The Hidden Data Risks of AI Chatbots: What Businesses Need to Know

Artificial intelligence chatbots like ChatGPT, Microsoft Copilot, and Google Gemini are now common in the workplace. Teams use them to summarize meetings, draft proposals, analyze data, or even brainstorm strategies. The productivity gains are real, but so are the risks. A recent study by Incogni shows that many of these platforms collect, store, and in some cases share sensitive user data with third parties .

From an MSP perspective, this raises serious questions about data governance, compliance, and security. If employees are feeding client information, internal financials, or intellectual property into these systems, where does that data go? Who has access to it? Can it ever be removed once it becomes part of an AI model?

This article will break down the risks, the business implications, and the steps organizations should take before adopting AI chatbots at scale.

What the Study Found

The Incogni team reviewed leading AI chatbots including Microsoft Copilot, Google Gemini, Meta AI, Claude, Grok (xAI), DeepSeek, and Pi.ai. Their findings were sobering:

• Extensive Data Collection: Many tools request or capture personal information such as names, phone numbers, email addresses, and precise location data.
• Data Sharing: Some vendors share this information with third parties. For example, Meta AI may disclose details like addresses and contact numbers to partners.
• Opaque Policies: Privacy disclosures are often vague, making it difficult for businesses to know exactly how data is used.
• Limited Opt-Out Options: Even when regulations like GDPR grant the “right to be forgotten,” removing data from AI training sets is technically difficult.

One striking detail: certain platforms allow uploaded documents, emails, or images to be shared beyond the immediate AI interaction. That means sensitive business files could end up exposed far more widely than intended.

Why Consent Is Not Enough

Most AI platforms rely on user consent to process data. The challenge is that consent screens are often designed for speed, not clarity. Users click “accept” without fully grasping that they may be authorizing the provider to store their prompts, share metadata, or reuse their content for training future models.

For individuals, this is a privacy problem. For businesses, it becomes a liability. Employees may think they’re simply asking the chatbot to summarize meeting notes, but if those notes include confidential client information, the business has now shared data externally without proper safeguards.

The Security Risks

1. Loss of Confidentiality

Once data leaves your controlled environment and enters a vendor’s ecosystem, you cannot guarantee who has access to it. Even if the vendor has good intentions, misconfigurations, insider threats, or future policy changes can compromise that data.

2. Regulatory Exposure

Industries bound by HIPAA, SOC 2, or GDPR face heightened risks. Feeding personal health information, customer data, or regulated financial details into a chatbot could trigger compliance violations. Regulators won’t accept “the chatbot did it” as an excuse.

3. Shadow IT Expansion

Employees are adopting AI tools informally. This means IT leaders may not even know which tools are in use, making it impossible to apply consistent security or governance policies.

4. Intellectual Property Leakage

Imagine R&D engineers pasting proprietary code or product designs into an AI chatbot to troubleshoot a problem. That content could be stored, analyzed, and potentially exposed outside the organization.

5. Advertising and Profiling

Some providers disclose that user data may be shared with advertising partners. That means conversations or prompts—possibly containing business-sensitive context—could influence targeted ads or profiling.

Business Risks in Practice

To make this more concrete, here are a few scenarios that highlight how real these risks are:

• Client Contract Drafting: A law firm uses a chatbot to review sections of a client’s contract. The terms, clauses, and client names are now part of the chatbot’s stored prompts. If that data is reused or exposed, client confidentiality is broken.
• Financial Forecasting: A CFO pastes a revenue forecast spreadsheet into a chatbot to generate visual summaries. If those figures are shared or logged by the provider, competitors or attackers could gain insight into the company’s financial position.
• Healthcare Notes: A doctor or administrator uploads case notes to streamline documentation. Unless the platform is HIPAA-certified, that single action could create a regulatory violation with severe penalties.

Why Data Removal Isn’t Simple

Regulations like GDPR and CCPA allow users to request erasure of personal data. The challenge is that once data is fed into an AI model, it can’t easily be pulled back out. Training data is baked into model weights, making selective deletion technically infeasible.

This creates a dangerous gap between legal rights and technical reality. Businesses may assume they can request removal if needed, but in practice, it’s not possible to guarantee. That’s why the best strategy is prevention: avoid exposing sensitive information to AI platforms unless you fully control the environment.

Options for Businesses

1. Policy First, Technology Second

The first step is building clear internal policies. Employees should know what types of data can and cannot be shared with AI chatbots. For example:

• Allowed: Drafting marketing copy, brainstorming blog titles.
• Prohibited: Uploading client data, financial forecasts, or medical information.

2. Vendor Audits

Before rolling out any AI platform organization-wide, review the vendor’s privacy policy and security certifications. Look for:

• Data residency commitments
• Encryption standards
• Clear statements on data sharing with third parties
• Opt-out or enterprise privacy controls

3. Deploy Enterprise-Grade Solutions

Consumer-facing chatbots are the riskiest. Instead, consider enterprise versions (such as Microsoft Copilot for Business) that offer stronger privacy guarantees, including the option not to use your data for training.

4. Monitoring and Logging

Your MSP or IT team should log AI tool usage. This provides visibility into who is using what, when, and for what purpose. Without monitoring, shadow IT will grow unchecked.

5. User Awareness Training

Like phishing, consent phishing, or ransomware, employee behavior is the weakest link. Regular training should cover:

• Risks of sharing sensitive data
• How to spot vague or overreaching consent requests
• Reporting processes for suspicious AI behavior

Leasing vs. Owning the Risk

Businesses need to decide whether to treat AI chatbots as essential infrastructure or as consumer-grade add-ons. Leasing here refers to using third-party AI with their infrastructure and rules. Owning refers to deploying private or on-prem models with full control.

• Leasing (third-party SaaS AI): Lower upfront costs, easy access, faster adoption. But risks include vendor lock-in, limited control over data, and exposure to policy changes.
• Owning (private deployments): Higher setup costs, but you control where data lives, how it is used, and whether it is ever shared externally.

Some MSPs now offer managed private AI deployments, where models run inside secure business environments. This hybrid approach balances innovation with compliance.

The Financial Impact

Beyond regulatory fines, data exposure through chatbots can lead to:

• Higher Cyber Insurance Premiums: Insurers increasingly require disclosure of AI usage. Weak controls may raise premiums or reduce coverage.
• Legal Liability: Clients could sue if their data is mishandled via AI platforms.
• Reputational Damage: Trust is fragile. Once clients know your business leaked data through a chatbot, the damage may outweigh the convenience.

MSP Role in Managing AI Risk

As MSPs, we act as both advisors and guardians. Our role is to:

• Conduct risk assessments for AI adoption.
• Provide approved AI platforms or sandboxed environments.
• Create security baselines for data handling.
• Monitor for policy violations.
• Educate teams and leadership on evolving AI risks.

AI isn’t going away, but unmanaged use will create vulnerabilities just as dangerous as unpatched servers or poor password hygiene.

Building a Practical Roadmap

1. Inventory Current Usage: Identify which teams are already using chatbots.
2. Assess Risk Level: Classify data being exposed—low (marketing), medium (internal workflows), high (client/financial).
3. Define Policy: Create clear, role-based guidelines.
4. Choose Approved Tools: Standardize on enterprise AI solutions with stronger controls.
5. Train Employees: Run awareness sessions and phishing-style simulations for AI risks.
6. Monitor and Audit: Review AI activity quarterly.

Final Thoughts

Generative AI is a powerful ally for businesses, but it is also a data exposure risk hiding in plain sight. The study makes clear that leading chatbots collect more personal and corporate data than many realize . Businesses cannot afford to be blind to these risks.

The solution isn’t to ban AI outright. It’s to adopt it with discipline: enforce strong policies, choose enterprise-grade vendors, train employees, and let your MSP help monitor usage. Done right, AI can increase efficiency without opening your organization to compliance nightmares or data leaks.

This is the new balancing act—innovation with caution, speed with governance, productivity with protection. Companies that get it right will not only stay safer, they’ll stay ahead.