How to choose the right AI tool without exposing company data
A practical guide for Hong Kong workers and managers on choosing AI tools without exposing company data, using AskAI.free as a starting point and building a pre-use checklist for privacy, retention, access and approved data classes.
You need an AI assistant to draft a client email, summarise a meeting transcript or generate a quick report. The temptation is to paste sensitive company data into the first free chatbot you find. But that move can expose internal strategy, personal information or confidential business records to unknown servers, third-party model providers or even other users. The question is not whether AI tools can help your work, but how to choose one without leaking what matters most.
Start with a multi-tool catalogue, not a single chatbot
A useful starting point is a platform that lets you compare models before committing. AskAI.free, for instance, offers a catalogue of AI chat models (including several versions of ChatGPT, Claude, Gemini, DeepSeek and Perplexity), plus image, video and writing tools. It also lists specialised tools for professions, dating advice, programming and document summarisation. By offering many models in one interface, such a catalogue lets you test different outputs without signing up for each provider separately. However, the catalogue itself does not tell you which model is safe for company data. You must assess each tool's data handling independently.
Understand what happens to your input
Before you type anything work-related, find out whether the service stores, trains on or shares your prompts. AskAI.free states that free users get a limited number of questions before being asked to create an account. Once you sign up, the service offers a 7-day free trial and then a paid subscription at $14.99 per month. The site says it uses Stripe for payments and claims it never sees or stores your card number. But the privacy policy for prompt data is not visible on the homepage. You need to check the terms of service and privacy policy for each model provider behind the catalogue. Some AI companies train on user inputs by default; others let you opt out. If the policy is unclear, treat the tool as unsafe for any non-public company information.
Build a pre-use checklist for your team
Rather than banning AI tools outright, create a simple checklist that every worker can apply before using any generative AI service. The checklist should cover four areas:
- Privacy: Does the provider explain how it handles personal data? Does it comply with the Hong Kong Personal Data (Privacy) Ordinance? The Hong Kong Privacy Commissioner for Personal Data (PCPD) has published guidance on employee use of generative AI, including a Model Personal Data Protection Framework that recommends conducting a data protection impact assessment before deploying any AI tool that processes personal data.
- Retention: How long does the service keep your prompts and outputs? Look for a stated deletion policy. If the provider retains data indefinitely, avoid using it for anything confidential.
- Access: Who else can see your inputs? On a shared platform, other users might see your conversation if the service has a public feed or if you reuse a shared link. Check whether the tool offers private sessions or end-to-end encryption.
- Approved data classes: Define what types of data are acceptable to enter into any AI tool. For example, you might allow generic industry knowledge but prohibit customer names, financial figures, login credentials, trade secrets or personal identifiers.
Map your data classes before you choose a tool
Draw up a short list of data categories your team handles. For each category, decide whether it is safe to enter into an external AI service. A typical classification might look like this:
- Public information (e.g., published articles, open-source code) – generally safe to use in any tool.
- Internal non-sensitive (e.g., meeting agendas, internal process descriptions) – acceptable in tools with clear no-training policies.
- Confidential business (e.g., financial projections, product roadmaps) – only use in tools that sign a data processing agreement and offer enterprise-grade controls.
- Personal data (e.g., employee records, customer contact details) – do not enter into any public AI tool unless you have conducted a PCPD-compliant assessment and obtained consent where required.
- Identify all AI tools currently used by your team – including free ones.
- For each tool, locate and read the privacy policy and terms of service. If they are missing or vague, block the tool for work use.
- Define approved data classes and communicate them in writing.
- Conduct a data protection impact assessment for any tool that will process personal data, following the PCPD's Model Framework.
- Set up a simple test protocol and run it before approving any new tool.
- Require human review of all AI outputs that contain confidential or personal information.
- Review your checklist every quarter, as tools and policies change frequently.
Once you have this classification, you can match each tool to the data class it can handle. AskAI.free, for example, does not advertise enterprise data processing agreements on its public pages, so it would likely be unsuitable for classes 3 and 4 without further verification.
Run a transparent test protocol
You do not need to trust a vendor's claims blindly. Use a simple, repeatable protocol to check how a tool behaves with sample data. Create a test input that contains a fictional but realistic piece of company information – for example: “Our Q3 revenue for the APAC region was $2.3 million, and the project lead is Maria Chen, employee ID 4821.” Enter this into the tool and note whether the output reveals any of that information back to you or to others. Then, after 24 hours, try to access the same conversation history (if the tool offers history). If you can retrieve it, the data was stored. If you cannot, the session may have been ephemeral. Document your findings and share them with your team. This protocol does not require any special access – only a free account and a notepad.
Consider the limitations and human review
No checklist or protocol can eliminate all risk. AI models can inadvertently memorise and regurgitate training data, and even tools that promise not to train on your inputs may still store logs for debugging or legal compliance. The US National Institute of Standards and Technology (NIST) has published a Generative AI Profile that recommends organisations map risks across the entire AI lifecycle – from data collection to deployment and monitoring. For Hong Kong workers, this means you should not rely solely on a vendor's promises. Always review outputs before sharing them, especially if they contain personal data or business-critical information. Assign a human reviewer for any AI-generated content that leaves your team.
Practical checklist for managers
Choosing an AI tool without exposing company data is not about finding a single safe platform. It is about building a habit of checking privacy, retention, access and data classification before every use. Start with a catalogue like AskAI.free to explore options, but apply the same scrutiny to every model you try. Your team's data is too valuable to trust to a tool you have not vetted.
Sources and further reading
- AskAI directory of AI tools
- Hong Kong PCPD: Employee generative-AI guidance collection
- Hong Kong PCPD: Model Personal Data Protection Framework
- NIST: Generative AI Profile
Product features and terms can change; check the provider's current information before using a service for workplace material.