Before you upload that document: A guide to AI privacy

AI tools are everywhere now, and for good reason. It’s in our email composers, our Google documents, in our everyday work systems and tools. Undoubtedly, AI can save time, reduce manual effort and help organisations manage increasingly complex workflows. 

But as AI becomes embedded in our everyday work, a quiet but persistent question sits behind every prompt: where does this data actually go?

For awards program managers handling sensitive entries, collecting information such as commercial strategies, confidential innovations, personal achievements and other important data, AI security is fast becoming a very relevant concern.

Why AI and data privacy deserve your attention

AI and privacy concerns have become the top challenge for organisations worldwide for the second consecutive year, with 70% of companies identifying AI as an important or very important privacy concern.

When you paste a document or data into an AI tool or upload a submission for review, you may be feeding that content into a system trained to learn from user input. Depending on the platform’s data policy, your content could be retained, used to improve the model or processed on third-party servers outside your region.

The risks are real. Generative AI tools can sometimes share data across platforms or with third parties without the user’s consent. Alarmingly, this could include proprietary business data or personal information, which might be shared inadvertently. 

For awards programs, this matters acutely. Entrants trust you with their best work. Judges trust you with their evaluations. That trust is worth protecting.

How does generative AI handle privacy and data security?

The quick answer: it depends entirely on the platform.

Some consumer AI tools, including widely used chatbots, may use inputs for model training by default. Others offer enterprise tiers with stronger protections. 

Generative AI systems present specific risks when it comes to personally identifiable information (PII): if PII is part of a large language model, it may be possible for generative AI to expose that PII in its output.

The key questions to ask of any AI platform you use are:

• Does my data leave the platform’s environment?
• Is it used to train models?
• Where are servers located, and which data protection laws apply?
• Can I request deletion of my data?

Leading approaches to AI privacy governance include encryption at every stage, data minimisation (only collecting what’s necessary), anonymisation before training and clear deletion policies. 

AI systems should maximise security at every stage. Encryption is particularly important because it secures sensitive data from the outset, building a foundation that is easier to maintain than retrofitting security later. 

What makes an AI platform privacy-first?

The best AI governance solutions for data privacy protection share a few things in common:

• They keep data within a controlled environment
• They are transparent about how data flows
• They give administrators meaningful control over what’s switched on

This is precisely the approach Award Force has taken. Award Force AI tools run entirely within Award Force’s own secure virtual private cloud (VPC). Data never leaves the platform’s environment. There is no third-party processing, no connection to the public internet and no use of submission data to train external models. Program managers can choose whether to enable AI tools at all, and which AI agent to trust.

It is a meaningful distinction from many platforms, where AI features are active by default and data handling is governed by terms buried in documentation.

Practical steps for your program

You do not need to avoid AI entirely, though you might choose to do so. But it’s also possible to use AI responsibly. Here is where to start:

Establish a clear AI policy.

Decide which tools are approved for which tasks. Make the distinction between tools that are safe for sensitive data and those that are not. Award Force’s approach to responsible, human-centred AI offers a useful framework: AI should assist people in making better decisions, not make decisions for them.

Apply data minimisation.

Before uploading anything, ask whether the AI tool needs the full document. Strip out unnecessary personal information where possible.

Be transparent with entrants.

If AI tools are used in any part of your review or judging process, say so. Transparency builds the trust that awards programs depend on.

Audit your tools regularly.

Data policies change. Set a reminder to review the privacy documentation of every AI tool your team uses at least once a year.

AI and data privacy don’t have to exist in opposition. With the right platform choices and clear internal governance, you can use AI to work more efficiently while maintaining the integrity and the trust that make recognition programs meaningful.