AI Data Leaks: How They Happen, and How the Right DLP Tool Can Help

You don’t have to look hard to find a scary headline about AI hacks. But as terrifyingly real as they are, how they happen is a little less thrilling.

In fact, AI data leaks mostly occur through normal, authorized, daily work. No stolen credentials, no zero-day exploit, no attacker lurking in your network: just an employee pasting sensitive customer data into a chatbot to save twenty minutes.

As boring as this may be, it’s actually very good news. Most AI data leaks aren't hacks, but habits. And habits can be changed – especially with the right tools.

In this article, we'll cover what defines an AI data leak, how they happen, why they're happening more often, and how the right combination of policy and DLP (data loss prevention) tools can stop them before it’s too late.

What are AI data leaks, and how do they happen?

An AI data leak is any exposure of sensitive data through the use, prompting, or training of AI tools. It's distinct from a traditional data breach in one very important way: a breach usually involves an external attacker exploiting a vulnerability, while an AI data leak usually involves an authorized user performing a legitimate task in the course of their work.

This distinction matters. Traditional security tools are built to catch intruders and look for anomalies: strange logins, unusual traffic, unauthorized access attempts. An employee pasting code into a browser-based AI assistant doesn't trip any of those wires. That’s because it looks exactly like normal traffic in everyday work.

This is why AI data leaks are one of the fastest-growing categories of security incidents. In fact, it even has its own dedicated OWASP Top Ten LLM list, with coders being encouraged to keep a close eye on the many ways AI tools can leak sensitive information. This isn’t surprising given a recent study revealed 77% of employees have pasted company data into AI tools. Behavior this widespread isn’t a fringe risk; it's a mainstream one.

Real-life AI data leak stories

A few incidents have become the go-to (don’t do) examples in the cybersecurity space, mostly because they're relatable rather than rare.

The classic is Samsung's 2023 scare, when engineers reportedly pasted proprietary source code into ChatGPT to help debug it, arguably handing that code to a third party outside the company's control.

In a related story also from 2023, Amazon reportedly urged employees not to paste company secrets into AI, after some were found feeding sensitive company information into generative AI tools.

A case from September 2024 illustrates the risks of meeting transcription software leaking company secrets, when AI researcher Alex Bilzerian received a transcript of hours of private conversations with a Venture Capital firm he’d met with, even after his meeting had ended.

Then, the 2023 ChatGPT outage separately exposed some users' chat titles to other accounts thanks to a system vulnerability – a potent reminder that even the AI vendor's own infrastructure can become a leak vector, even if only temporarily.

These cases tend to follow the same pattern: there is a real business need, an employee uses the fastest available AI tool, and the security team is never in the loop. This is shadow IT under a different guise. Most people involved aren't trying to cause harm; they're trying to get their job done quickly, and the risk of data leakage simply doesn't occur to them at the moment.

Unfortunately, this is having a huge impact: one recent study showed a 60% increase in company exposure to data leaks via AI tools in just the first half of 2026.

The most common forms of AI data leakage

So, we’ve covered the general idea of what an AI data leak is, and how it can happen. But not all leaks are the same – and the distinctions matter.

Here are the six most common forms:

1. Prompt-based leakage: Employees pasting source code, financials, contracts, or customer records into public AI tools to solve complex problems or get a faster answer.
2. Shadow AI: Unsanctioned tools and AI-powered browser extensions spread across a company without IT ever approving or knowing about them. It's the same dynamic as shadow IT: more broadly, the tools employees adopt on their own are usually the ones that feel fastest, not safest.
3. Training data memorization: Large language models can memorize fragments of the data they were trained on, and have been shown in limited conditions to reproduce them later to a completely different user – though AI companies are doing their best to put safeguards in place to prevent this.
4. Retrieval and RAG leakage: If a retrieval-augmented generation system doesn't carry over the original document-level permissions, a junior employee querying the company knowledge base could end up with an answer built from executive-only files.
5. Embedded AI features: Email clients, note-taking apps, CRMs, and SaaS tools now have AI baked in, often by default. Each one is a new place sensitive data can flow without anyone explicitly deciding to send it there – especially if data settings are ignored.
6. Prompt-injection leakage: As AI agents gain the ability to read documents and act on internal systems, an attacker can bury a malicious instruction inside ordinary content: an email, a shared file, an image, or a support ticket. The AI agent treats that hidden text as a command and can hand over sensitive data without an employee clicking anything.

Why AI data leaks are exploding right now

There are a number of forces converging to make this problem grow bigger every quarter. Let’s identify them.

• Speed: New AI tools are launching faster than any IT team can realistically vet them, and for some employees the temptation of getting work done faster is just too great.
• Private vs public: Most employees don't think about the difference between a personal AI account and an enterprise one. The two can have very different data retention and training policies, even if from the user's seat they look identical.
• Ease of adoption: Many consumer versions of AI tools are simply easier to adopt. With slick UX, being free or cheap, and solving a real problem right now, it’s easy to understand why they are so readily used.
• Time pressure: Deadlines and a “Just get it done” mentality mean caution tends to lose out over the impulse to use AI tools.
• Complexity: Agentic AI and MCP-style integrations are expanding the attack surface further, and making it more challenging for some users to know how their data is being handled.

For a more in-depth analysis, check out our exploration of the 5 biggest AI threats security teams are facing right now.

The risks: why these leaks are more than just an awkward headline

It's tempting to file AI data leaks under "embarrassing, but not dangerous." But that would be a mistake.

By failing to take charge of the way your teams use AI tools, you can risk the following:

• Loss of intellectual property: Source code and IP pasted into a public tool can end up outside your company's control permanently.
• Regulatory exposure: GDPR, HIPAA, SOC 2, and newer AI-specific rules like the EU AI Act all treat AI tools as third-party data processors, so the same obligations apply as with any other vendor handling sensitive data.
• Competitive advantage: Strategy documents, pricing, and roadmaps leaked into an AI tool can hand a competitor real insight.
• Detection challenges: Tracking data leaks from AI tools is harder than with a conventional breach, since the traffic looks like ordinary work.
• Fueling hacks: Attackers are now probing AI tools and their outputs for sensitive information they can use in follow-on attacks.

These risks show why it’s so crucial to take control. Here’s how.

3 steps to taking control of AI data leaks

1. Step 1: Get visibility. You can't protect what you can't see. Build an inventory of sanctioned and unsanctioned AI tools in use. Audit browser extensions and OAuth grants, a common blind spot. And use DLP or CASB tooling to see, concretely, what's actually being pasted into AI tools today – not just what you assume is happening.
2. Step 2: Set clear ground rules. Publish an AI acceptable use policy specific enough to be actionable: which tools are approved, what data is off-limits, and who to ask about a new tool. Build a fast-track approval process, because if the official route takes six weeks, people will skip it. Make sure to classify sensitive data before it can reach an AI tool at all, so the riskiest content is flagged automatically.
3. Step 3: Give people what they actually need. The single most effective move is offering an approved, secure AI alternative before employees go build their own workaround. Pair policy with real-time coaching, giving a quick nudge at the moment of risk rather than an annual training module nobody remembers by March. Make the secure path the easy one.

Speaking of taking control, check out our free list of 15 key cybersecurity practices for every employee

Your greatest defense: A DLP tool built for the AI era

Legacy, network-based DLP was built for a world of on-premise file servers and email gateways. It largely misses AI leakage, because so much of it happens over encrypted traffic, inside a browser tab, or through an app that network inspection is simply too old-school to see.

AI-aware DLP has to work differently. It needs to cover clipboard activity, not just file transfers. It needs to inspect uploads and prompts at the browser level, where the actual paste happens. And, it needs to do all of this without turning into a blanket ban that just pushes employees toward even less visible workarounds.

This is exactly the gap Riot's Sonar tool is built to close. Sonar gives you visibility into what's actually being shared and pasted into AI and collaboration tools, so you can catch a risky paste before it becomes an incident, rather than finding out about it in a post-mortem. It’s also designed to be minimally intrusive for users – if someone is using an AI tool in an approved manner, they can just say so.

AI data leaks need to be on your radar, and your best protection is to get ahead of it rather than clean up a mess afterward. Book a demo and see how Sonar can help.

FAQ

1. What is an AI data leak? An AI data leak is the exposure of sensitive information through the use, prompting, or training of an AI tool. Unlike a breach, it typically involves an authorized user in a legitimate workflow rather than an external attacker.
2. How is an AI data leak different from a data breach? A data breach involves unauthorized access by an external attacker exploiting a vulnerability or stolen credentials. An AI data leak happens through normal, sanctioned use of AI tools by employees performing a legitimate task in the course of their work, which is exactly what makes it harder to detect.
3. What's the most common cause of AI data leaks? Prompt-based leakage: employees pasting sensitive information such as source code, financial data, or customer records directly into AI tools, often public or unmanaged ones, to get a task done faster.
4. What is shadow AI, and how does it relate to AI data leaks? Shadow AI refers to AI tools and browser extensions employees adopt without IT's knowledge or approval. Because security teams don't know these tools are in use, they can't monitor or control the data flowing into them, making shadow AI one of the biggest drivers of these leaks.
5. How can a company prevent AI data leaks? Through a combination of visibility (knowing which AI tools are in use), policy (clear rules on what can and can't be shared), and technology (AI-aware DLP that can catch risky pastes and uploads at the point of action) paired with an approved AI tool that gives employees a safe, fast alternative to shadow AI.
6. Does DLP actually stop AI data leaks? Modern, AI-aware DLP does, but only if it's built for how people actually use AI tools today. That means monitoring clipboard activity and browser-level prompts, not just file transfers and email attachments, which is where legacy DLP tools fall short.