Artificial intelligence has become part of everyday life. We use AI assistants to write emails, debug code, analyse documents, generate images, research topics, and solve problems. A simple prompt can save hours of work.
But there is an important question many users overlook: What happens to the information we share with an AI system?
When you enter a prompt, upload a document, or paste a piece of source code, you are sharing information with a system. Depending on the service, account type, settings, and integrations involved, that information may be processed, stored, reviewed, or used in different ways.
This does not mean cloud-based AI is inherently unsafe. It means users should understand how their chosen service handles data before sharing anything sensitive.
1. Your Prompt Is Data
People often treat AI conversations like private discussions. However, an AI prompt is also a data submission.
Consider some everyday examples:
A developer pastes proprietary source code to troubleshoot an application.
An employee uploads an internal business report for summarisation.
A student shares personal information while asking for career advice.
A business owner uploads customer records to analyse sales trends.
A user shares screenshots containing email addresses, phone numbers, or account details.
These actions may seem harmless, especially when the AI produces a useful answer. But the information being shared could be confidential, commercially valuable, or personally identifiable.
The first principle of AI privacy is simple: do not share information with an AI service unless you understand whether you are permitted to share it and how that service handles it.
2. What Happens to Data Sent to Cloud AI?
Most popular AI assistants operate through cloud infrastructure. When you submit a prompt, it is transmitted to the provider's systems, where the request is processed and a response is generated.
The exact data-handling process varies between providers and products. Depending on the service and its policies, information may be subject to:
Processing: Your prompt and attachments are processed to generate a response.
Storage: Conversations or uploaded files may be retained for a period of time.
Model improvement: Some services may use eligible user content to improve their models, depending on account type and privacy settings.
Safety and abuse monitoring: Certain interactions may be reviewed or retained for security and abuse prevention.
Third-party processing: Connected tools, integrations, or external services may receive information required to perform an action.
It is important not to assume that every AI provider follows the same rules. Consumer accounts, business subscriptions, and API services can have different data controls and retention policies.
For example, OpenAI provides separate documentation covering data handling in consumer services, ChatGPT data controls, and API data usage. Anthropic also publishes information about how user data is handled and whether it may be used for model training.
Read the applicable policy for the exact product and account you use. Do not rely on assumptions based on another provider's policy.
3. Information You Should Think Twice Before Sharing
AI can be extremely useful for working with information, but not every piece of information belongs in a prompt.
Be particularly careful with the following:
Passwords and authentication credentials: Never paste passwords, API keys, private keys, access tokens, or recovery codes into a general-purpose AI assistant.
Personal identification: Avoid sharing identity documents, financial account details, or unnecessary personal information.
Customer and employee records: Do not upload personal or confidential records without appropriate authorisation and safeguards.
Proprietary source code: Check your organisation's policies before submitting private repositories, business logic, or commercially sensitive code.
Confidential business documents: Contracts, financial statements, internal strategies, and unpublished plans may require additional protection.
Medical and legal information: Treat sensitive records with particular care and use services approved for the relevant purpose.
If you need AI assistance with sensitive material, consider removing identifying details, replacing real values with placeholders, or using an approved environment with appropriate data protections.
4. Third-Party Integrations Add Another Layer
Modern AI assistants can connect to external applications, databases, cloud storage, email platforms, and business systems.
These integrations make AI more useful, but they also introduce additional data flows. Information may move beyond the AI provider to another service when an integration is used.
Before connecting an application, consider:
What information can the integration access?
Does it have read-only access or permission to modify data?
Is access limited to the information required for the task?
Can the connection be revoked?
What privacy and security policies apply to the external service?
Grant integrations only the permissions they genuinely need. Convenience should not become a reason to provide unrestricted access to business or personal data.
5. Prompt Injection Is Also a Security Concern
Data privacy is not the only concern when using AI. Systems that read documents, browse websites, or interact with external tools can also encounter malicious instructions hidden inside the content they process.
This is commonly associated with prompt injection.
For example, an AI assistant might be asked to summarise a document. That document could contain instructions designed to manipulate the assistant into revealing information, ignoring its original task, or attempting an unauthorised action.
Prompt injection is particularly important in applications where AI systems have access to tools, private files, or external services.
Developers should treat external content as untrusted input, restrict tool permissions, validate actions, and require appropriate authorisation before sensitive operations are performed.
The OWASP Top 10 for Large Language Model Applications provides useful guidance on risks such as prompt injection and insecure handling of AI system capabilities.
6. Cloud AI vs Local AI: Understanding the Difference
One alternative to sending every request to a cloud-based AI service is local AI inference.
With local inference, an AI model runs on your own computer or another system under your control. Depending on the software and configuration, prompts and model processing can remain on that machine rather than being sent to an external AI provider.
This approach offers several potential advantages:
Greater control: You have more control over where the model runs and how the environment is configured.
Reduced external data exposure: Properly configured local inference can avoid sending prompts to a cloud AI provider.
Offline operation: Some local AI applications can operate without an internet connection after the required models and components have been installed.
Predictable infrastructure: Organisations can design their own hardware, access controls, and data-handling arrangements.
However, local AI is not automatically private or secure.
An application may still transmit telemetry, synchronise data, access external services, or store conversations in insecure locations. A locally stored model does not protect information if the computer itself is compromised.
Local AI also has practical limitations. Model quality, response speed, memory requirements, hardware capabilities, and maintenance can all affect the experience.
The right choice depends on the sensitivity of the information, the task, available infrastructure, and the security controls in place.
7. What Developers Should Do When Building AI Applications
Developers have a responsibility to consider privacy and security from the beginning of an AI application's design.
Some important practices include:
Minimise data collection: Send only the information required to complete the task.
Protect credentials: Keep API keys and secrets out of source code, prompts, logs, and client-side applications.
Apply access controls: Ensure users and AI agents can access only the resources they are authorised to use.
Review logging: Avoid unnecessarily recording sensitive prompts, responses, and uploaded content.
Validate tool actions: Do not allow an AI-generated instruction to perform sensitive operations without appropriate checks.
Secure stored data: Apply suitable encryption, retention limits, access controls, and backup policies.
Assess external providers: Review data-processing terms, retention policies, security documentation, and integration requirements.
AI security should be treated as part of application security, not as an afterthought.
8. A Practical Checklist Before You Submit a Prompt
Before sharing information with an AI assistant, take a moment to ask yourself:
Does this prompt contain personal, confidential, or commercially sensitive information?
Am I authorised to share this information with the service?
Do I understand the provider's data retention and model-training policies?
Can I remove names, credentials, account numbers, or other identifying details?
Am I using the correct account and privacy settings for this task?
Does the application use third-party integrations that may receive this information?
Would local AI or an organisation-approved environment be more appropriate?
If you cannot answer these questions confidently, pause before submitting the information.
9. The Future of AI Should Include Data Control
Artificial intelligence is becoming an important part of software development, business operations, education, research, and personal productivity. Avoiding AI altogether is neither necessary nor practical for many people.
But convenience should not come at the cost of losing control over information.
Cloud AI will continue to offer powerful capabilities and easy access to advanced models. Local AI will also become increasingly relevant as hardware improves and more capable models become available for private deployment.
The future is unlikely to belong exclusively to one approach. A sensible direction is to use cloud AI where its capabilities are valuable, local AI where data control is important, and strong security practices in both environments.
For developers and organisations, the challenge is to build systems that make these choices clear and give users meaningful control over their information.
Conclusion: Think Before You Prompt
AI can help us work faster, learn more, and solve problems that once required significant time and effort. But every interaction should begin with an understanding of what information we are sharing.
You do not need to become a cybersecurity expert to use AI responsibly. Start with a few basic habits: avoid sharing credentials, minimise sensitive information, understand privacy settings, review integrations, and choose the right environment for the task.
The most important question is not simply what AI can do with your data. It is whether you understand where your data goes and who has control over it.
Think before you prompt. Your data deserves the same care as the systems you use to protect it.
