Your team is almost certainly using AI already. A sales rep drops a client proposal into ChatGPT to tighten the wording. Someone in finance asks Gemini to make sense of a messy spreadsheet. A developer pastes a chunk of code into an assistant because the deadline is tomorrow.
None of that feels risky in the moment, which is exactly why it slips through. For most companies, the real generative AI security risks come from what employees hand over to these tools, often through personal accounts IT can't monitor. Netskope's Cloud and Threat Report: 2026 found that incidents of users sending sensitive data to AI apps doubled in a single year, reaching an average of 223 per month in a typical organization. Below, we look at where that risk actually sits, why a blanket ban usually backfires, and what a practical set of controls looks like.
What Are the Biggest Generative AI Security Risks for Businesses?
Most AI security risks fall into four areas: data leaking through prompts and uploads, AI use that happens outside IT's view, sharper phishing, and AI agents with more access than they need.
Sensitive Data Shared Through Prompts and Uploads
When someone asks an AI tool to summarize, rewrite, or analyze a file, the content of that file goes to an outside service. In Netskope's data, the most common type of sensitive information involved was source code (42%), followed by regulated data like personal, financial, and healthcare records (32%), and intellectual property (16%).
Samsung found this out publicly. In 2023, Bloomberg reported that engineers had uploaded sensitive internal code to ChatGPT, and the company responded by banning generative AI tools on its devices while it prepared internal alternatives.
For regulated industries, there's a compliance side too. Healthcare organizations generally need a Business Associate Agreement (BAA) with any vendor that handles patient information, so putting that data into an unapproved AI tool can create HIPAA exposure. GDPR works in a similar way for personal data of EU residents, and companies that go through SOC 2 audits can expect questions about where their data ends up. Our guide on data protection for small businesses covers the groundwork.
AI Use Outside IT's Visibility
Once an employee logs in with a personal account, the company loses the thread. There's no record of what was shared and no way to apply company policy to it. In most cases, the security team never finds out it happened. That blind spot has a name, shadow AI, and we'll get to it in a moment.
More Convincing Phishing
Attackers use the same tools. AI helps them write clean, personalized messages without the typos and odd phrasing employees were taught to look for. We covered this in more detail in The "Mirror Effect" of AI.
AI Agents With Too Much Access
Newer AI agents can open files, connect to other apps, and complete tasks for a user. Give one broad permissions, and a single manipulated instruction (a technique called prompt injection) could get it to pull or send data it was never meant to touch.
What Is Shadow AI, and Why Is It So Hard to Catch?
Shadow AI is any use of AI tools at work that happens without the company's approval or security controls, usually through personal accounts. From the outside, it looks like ordinary work: a browser tab and someone trying to get through their to-do list faster.
It's also widespread. According to Netskope, 47% of generative AI users still rely on personal AI apps at work. That's a big improvement over the 78% reported a year earlier, as more companies roll out sanctioned tools, but it still means close to half of the AI activity in a typical company may be invisible. The motive is rarely bad intent. People use what works when nobody has offered them a safe option.
Vinali Advisory has a useful piece on the governance side of this: managing shadow AI with an AI data governance framework.
Can't You Just Block AI Tools?
Partially. Blocking makes sense for apps with no business purpose or obvious red flags, and most organizations already do some of it. It gets harder with the tools people rely on every day. Cutting off ChatGPT, Gemini, or Microsoft 365 Copilot entirely tends to slow teams down, and employees who feel blocked often move the same work to a personal phone or laptop, where you see even less.
The approach that holds up better is to approve specific tools and control what data goes into them. Many companies haven't made that shift yet. Netskope found that half of organizations still have no enforceable data protection policies for generative AI apps.
How Can You Improve Generative AI Security Without Slowing Your Team Down?
Good generative AI security comes down to four moves, and none of them requires taking AI away from your team.
- Find out what's being used. A Cloud Access Security Broker (CASB) shows which AI apps are in your environment, who uses them, and whether they sign in with company or personal accounts. Think of it as turning the lights on.
- Offer an approved alternative. Business versions of AI tools come with different terms than consumer ones. OpenAI states that, by default, it doesn't use data from ChatGPT Business or Enterprise to train its models, and Microsoft says the same about prompts and responses in Microsoft 365 Copilot. One caveat: Copilot can surface anything a user already has permission to see, so review file permissions before rolling it out.
- Protect the data itself. Data Loss Prevention (DLP) catches sensitive content, such as client records, passwords, or source code, before it reaches an AI tool, then warns the user or stops the upload. A Secure Web Gateway (SWG) adds context by telling your company's ChatGPT workspace apart from someone's personal account, so you can allow one and restrict the other.
- Write rules people will actually follow. Spell out which tools are approved, what should never be pasted into them, and who to ask when something's unclear. As Vinali Advisory puts it, AI transformation is a problem of governance, and short, practical training keeps a policy from gathering dust.
If you want a second opinion on which of these you already have covered, talk to a Netvin security specialist.
How Do You Know If Your Company Is Already Exposed?
Run through this quick check:
- Nobody has published a list of approved AI tools.
- IT can't say which AI apps are in use or how often.
- Employees use personal AI logins for work tasks.
- There's no written rule on what data can go into AI.
- Your data protection controls were set up before AI apps were part of the picture.
If two or more sound familiar, start with visibility. Every other control depends on knowing what's happening first.
Should You Handle Generative AI Security In-House or With a Partner?
Mostly, it depends on bandwidth. Running these controls in-house means having people who can deploy them, tune policies so users aren't buried in false alarms, and keep up as new AI tools appear. Plenty of mid-sized companies don't have that capacity to spare.
Netvin works with organizations as a Netskope partner, handling the rollout and ongoing management of CASB, DLP, SWG, and Zero Trust access (a model that verifies every user and device before granting access, rather than trusting anything already inside the network). You don't have to start big. A quick-start package or a proof of value can show what's happening in your own environment before you commit to a larger project, with the nearshore talent of Vinali Group behind the team.
Banning AI rarely holds up for long. Knowing where your data goes does, and it's more achievable than most companies expect. To see how much AI activity is running through your environment today, schedule a conversation with a Netvin security specialist.
Disclaimer: Statistics referenced in this article come from Netskope Threat Labs' Cloud and Threat Report: 2026, based on anonymized data collected between October 2024 and October 2025. Figures reflect Netskope's findings and may vary by organization.
