[Disclaimer: I am a business consultant, not an attorney. The following information is for educational purposes and based on my interpretation of current business risks and public regulations. For specific legal advice regarding the VCDPA or other statutes, please consult a qualified Virginia attorney.]
Picture this scenario: You’re a business owner in Fairfax County. It’s Tuesday morning, and you just realized that six months of client intake notes—including sensitive financial details—were fed into a free AI tool by well-meaning staff trying to speed up their workflow.
The data wasn’t just processed; it was ingested. It’s now potentially part of a global training dataset. Forever.
In most states, this is just embarrassing. In Virginia, it could be a significant business risk.
Virginia is not the Wild West when it comes to data. We have the Virginia Consumer Data Protection Act (VCDPA) and distinct statutes protecting individual “likeness.” Small businesses here face a unique paradox: you likely don’t have a compliance team or a legal department on retainer. But you do have employees eager to adopt AI—often unaware that they might be navigating complex regulatory waters.
In my SMB AI guide, I wrote about why 95% of AI initiatives fail. Privacy disasters are a big reason.
Here are the seven AI privacy risks catching Virginia businesses off guard, and the practical steps you can take to manage them.
1. Training Data Exposure & “Sensitive Data”
The Business Risk: When you use free AI tools (like the basic versions of ChatGPT, Gemini, or Claude), your inputs often train their models. That prompt your team typed? It helps the model learn—and could theoretically resurface in someone else’s response.
This isn’t hypothetical paranoia. In 2023, Samsung engineers leaked semiconductor source code to ChatGPT within 20 days of the company allowing its use. Three separate incidents: proprietary chip designs, defect-identification code, and internal meeting transcripts. Because they used the free tier, all of it became training data.
AI companies are now facing a wave of lawsuits over training data—from authors to music publishers to the New York Times. If the data they scraped is legally contested, what about the data you’re feeding them voluntarily?
The Virginia Context: Virginia’s VCDPA defines “sensitive data” very specifically—including things like precise geolocation and biometric data. If your staff pastes this type of information into a public AI model without careful protocols, you could be stepping into “sensitive data” processing territory, which generally requires strict consumer consent.
Practical Steps:
- Check Your Tier: Consider upgrading to paid Enterprise tiers that typically exclude training on your data.
- Classify Your Data: Create a clear internal rule: “Sensitive data” (as defined by local regulations) should generally never enter a cloud AI.
- Configure Opt-outs: If you use consumer tools, look for settings to disable “Chat History & Training.”
I covered this in more depth in my post on navigating privacy in AI chatbots—it’s worth reading if you want to understand how different services handle your data.
2. Shadow AI & Automated “Profiling”
The Business Risk: Microsoft’s 2024 Work Trend Index found that 75% of knowledge workers use AI at work—and 46% started using it less than six months ago. Many use it without their boss knowing (“Shadow AI”). They aren’t being malicious; they’re trying to be efficient.
The Virginia Context: The risk rises if your employees use AI to help make decisions about hiring, housing, or creditworthiness. In the VCDPA, this is often referred to as “Profiling.” Virginia consumers typically have the right to opt out of profiling that produces “significant effects.” If you don’t know your staff is using AI for these decisions, you can’t offer that required choice.
Practical Steps:
- Create a Policy: Write a simple one-pager: “Here’s what you can use, here’s what you can’t, here’s why.”
- Monitor Traffic: Use network visibility to spot traffic to AI domains so you can ask the right questions.
- Audit for Decisions: Ask your managers: “Are we using AI to rank job candidates or score credit?” If yes, reviewing the VCDPA opt-out rules is a smart move.
- Train, Don’t Just Prohibit: Help employees understand why pasting client data into free AI tools is dangerous. Samsung’s engineers weren’t malicious—they just didn’t understand the implications.
3. Vendor Data Practices & Assessments
The Business Risk: You might click “I Agree” on a new software tool without reading the terms. But some vendors quietly reserve the right to share your data with “affiliates” or use it for training.
The RealPage antitrust case is instructive. Landlords used an AI pricing tool that allegedly shared competitive pricing information between companies that shouldn’t have been talking to each other. The tool became evidence in a price-fixing conspiracy case. Your AI vendor’s data practices can become your legal liability.
The Virginia Context: Under Virginia law, businesses are often required to conduct a Data Protection Assessment (DPA) for processing that presents a “heightened risk”—such as selling personal data or certain types of profiling. If your vendor practices trigger this definition, you want to be sure you have that assessment on file.
Practical Steps:
- Review Contracts: Look specifically for data training clauses.
- Ask the Question: Before buying, ask in writing: “Is my data used for training AI models?”
- Document Risks: Consider conducting a documented risk assessment for any new AI tool handling personal data.
- Build a Vendor Checklist: In my Copilot audit framework, I cover how to evaluate whether you’re getting value from AI subscriptions. The same rigor applies to privacy.
4. Prompt Injection & Data Leaks
The Business Risk: “Prompt Injection” is a technique where a user tricks an AI into ignoring its instructions. A hacker (or a curious employee) could potentially trick your internal chatbot into revealing private salary data or customer lists.
The Virginia Context: If such a trick leads to the exposure of unredacted “personal information” (like Tax IDs), it could trigger Virginia’s Breach Notification Statute. You generally want to avoid any scenario where a “clever trick” forces you to send notification letters to the Attorney General.
Practical Steps:
- Limit “Read” Access: Ensure your AI only has access to the specific documents it needs to do its job.
- Verify Permissions: Does the AI have the same security clearance as the human using it?
- Filter and Validate Outputs: Don’t let AI output raw database queries, file paths, or unfiltered document contents.
- Test Your Systems: If you’re building custom AI integrations, red-team them. Try to break your own system before someone else does.
5. Hallucinations & The “Right of Publicity”
The Business Risk: AI makes things up. It’s called hallucination, and it’s not a bug—it’s a fundamental characteristic of how these systems work. Your AI might generate a “customer testimonial” using a real person’s name or create a marketing image that looks exactly like a local figure.
It might also produce legal citations that sound authoritative but don’t exist—which is what got lawyers sanctioned in the Mata v. Avianca case.
The Virginia Context: This creates a risk beyond just bad PR. Virginia’s Right of Publicity statute protects individuals from having their name or likeness used for commerce without consent. If your AI accidentally “invents” an endorsement from a real person, you could face a direct claim from that individual.
Practical Steps:
- Human Review is Mandatory: Never publish AI content without a human eye on it.
- Verify Identity: If AI generates a face or a name for an ad, double-check it doesn’t resemble a real local person.
- Use Clear Disclaimers Internally: Make sure your team knows AI output is a draft, not a source of truth.
- Constrain Outputs: For customer-facing AI, configure it to only surface information from your verified knowledge base—not generate new “facts.”
6. Compliance Blind Spots
The Business Risk: Small business owners often assume they are “too small to be noticed” by regulators.
Many don’t realize that dropping client data into an AI tool can trigger compliance obligations. If you’re a healthcare practice using ChatGPT to summarize patient notes, you may have just committed a HIPAA violation—even if the AI tool has a BAA available, you probably didn’t sign one for the consumer tier.
The Virginia Context: In Virginia, privacy enforcement is largely handled by the Attorney General. While there are volume thresholds (like processing data of 100,000 consumers), even smaller firms need to be mindful of sensitive data handling and breach notifications. Ignoring these standards can be a costly gamble.
The penalty structure is real. HIPAA violations run $100 to $50,000 per violation, with annual caps up to $1.5 million.
Practical Steps:
- Map Your Data: Know where sensitive data goes and which AI tools touch it.
- Check Thresholds: Consult with a professional to see if you meet the specific volume thresholds for full VCDPA compliance.
- Create Approved Tool Lists by Data Type: “For client PII, use Tool A. For general research, Tool B is fine. Tool C is never approved.”
- Get It in Writing: If a vendor claims compliance, get documentation. “Trust me” isn’t a compliance strategy.
7. The “Local Is Safe” Misconception
The Business Risk: “We’ll run our own AI locally! No cloud!” This sounds secure, but if your local server is misconfigured, you might still be exposed.
Your “local” LLM might still phone home for telemetry. The web interface you use to access it might log prompts to a cloud service. I’ve seen companies pat themselves on the back for running “local AI” while their Docker deployment sends logs to a third-party monitoring service that stores everything they type.
The Virginia Context: If you work with the public sector, you may fall under the Government Data Collection and Dissemination Practices Act. A “local” AI that accidentally scrapes or shares public data without authority could be non-compliant with these specific state rules.
Practical Steps:
- Audit Tech Dependencies: Ensure your “local” software isn’t sending telemetry data back to a third party.
- Review Contracts: If you are a government contractor, double-check your data handling agreements before deploying AI.
- Air-Gap Sensitive Deployments: For truly sensitive work, the machine shouldn’t be on a network that reaches the internet.
- Treat Local AI with the Same Rigor as Cloud: Just because it’s on your hardware doesn’t mean it’s automatically secure.
The Virginia Business Privacy Self-Check
How robust is your current setup? Here’s a quick self-assessment:
| Question | Potential Risk | Best Practice |
|---|---|---|
| Are you “Profiling”? | Using AI to score/rank people without disclosure | Offering a clear opt-out choice |
| Sensitive Data? | Pasting biometric/location data into public AI | Strict “No Sensitive Data” policy for AI |
| Deepfakes/Likeness? | Using AI to generate “people” in ads | Verifying no Right of Publicity issues |
| Assessments? | No documentation of risks | Documented Data Protection Assessment |
| Breach Protocol? | Unclear plan | Plan aligned with notification statutes |
| Vendor Contracts? | Standard “Click-to-Accept” | Signed Data Processing Agreement |
If you see more risks than best practices, it might be time to review your strategy.
What to Do Next
You can’t hide from AI, but you can manage how you use it.
Start with these three steps:
-
Inventory: Identify every AI tool your staff uses.
-
Policy: Draft a simple usage policy (we can help with templates).
-
Triage: Focus on the highest risk first—usually where sensitive client data meets free, public AI tools.
The businesses that thrive with AI aren’t the ones who avoid it. They’re the ones who use it deliberately, with clear boundaries and appropriate controls.
Need help navigating these business risks? We help Virginia businesses balance innovation with prudent risk management. Get in touch for a consultation or download our Business AI Policy Template.