Here is a scenario that is already happening inside companies.
A marketing team member copies a spreadsheet of customer names, buying history, and email addresses into a public AI tool to help write a campaign. An engineer uploads proprietary code to get help debugging an issue. A finance person pastes internal revenue projections into a chatbot to summarize the story for leadership.
Nobody is trying to be reckless. They are trying to move faster.
Then, months later, IT or compliance discovers the activity during an audit. By that point, the data has already left the building, and the company is left trying to answer uncomfortable questions.
Where did the data go? Was it retained? Was it used to improve a model? Did it violate a customer agreement, compliance requirement, or internal policy?
That is the real workplace AI risk. It is not that AI is bad. It is that employees are already using it faster than most organizations have learned how to govern it.
The Shadow AI Problem
Most leaders have heard of “shadow IT.” That is when employees use software, cloud storage, messaging apps, or other tools without formal approval from IT.
Now we have shadow AI.
Shadow AI happens when employees use AI tools without security review, legal review, compliance approval, or technical controls. The tool might be ChatGPT, Claude, Gemini, a browser extension, a meeting note-taker, a code assistant, or a niche AI app that solves a very specific problem.
The reason this happens is simple: AI is useful.
Employees do not wake up thinking, “How can I create a data exposure problem today?” They think, “I have three hours of work and only 45 minutes to do it.” AI helps them summarize documents, draft emails, clean up code, analyze spreadsheets, and brainstorm ideas.
Research has shown that workplace AI adoption has moved quickly, often ahead of formal company approval. Salesforce reported that more than a quarter of workers used generative AI at work in 2023, and more than half of those users did so without formal employer approval. Slack’s 2025 Workforce Index also reported that AI adoption among workers had risen sharply, with 60% of workers using AI.
From an operations perspective, I understand why employees do it. If a tool helps them save time, they will use it.
But from a finance, risk, and leadership perspective, that convenience can create real exposure. The company may be accountable for data it did not even know was being shared.
What Actually Gets Exposed
When people hear “AI security risk,” they often picture hackers or science fiction scenarios. The more common risk is much simpler.
Employees paste sensitive information into tools that were never approved to receive it.
The first category is customer information. This can include names, email addresses, phone numbers, purchase history, support tickets, contracts, health information, location data, or private business details.
The second category is proprietary code and intellectual property. Developers may use public AI tools to debug source code, generate functions, explain errors, or review architecture.
The third category is financial data. This can include budgets, forecasts, debt schedules, pricing models, payroll assumptions, margin analysis, and acquisition planning.
The fourth category is strategy. Employees may ask AI to summarize board materials, rewrite investor updates, evaluate competitors, or draft plans based on confidential internal direction.
The fifth category is employee and HR information. That might include performance reviews, compensation data, disciplinary notes, hiring plans, resumes, or sensitive workplace issues.
Each of these categories matters because the exposure is not always obvious when it happens.
For example, a finance employee might paste a revenue forecast into an AI tool and remove the company name. That still may not be safe. The data itself can be sensitive even if the obvious identifiers are removed.
In my opinion, this is where companies often underestimate the problem. They focus on whether someone shared a password or Social Security number, but business data need not be personally identifiable to be damaging.
A customer list is valuable. A pricing model is valuable. A source code snippet is valuable. A strategic roadmap is valuable.
How Public AI Tools Use Your Data
This is where things can get confusing for everyday users.
Different AI tools have varying rules for consumer, business, and enterprise accounts, APIs, temporary chats, retention, and training. The average employee is not reading the terms of service before pasting it into a spreadsheet.
For example, OpenAI says that for its business products, including ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, and the API Platform, it does not train models on business data by default. OpenAI also says users own and control their inputs and outputs, where allowed by law.
That is very different from many individual or consumer usage scenarios. OpenAI’s help materials explain that when people use individual services such as ChatGPT and Codex, their content may be used to train models unless they opt out through available controls.
Anthropic makes a similar distinction. Its privacy materials state that commercial products such as Claude for Work, Anthropic API, and Claude Gov are not used by default for model training. But its consumer terms changed in 2025 to give users a choice about whether Claude data may be used to improve models.
Google’s Gemini privacy materials also make clear that Gemini Apps can save and use information from connected apps to provide and improve Gemini Apps, depending on settings and product context.
The point is not that one vendor is good and another is bad.
The point is that “we use AI” is not a data governance strategy. Companies need to know which AI tools are being used, under what account type, with what contractual protections, and with what data retention and training rules.
Compliance and Legal Exposure
The compliance issue is where shadow AI can turn from a bad habit into a serious business problem.
If your company handles healthcare data, HIPAA may apply. If you handle payment card information, PCI DSS may apply. If you work with EU residents, GDPR may apply. If your company promises customers certain security controls, SOC 2 commitments, or customer contracts may apply.
A public AI tool may not be approved for any of that data.
Think about a healthcare employee asking an AI tool to summarize patient notes. Or a support team pasting customer payment information into a chatbot to help write a response. Or a software company using public AI tools in a way that conflicts with its own security questionnaire responses.
That creates a painful gap between what the company says it does and what employees are actually doing.
This is why AI governance needs to be part of security and compliance, not just innovation. The NIST AI Risk Management Framework was created to help organizations manage AI risks to individuals, organizations, and society. It is a useful starting point because it treats AI as requiring governance, measurement, and accountability.
This also connects directly to Seisan’s work around secure software development, enterprise systems, and long-term application support. Internal link suggestion: [Seisan Software Development Services] or [Seisan Security & Compliance Capabilities].
The IP Ownership Problem
There is another risk that does not get enough attention: intellectual property ownership.
If an employee uses AI to generate code, content, images, product concepts, or design assets, who owns the final work? Is it protectable? Was it influenced by copyrighted or licensed material? Can the company safely use it in a commercial product?
The answer is not always clean.
For example, if a developer asks AI to generate code for a core product feature, the company may need to understand whether that code can be owned, licensed, reused, or defended. If a marketing team uses AI to create brand copy or campaign concepts, the company should still review the work for originality, accuracy, and legal risk.
There is also the risk of accidental leakage in the other direction. An employee may paste proprietary code into a public tool and then receive generated code back. Now the company has two questions: did we expose our own IP, and can we trust the origin of what came back?
This is especially important for software companies, product companies, and organizations building competitive technology.
AI can absolutely help accelerate development. I believe it will become a normal part of how modern teams work. But it needs guardrails, review, and approved workflows.
What Companies Should Do Instead
The wrong answer is to simply say, “No one is allowed to use AI.”
That may sound safe in a policy document, but in the real world, employees will often work around it. If a tool saves them hours, they will find a way to use it unless the company gives them a safer alternative.
The better answer is practical governance.
First, companies should approve specific AI tools for specific use cases. Not every AI tool is appropriate for every type of data.
Second, they should use enterprise-grade AI agreements where possible. Business and enterprise products often provide stronger contractual protections, clearer data controls, admin visibility, and better security commitments than individual consumer accounts.
Third, companies should create an AI usage policy that employees can actually understand. A 14-page legal document that no one reads will not solve the problem.
Fourth, companies should train employees with real examples. Show them what they can paste into AI, what they cannot paste, and what requires approval.
Fifth, companies should consider private AI deployments for sensitive use cases. That is where solutions like Seisan’s Delta Shield AI become important. A private or controlled AI environment can help employees gain the productivity benefits of AI without exposing sensitive data to unmanaged public tools.
Finally, companies should apply technical controls where appropriate. That may include browser controls, data loss prevention tools, approved integrations, logging, access controls, and blocking high-risk AI destinations.
The goal is not security theater. The goal is to make the secure path easier than the risky path.
Secure AI Use Before It’s Too Late
The most important thing leaders need to understand is this: employees are probably already using AI.
Waiting for the perfect AI policy means the company keeps operating in the dark. That is not governance. That is hope.
A practical AI security plan should answer a few basic questions.
Which tools are approved? What data can employees use? What data is prohibited? Who reviews new AI tools? What happens when someone wants to use AI with customer data, source code, financial data, or regulated information?
Companies should also pay attention to AI-specific security risks. The OWASP Top 10 for Large Language Model Applications is a helpful resource because it identifies issues like prompt injection, sensitive information disclosure, insecure output handling, and excessive agency.
Those risks may sound technical, but the business lesson is straightforward. AI systems can expose data, make mistakes, follow malicious instructions, or take actions users did not fully intend.
That does not mean companies should avoid AI. It means they should manage it like any other powerful business technology.
Get AI Security Right
AI in the workplace is inevitable. Unsecured AI is not.
The answer is not to block every tool and hope employees stop using them. The answer is to give teams secure, approved, practical options that help them do their jobs better without creating unnecessary risk.
At Seisan, we see AI as a major opportunity for companies that approach it with the right balance of productivity, security, and governance.
That is exactly where Delta Shield AI and Seisan’s security-minded consulting approach fit. We help organizations think through how to use AI in the real world: safely, privately, and in a way employees will actually adopt.
Ready to get AI security right? Contact us today.
