AI and Privacy in Virginia: Extending Strong Foundations Into the Future

Artificial intelligence is no longer hypothetical. It is already part of the tools we use every day, whether we invite it in or not. For compliance professionals, legal operations teams, and business leaders, the real challenge is not whether to adopt AI, but how to adapt existing processes so they remain strong when AI enters the picture.

Responsible use of AI is not about rewriting everything from scratch. It is about extending the practices you already know: records management, privacy, governance, and operational discipline. These are the same foundations that compliance professionals rely on every day, and they remain just as important now. AI simply raises the bar for making sure those procedures work.

Virginia’s Starting Point

Virginia has taken important steps with the Virginia Consumer Data Protection Act (VCDPA). Residents now have rights to access, correct, delete, and obtain a copy of their personal information, along with the ability to opt out of targeted advertising and profiling. Businesses have clear obligations to respond to those rights.

For operations teams, the VCDPA created new workflows, reporting requirements, and data-handling standards. That was progress.

But AI changes how those obligations play out. Systems can now generate insights from data that were never intentionally collected. For example, a location tracker and search history can be combined to infer health status. Suddenly, businesses may be processing “sensitive” information without realizing it.

For compliance and operations professionals, this is not just a policy issue. It is a process issue. How do you audit, document, and manage workflows when AI tools expand the scope of what data means?

Three Operational Gaps

1. Opt-Outs That Work

Right now, businesses may handle opt-outs through individual forms, links, or settings. But that is inefficient for consumers and creates a tracking nightmare for compliance teams. States like Colorado have introduced universal opt-out signals, which allow consumers to apply their choice once and have it recognized across sites and systems.

For an operations professional, this shift means building a process that recognizes external signals automatically. It also means auditing your systems for “dark patterns,” the confusing or manipulative interfaces that regulators in California have already started targeting.

If your intake or consent process is complex, the operational fix is simplification: clear records of consent, automated recognition of opt-out requests, and transparent workflows for responding to them.

2. Health Data Outside HIPAA

HIPAA does not apply to most consumer apps, wearables, or data brokers. Yet AI can take seemingly neutral information, like shopping patterns or activity levels, and turn it into health insights.

States like Washington and Connecticut have filled this gap with new rules. For businesses in Virginia, the operational question becomes: what counts as “health data” in your system, and do you have a process to handle it differently?

That means:

• Clear definitions in your records policies.

• Consent tracking that accounts for sensitive categories.

• Retention schedules that reflect higher-risk data.

• Controls on geofencing and targeted ads.

Compliance teams need to map where this data flows and how it is stored, because AI makes “health data” show up in places you may not expect.

3. AI in Decision-Making

AI is increasingly influencing credit, hiring, insurance, and housing. Virginia residents have limited rights to opt out of profiling, but they do not receive explanations or review mechanisms. From an operational standpoint, that is a red flag.

California and Colorado have required algorithmic impact assessments and plain-language notices. Virginia tried a similar law in 2024 but it was vetoed. That does not erase the operational need. If you are deploying AI in decision-making, you must be able to:

• Document how the model is trained and tested.

• Explain the logic to stakeholders in plain language.

• Maintain a process for human review of high-impact outcomes.

Operations and compliance professionals will be the ones who build and maintain these controls, regardless of whether state law catches up immediately.

Why This Matters for Operations

For legal operations and compliance teams, the lesson is simple: these reforms may sound like policy debates, but they land squarely in your workflows. Opt-outs require system changes. Expanded definitions of health data demand updates to records schedules and consent tracking. AI governance requires new documentation, training, and review processes.

This is not abstract law. This is process design. And when AI raises the stakes, organizations that already have strong operational foundations will be the ones that succeed.

Think of AI as a new tool in the workshop. You do not throw out the old ones, but you do need stronger safety practices. If you have weak processes today, AI will expose them. If you have strong foundations, AI can help you scale without losing control.

The Path Forward

Other states have already shown what is possible. Virginia can adopt proven models for opt-outs, health data, and AI governance without forcing businesses into chaos. That means compliance teams can prepare now by:

• Auditing opt-out processes and simplifying them

• Mapping health-related data beyond HIPAA categories

• Creating documentation and review steps for AI-driven decisions

This work does not wait for a new statute to pass. It is part of building resilient operations today.

Conclusion

Virginia’s VCDPA gave us a strong starting point. AI has revealed where the gaps are, and the fixes will come. For businesses, nonprofits, and legal teams, the question is not when the law changes but whether your processes are ready when it does.

AI is not a replacement for operational discipline, it is a stress test of it. Responsible use is simply an extension of what compliance and legal operations professionals already do. If your foundation is solid, AI can be just another tool in your arsenal. If not, it is time to reinforce the structure before the pressure increases.

If you want to talk through what these changes could mean for your organization, let’s connect. My work is about helping operations teams build scalable, resilient processes that stand up to both today’s expectations and tomorrow’s technology shifts.