AI and Data Exposure: The Hidden Risk No One Wants to Talk About
- Brett Azzopardi
- Jul 1
- 3 min read
Artificial Intelligence (AI) has officially left the lab. From marketing teams using generative tools to draft copy, to finance departments summarising reports in seconds, and developers getting coding suggestions in real time — AI is everywhere. And it’s incredible.
But with great power comes… well, not always great caution.
While organisations are racing to adopt AI to boost productivity and reduce costs, a critical risk is quietly growing in the background: data exposure.
What Does “Data Exposure” Mean in the Age of AI?
It’s not a cyberattack. It’s not a phishing email. It’s something far more mundane — and far more dangerous because of it.
It’s an employee pasting customer records into ChatGPT or Copilot to get help writing an email. It’s a developer feeding proprietary code into Claude to debug an issue. It’s a team uploading internal documents into an AI summariser without reading the fine print.
Each of these actions can send sensitive, regulated, or confidential data outside the organisation's control. And once it’s out, there’s no pulling it back.
The Most Common Ways AI Tools Leak Data
Public AI tools with unclear data policies
Many free or low-cost AI services store your inputs to improve their models — meaning anything you upload could be retained or even learned from.
No internal usage policy
Employees are often left to figure out “what’s okay” on their own, leading to inconsistent and risky behaviours.
Shadow AI adoption
Just like shadow IT, teams start using AI tools without telling IT or security, leaving a blind spot in your data protection strategy.
Third-party integrations
AI is increasingly embedded in SaaS apps. These integrations can move data across systems with minimal visibility — or control — from the user.
Real Consequences, Not Hypotheticals
In 2023, a group of Samsung employees inadvertently leaked confidential source code by using ChatGPT to fix bugs. The company responded by banning external AI tools altogether.
They’re not alone. Several major banks and legal firms have followed suit, temporarily blocking generative AI while they work out how to use it responsibly.
The risks aren’t just reputational — they’re legal. Depending on your industry, exposure of personal, financial, or health-related data could trigger regulatory breaches under GDPR, CCPA, HIPAA, or Australia’s Privacy Act.
What You Can Do Now to Protect Your Business
If AI is here to stay (and it is), you need a proactive plan to ensure it doesn’t become your next data breach.
1. Set clear guidelines for AI use
Define what can and can’t be entered into AI tools. Make it simple, practical, and easy to follow.
2. Use enterprise-grade AI where possible
Microsoft, Google, and others are rolling out secure, company-specific AI tools that don’t use your data to train public models.
3. Provide training and awareness
Most data exposure isn’t malicious — it’s unintentional. Help your teams understand the risk and how to avoid it.
4. Involve IT, Security, and Legal early
AI governance isn’t just about risk; it’s about enablement. Bring the right stakeholders together to set a path forward.
5. Monitor and audit usage
Tools like Data Loss Prevention (DLP) systems and CASBs can help you keep an eye on data flowing to external services.
The Bottom Line
AI isn’t going away — and nor should it. The benefits are too significant to ignore. But just like every technology wave before it, the key to long-term success lies in responsible adoption.
Treat AI like any other business tool: with purpose, policy, and protection.
Because while AI might be artificial, the risks to your data are very, very real.
Comments