The most common AI security misconceptions
More than a billion people now use AI tools at work, and most companies are struggling to keep up with the security implications. Recent enterprise research found that 57% of employees paste confidential business data into public AI services, often without checking how the tool stores or reuses it.
The risks are growing faster than the rules. In Europe, the EU AI Act begins enforcing major obligations on businesses using AI from August 2026, including non-EU companies, such as those based in Switzerland and the UK, that serve EU customers.
In the meantime, four assumptions about AI security keep tripping up otherwise well-run organisations.
1. AI Systems are Inherently Secure
It’s easy to assume AI tools are always designed to be safe – this is not true. Like any technology, AI needs to be checked by a human. Outdated data and coding issues are both potential weak spots that can be built directly into an AI, and can lead to risk. Keep in mind that no system is free from risk; safe use comes from caution, research and scrutiny.
Firms like AI management experts Abilene advisors can guide users through safe use, but all tools still must be set up and used with care: you must check how they store and use your data, and verify the accuracy and safety of outputs they produce.
“The single most common reason a company fails an AI security review is that nobody checked the assumptions,” says Alexis Hirschhorn, ISO 27001 and ISO 42001 Lead Implementer at Abilene Academy, a Swiss training provider for compliance professionals. “Teams adopt a tool because it makes them productive, then treat it like a calculator instead of a system that processes data.”
2. Data Shared with AI is Private
A common myth is that all data you give to AI is kept private. This is rarely true. Some tools – including the most frequently used LLM, ChatGPT – store or use your data to train their models. If privacy settings are not configured correctly, your data may be exposed in ways you have not consented to. Users must read the terms. You must be aware of what data is saved and how it is used.
Do not share private data unless you understand the tool, its rules, and the risks. Businesses should have clear guidelines around inputting sensitive data into AI to keep confidential information safe, and regularly ensure all employees understand their policies.
“That matters under both Switzerland’s revised Federal Act on Data Protection and the EU’s GDPR. Sharing personal data with an AI processor without a clear legal basis is a notifiable failure, regardless of whether anyone outside the organisation ever sees that data.
“Most acceptable use policies on the books today were written before generative AI entered the workflow,” Alexis says. “That’s the gap. Either the tool is on an approved list with a signed data processing agreement, or it’s prohibited for confidential inputs. Halfway positions don’t survive an audit.”
3. AI can Replace Human Security Roles
In all likelihood, AI will not be able to replace those working to keep businesses and individuals secure – at least not anytime soon. AI can help with tasks – it can scan data fast and spot some risks, but it lacks human sense and is prone to errors – some studies suggest that 45% of responses from LLMs contain mistakes. It can miss context, reach poor conclusions, or “hallucinate” information.
A human can judge a case carefully – an essential for security management. In most cases, an AI can only work best with human guidance. Keep in mind that it is a tool, not a security solution.
4. AI Performance Improves Automatically
There are several claims online that AI is smart enough to improve itself – and, by extension, its ability to protect your data – automatically. This is not how it works. AI needs new and clean data to learn. If you’re feeding an AI poor data, the output is more likely to be incorrect.
If you’re using an AI tool to automate your work, you must check that your input is reliable, coherent, and that it is within the terms of your business and the tool to share it. You should also frequently perform checks and tests, and track the appearance and severity of errors to understand the relationship between your data and an AI’s performance. Like any other system, AI needs regular updates and checks.
“As AI becomes embedded in business operations, the assumption that these tools are inherently safe is one of the most dangerous misconceptions we see,” adds Henri Haenni, expert in risk management at Abilene Academy.
Too often, organisations adopt AI tools quickly, driven by competitive pressure, without fully understanding how those tools handle data, where vulnerabilities lie, or what oversight is required. The technology moves fast, but due diligence cannot be skipped. The businesses that get this right are those that treat AI governance with the same rigour they would apply to any other area of operational or legal risk.”
