In an increasing number of countries, security services are using information freely available on the internet: news articles, social media, websites, and forums. They call this open-source intelligence, or OSINT. It was recently explained to the public that no “personal data” is collected in the process and that they limit themselves to what everyone can see anyway. That sounds reassuring, but at the same time raises legitimate questions: what exactly does this mean for privacy, trust, and the use of AI in analyzing such data?
This is familiar to your company. You too can use AI today to analyze large amounts of publicly available data: from market research to reputation monitoring. The core question is not whether that is allowed, but how to do so carefully, transparently, and in line with European values.
What exactly is going on
A government agency has clarified that security services use open-source intelligence (OSINT). Specifically, this concerns information that is publicly accessible: think of media reports, public websites, and publicly accessible social media posts. According to the explanation, no personal data is collected and no privacy rules are violated, precisely because they limit themselves to sources that everyone can consult.
The message is therefore: public data is indeed actively searched and analyzed, but this takes place within a legal framework and without additional, hidden forms of surveillance. At the same time, it involves large-scale and systematic processing, often with the aid of AI and advanced search and analysis tools. It is precisely there that the need for further clarification and clear frameworks arises.
Impact on people and society
The fact that governments and organizations use OSINT is not new in itself. What is new is the scale and speed at which AI can recognize patterns, establish connections, and flag risks. Whereas an analyst previously needed hours to review a number of sources, an AI system can today filter thousands of messages per minute.
For society, this presents opportunities: faster risk assessment, better crisis preparedness, and faster detection of disinformation or organized fraud. At the same time, concern is growing about a “transparent person,” even when “only” public sources are consulted. For citizens and employees, the distinction between public, anonymized, and personal sources is often not transparent.
For organizations, the lesson is clear: trust is not only a legal matter, but also a relational one. What is technically and legally permissible is not automatically what people perceive as fair and respectful.
Ethical and sustainable considerations
OSINT and AI raise a range of ethical and sustainability questions that are also relevant to your company:
Ethics and transparency. Ethically speaking, it is not enough to say, “It’s online anyway, so we can use it.” People often place content in a specific context (e.g., a target audience, a zeitgeist) and do not expect that data to be analyzed endlessly. Being transparent about what you collect, for what purpose, and how long you retain it is crucial for trust.
Honesty and bias. AI models that analyze public data adopt the skews in that data. If certain groups have a louder presence on social media than others, they are given more weight in your analyses. This can lead to unfair decisions, for example in risk assessment or customer selection.
Sustainability and energy consumption. Large-scale data analysis requires energy. Unbridled scraping of everything “just because it’s possible” is not only legally risky but also ecologically unwise. A sustainable approach means: data minimization, targeted collection, and the use of efficient models.
Safety. The massive collection of (including public) data creates new “data treasure troves”. Anyone who fails to build robust security around this runs the risk of that information being misused – by hackers, competitors, or malicious actors.
Safety and risk dimension
The security and abuse risks surrounding OSINT and AI are real, but well manageable if approached with a level head:
Hacking and data leaks. Large databases with collected content are interesting targets. Even if the data is formally public, its combination, structure, and enrichment can be highly sensitive. A leak then reveals not only “what was online,” but also “who is linked to whom,” “who exhibits what behavior,” etc.
Privacy and profiling. By cleverly combining public data, you can profile individuals or organizations far beyond what they reasonably expect. Legally and ethically, the line between public and private then quickly becomes blurred.
Abuse of AI. The same tools you use for risk management or reputation monitoring can also be used for steering, manipulation, or unwanted surveillance. Without clear governance and logging, it can be difficult to demonstrate that your systems are being used correctly.
A sensible approach starts from privacy by design and security by design: limit the data you collect, anonymize where possible, and build in controls at both human and technical levels.
What does this mean for your business?
As a Flemish or European SME, you probably do not operate at the level of a security service. But the underlying questions are the same: how do you handle public data and AI responsibly?
Perhaps you monitor what is being said about your brand on social media daily, have a competitive analysis tool, or use AI to pick up market signals. That is valuable and legitimate in itself, as long as you:
- be clear about the purpose: why are you collecting this data?
- Consciously limit what you collect: not everything that is possible is necessary.
- ensures a governance framework: who has access, how long do you retain data, how do you monitor the output of AI?
It is also important to combine legal and ethical frameworks. GDPR, ePrivacy, and national legislation provide a minimum standard. However, if you truly want to build trust with customers, employees, and partners, you often go a step further than what is strictly required.
3 concrete recommendations for SMEs
- Create a clear OSINT and AI policy. Write down briefly and clearly which public data you collect, why, how long you retain it, and who has access. Explain this internally to your teams as well.
- Apply data minimization and anonymization. Collect only what you need for a specific purpose and see if you can work with aggregated or anonymized data instead of individual profiles.
- Conduct periodic ethical and security checks. Have your AI and data systems audited for bias, privacy risks, and security at least annually. Involve IT, management, and business people in this process.
Concluding paragraph
AI and open data do not have to be the start of a surveillance society. On the contrary, they can help to identify risks earlier, make better decisions, and work smarter – provided that you keep people at the center. That means being transparent, handling data carefully, and designing your technology so that security, privacy, and sustainability are built in, not added afterwards.
At Canyon Clan, we help your company deploy AI and data analytics in a down-to-earth, ethical, and future-proof manner. From strategy and governance to the concrete development of secure, reliable solutions. Would you like to explore the possibilities for your organization, without hype and without doom-mongering? Feel free to contact us for a no-obligation consultation.