What Is a Key Security Concern When Using Generative AI? 2026 Risks Explained

A key security concern when using generative AI is sensitive data exposure, meaning confidential information slips out of your hands the moment someone pastes it into an AI tool.

Most of the time it happens quietly. A sales rep wants a messy customer list reformatted, so it goes into a chatbot. A developer can’t figure out why a function fails and shares it with an AI assistant, forgetting the API key sitting on line twelve. Ask either of them afterward and they’d say they were just doing their jobs.

There’s more to worry about than leaks, of course. Teams building AI apps have prompt injection to deal with, and deepfake fraud keeps hitting finance departments. Which threat deserves your attention first depends on your job. People who simply use AI tools mostly need to watch what they type in. Builders need to think about what an attacker could make their model do, and security teams end up carrying a bit of everything.

The figures below are drawn from IBM’s 2026 Cost of a Data Breach Report, OWASP’s 2026 Top 10 for LLM Applications, NIST’s generative AI profile, and the UK National Cyber Security Centre (NCSC).

Why is data exposure a key security concern when using generative AI?

Because nobody has to break in. Any employee with a browser can hand confidential material to an outside AI service in seconds, and after that the provider’s own retention, access, and training rules apply.

Does that mean every prompt trains a public model? No. It depends on the provider, the kind of account, and which settings someone switched on or off. The same paragraph could be handled one way on a free personal account and another way entirely under an enterprise contract.

What you definitely give up is oversight. You can’t easily tell how long the data stays on the provider’s servers or who reads it there. And if that provider gets breached next year, your data goes with everything else.

OWASP ranks this second in its Top 10 for LLM Applications 2026, released in August 2026, with prompt injection in first place. That list targets teams building on language models rather than office staff using chatbots. Still, the end result looks much the same either way.

What the Samsung source code leak shows

Samsung found out the hard way in April 2023, when engineers accidentally uploaded internal source code to ChatGPT. The company followed up with a memo, first reported by Bloomberg, banning generative AI tools on company devices. CSO Online’s report on the Samsung memo describes Samsung’s concern about sensitive company data ending up on outside servers.

None of those engineers meant any harm. They grabbed a handy tool, and no policy told them what it could safely take. Samsung said at the time that the ban was temporary while it worked on a safer setup.

What information should you never paste into an AI tool?

Short version: anything your tool hasn’t been cleared to handle. For a tool your organization hasn’t vetted, that rules out:

• Passwords, API keys, recovery codes, and authentication tokens
• Customer personal information and confidential records
• Unreleased financial results, contracts, and business plans
• Proprietary source code and security configurations
• Regulated personal data, such as health or financial records, unless the tool and workflow are approved for it

Some enterprise setups are designed to process sensitive data under defined controls, so treat the list as a default rather than a law. What holds everywhere is knowing what your particular tool is authorized to receive.

Privacy law can apply too once personal data lands with a third-party AI service. Whether it does depends on where you operate and what the data is. Run that past legal or compliance before calling the workflow safe.

How do you check an AI tool’s data handling before using it?

You don’t need a security background for this. Pull up the tool’s data terms and look for answers to five questions:

1. Does the provider train on your input by default? If yes, is there a way to turn it off?
2. How long are prompts and uploaded files kept? Look for a stated retention period, a deletion option, or zero-retention terms, which some providers negotiate with qualifying business or API customers.
3. Can the provider’s staff read your conversations? Certain services allow human review for safety or quality checks.
4. Is there a data processing agreement? Organizations that handle personal data usually need one before rolling a tool out.
5. Can an administrator see and manage usage? Single sign-on, audit logs, and admin settings are usually what set a business tool apart from a personal account.

Vague or missing answers are a signal in themselves. A tool like that should be limited to public information, and sorting tools this way prevents more accidental leaks than most people expect.

Why does shadow AI increase data exposure risk?

Shadow AI covers any AI tool staff use without the organization knowing about it or signing off. You can’t protect data going into a tool you’ve never heard of. Pasting into a browser tab doesn’t create an attachment or an outbound email either, so older security controls often miss it completely.

And it’s spreading. According to IBM’s breakdown of its 2026 breach data, security incidents involving unapproved AI rose from 20% to 43% of breached organizations in a year. Nearly half of those incidents (49%) led to data loss or compromise, and 21% ended in a regulatory fine. IBM puts the average cost of a shadow AI breach at $5.39 million, roughly $400,000 more than a typical one.

Governance lags behind. IBM’s analysts note that most breached organizations either lacked AI governance or were still building it, and only 19% reported meaningful coordination between their governance and security teams.

Do bans on AI tools work?

Rarely, on their own. If AI makes someone’s work faster, they’ll keep using it, and a ban mostly pushes that use onto personal accounts and personal phones, where nobody has any visibility. A sanctioned tool with simple data rules tends to hold up better.

What is prompt injection, and can it be stopped?

Prompt injection is any input crafted to make a model ignore its intended instructions. The version that worries security teams most is indirect: someone hides instructions in content the AI reads, such as a web page, an email, or a shared document, and the model follows them instead of the user’s. With a chatbot that only answers questions, you might get a strange reply. With an AI that can send emails or edit records, you could get a costly action.

Nobody has found a fix that works across every application. The NCSC lays out the reason in its December 2025 post, prompt injection is not SQL injection. SQL injection was eventually tamed with parameterized queries, which work because databases keep data and commands separate. Language models have no such separation, and the NCSC warns prompt injection may never be mitigated the same way. Its advice is to lower both the odds and the impact, and to be wary of any vendor who says they can block it outright.

When these attacks land, they’re expensive. IBM’s analysis of its 2026 breach findings puts the average prompt injection incident at $5.89 million. Model inversion, where attackers reconstruct sensitive training data from a model’s answers, cost even more at $6.07 million. IBM ranks those two as the most expensive AI-related incident types it measured, though both numbers are averages across IBM’s breach sample rather than a fixed price per attack.

So what can builders actually do? Plan for containment. Expect an injected instruction to get through now and then, and keep the model’s reach small enough that it can’t do much when one does.

Why did OWASP move Excessive Agency to third place in 2026?

Excessive Agency is OWASP’s term for an AI system holding more tools, permissions, or freedom than its job requires. It jumped from sixth to third in 2026, largely because teams now connect models to live systems through AI agents that act. An odd chatbot reply wastes a few minutes. An agent with write access to production can do far more damage.

Eight of OWASP’s ten entries changed position this year. Prompt injection held the top spot, sensitive information disclosure stayed at number two, and improper output handling dropped from fifth to tenth. Supply chain exposure, which covers third-party models, plug-ins, and datasets, sits at number four.

Put prompt injection and excessive agency together and you get the worst outcomes. Someone plants instructions in a document, and an agent with too much access carries them out. Narrow permissions take away most of that danger. Payments, deletions, code merges, and anything else you can’t undo should still wait for a person to approve them.

Identity plays a bigger part than many teams realize. According to IBM, fewer than half of organizations actively secure non-human identities, meaning the accounts and credentials agents use to reach other systems. Giving each agent its own agent identity, with short-lived, task-scoped access, lets you trace what it did and cut it off fast.

Plenty of AI breaches never touch the model at all. In IBM’s 2026 study, the two most common causes of attacks on AI models and applications were compromised APIs, applications, or plug-ins, and cloud misconfigurations affecting AI workloads, each found in 27% of those breaches. The plumbing around an AI system needs as much attention as the model inside it.

How do criminals use generative AI against organizations?

Here the threat comes from outside. Your staff don’t have to paste a thing. Criminals use generative AI to impersonate real people, write phishing emails that read convincingly, and build malware faster than before.

IBM’s 2026 study found AI behind one in four malicious breaches, a 56% jump in a single year. Those breaches averaged about $6 million, around $1 million over the $4.99 million global figure. IBM’s UK release on the 2026 findings gives the breakdown: deepfake impersonation made up 45% of these attacks, AI-enabled malware 19%, and AI-generated phishing 17%.

The best defense is a simple habit. Whenever a request involves money, credentials, or access, confirm it through a channel you already trust, like a phone number you looked up yourself rather than one in the message. That applies even when the voice on the call sounds exactly like your CEO.

Which generative AI security controls fit each role?

A chatbot policy does nothing for an AI agent, and agent permissions won’t stop a fake CFO from phoning accounts payable. Each group needs safeguards built around its own weak spot.

Most organizations have a long way to go. IBM found that only 40% of the breached companies it studied used access controls on AI models and data. Among those hit by an attack on their own AI models or applications, 92% lacked proper AI access controls.

That last row scales with the stakes. NIST’s Generative AI Profile (NIST AI 600-1), published in July 2024, lists confabulation, meaning confident but false output, among the risks specific to generative AI. A brainstormed list of blog ideas can survive a quick skim. A payment, an access change, or a claim you’re about to publish needs someone to check it first.

Teams that keep a human approver on consequential steps still get the drafting speed, and the final call stays with a person.

Protect the data first, then limit what AI can reach

Begin with everyday use, since that’s where most exposure starts. Decide which tools staff may use, read their data terms, and hand people a short list of what never goes in.

Then turn to what your AI can reach. A drafting assistant has limited room to cause trouble. An agent wired into email, payments, or production systems needs much tighter limits, and for some tasks risk can veto automation altogether.

Expect these numbers to date quickly. Recheck them after IBM’s next Cost of a Data Breach report (it usually lands in late July), after any OWASP revision, and whenever a major AI provider changes its data-retention defaults.

Frequently asked questions about generative AI security

Q. What is a key security concern when using generative AI?

Sensitive data exposure. Credentials, customer records, and proprietary code shared with an unvetted AI tool can be stored, reviewed, or leaked by a third party.

Q. Is it safe to use free AI chatbots at work?

Only for material you’d be comfortable making public, unless your employer has signed off on the tool. A free personal account gives your organization no contract and no visibility into what was shared.

Q. What’s the difference between prompt injection and jailbreaking?

Jailbreaking is a user deliberately trying to talk a model past its safety rules. OWASP files it under prompt injection as a subtype. The difference is intent and source: a jailbreaker attacks the model’s guardrails head-on, while the most damaging injections arrive hidden in third-party content.

Q. How is shadow AI different from shadow IT?

Shadow IT covers any software or service used without approval. Shadow AI is the AI subset, and what sets it apart is what leaves the building: staff hand over the content itself, often in bulk.

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Disclaimer: This article is for informational and educational purposes only and does not constitute professional cybersecurity, legal, or compliance advice. AI security threats, statistics, and best practices may change over time. Readers should verify current guidance and consult qualified security professionals before making security-related decisions.