitor behavior, recognize patterns, and profile individuals. This is particularly sensitive for security services because of their position of power.
For companies, the line between personalized service and unwanted surveillance is thin. The Canadian approach—clear goals, oversight, and explicit frameworks—offers a direction: collect only what you need, explain why, and ensure people have input or at least insight.
Abuse of AI
Even well-intentioned AI can be abused: through misconfiguration, lack of training, or deliberate manipulation of outcomes. In a security context, this can lead to unjustified suspicion or unequal treatment.
In a business environment, it can result in discrimination (for example, in recruitment), incorrect credit decisions, or the unjustified blocking of customers. Preventing abuse means regulating and controlling not only the technology but also its use.
What does this mean for your business?
The essence of the Canadian story is surprisingly applicable to Flemish and European SMEs: powerful technology requires powerful governance. Not to slow down innovation, but to enable it safely and responsibly.
Some lessons you can translate immediately:
- Organize your own internal supervisor. That doesn't have to be a separate position, but at least a clear division of roles: who is responsible for ethics, privacy, security, and legal review of AI projects?
- Document your AI usage. What exactly do you use AI for, which data, which models, and which decisions are supported by it? This helps with audits, complaints, and building trust.
- Think of people before technology. Start from your customer's or employee's problem, not from 'we need to do something with AI'. This way, you avoid unnecessary data collection and complex systems that no one really understands.
Anyone who consciously tackles these questions today not only builds trust with customers but is also better prepared for regulations such as the European AI Act.
3 concrete recommendations for Flemish and European SMEs
- Create a short AI policy on 2 pages. Describe: what you do and do not use AI for, what data you use, how you handle privacy, and which roles are responsible for oversight. Keep it concrete and understandable for non-technical colleagues.
- Perform a 'light' AI impact scan. For every new AI project: systematically check the impact on privacy, discrimination, security, and the environment/energy consumption. Based on this, decide whether to adjust, scale down, or accelerate the project.
- Provide an explanation upfront. Explicitly inform customers and employees when AI is involved in decision-making: in onboarding, in quotes, and in support processes. Short, clear texts build more trust than technical jargon or silence.
Conclusion: Technology that works for people
The fact that even intelligence services are under scrutiny when deploying AI is no cause for fear, but a sign of maturation. Together, we are learning where the boundaries lie, how to embed technology within clear ground rules, and how to keep people and society at the center.
Your company can also view AI in this way: not as a magical black box, but as a tool that you responsibly integrate into your processes step by step. With attention to ethics, sustainability, safety, and clear communication.
At Canyon Clan, we help SMEs and organizations in Belgium and Europe design AI solutions that are human-centric, explainable, and safe. Would you like to brainstorm about a concrete idea or an ongoing project? Feel free to contact us for a down-to-earth exploration of what AI can mean for your organization – without hype, without doom and gloom, but with a focus on value for people.