Virginia’s Landmark High‑Risk AI Law: What It Means for Developers, Deployers, and Every Business Touching AI

Did your AI just become “high risk” overnight? If you hire, lend, diagnose, underwrite, assess eligibility, or influence liberty or livelihoods with automated systems, the answer in Virginia may now be yes. On February 20, 2025, Virginia enacted a comprehensive law to regulate the development and deployment of high‑risk AI systems—one of the most consequential state moves in the United States to date.

Why does this matter? Because Virginia just raised the bar for how companies must assess, disclose, and mitigate AI risks—echoing the shape of Europe’s AI Act while tailoring the rules to the state’s business landscape and enforcement style. With state attorneys general increasingly stepping into the AI arena and federal legislation still in flux, companies building or buying AI will have to navigate a growing patchwork of rules. Virginia’s law is a big step in that direction.

In this post, we’ll break down what the law covers, who it affects, what “high-risk AI” really means, and how to get compliant without stalling innovation. We’ll also compare Virginia’s approach to the EU AI Act and Colorado’s AI statute, and share a pragmatic roadmap you can start today.

For the original news reference, see the February 2025 roundup from Securiti: Virginia Passes New Law Regulating Development and Deployment of High-Risk AI Systems.

Quick take: The core of Virginia’s new high‑risk AI law

Virginia’s law targets AI systems with potential to cause substantial harm to individuals’ rights, safety, or economic opportunities. Think decisions and inferences that affect whether someone gets hired, a loan is approved, a diagnosis is escalated, or a person is flagged in a criminal justice workflow.

At a glance, the statute requires: – Risk and impact assessments before deployment of high‑risk AI systems – Clear disclosure to affected individuals that AI is being used – Mitigation strategies for identified biases, errors, or systemic risks – Accountability measures for both developers and deployers – Enforcement by the Virginia Attorney General with civil penalties up to $10,000 per violation

The law borrows the risk‑based posture popularized by the EU AI Act while tailoring it to local enforcement and business processes. Proponents argue it balances innovation and safety; critics worry about startup friction and offshoring incentives. Either way, implementation clock is ticking.

Why Virginia’s law matters now

AI impact is no longer hypothetical. Hiring algorithms, underwriting models, diagnostic triage, and predictive policing tools have moved from pilots to production.
Federal law is not keeping pace. With national AI legislation delayed, states are filling the vacuum, creating real obligations (and real enforcement) today.
The patchwork is expanding. Colorado passed an AI law in 2024 focused on consumer harms. California has advanced executive actions and multiple bills. Virginia’s move signals a maturing second wave of state AI governance.
Investors and boards expect control. AI diligence now hinges on demonstrable risk management—Virginia just codified that expectation.

What counts as a “high‑risk” AI system?

The statute defines high‑risk systems as those with the potential to cause substantial harm to individuals’ rights, safety, or economic opportunities. Based on Virginia’s description, examples include AI used in: – Hiring and employment decisions – Lending, credit, insurance underwriting, or eligibility determinations – Healthcare diagnostics, clinical decision support, triage, or risk prediction – Criminal justice, law enforcement triage, or public safety decisioning

That framing tracks with global norms: systems that materially affect access to jobs, money, medicine, safety, or fundamental rights undergo stronger scrutiny.

Practical rule of thumb

If your AI: – Scores, ranks, or classifies people in ways that gate access to critical services or opportunities, or – Contributes to decisions that carry legal, economic, or health/safety consequences

…assume you’re in scope, conduct a formal impact assessment, and build an auditable mitigation plan.

Borderline and adjacent use cases

These can tip into high‑risk depending on context and impact: – Proctoring/remote exam integrity tools for professional licensure or education – Tenant screening, housing eligibility, rent setting – K‑12 student risk or resource allocation models – Workplace monitoring or productivity scoring tools that feed into disciplinary actions – Content moderation where false positives can limit speech or access to services

Context matters. A recommendation model in a news app isn’t the same as a model that sets credit limits. The closer you are to decisions about rights, safety, or economic outcomes, the higher your compliance bar.

Who has obligations: Developers vs. deployers

Virginia explicitly places duties on both builders and users of high‑risk AI.

Developers (those who create or substantially modify high‑risk AI systems) are expected to:
Produce documentation enabling downstream risk assessments
Disclose known risks, limitations, and intended use cases
Support bias/error testing through access to model cards or equivalent technical documentation
Deployers (organizations that implement AI in a product, workflow, or decision process) must:
Complete impact assessments before deployment
Provide disclosures to affected individuals when AI is used
Implement and document mitigation strategies for identified risks
Monitor performance and adjust controls over time

If you play both roles—say you develop a hiring AI tool and use it internally—you inherit both sets of obligations.

The required impact assessment: What it should include

While Virginia will likely specify details in guidance or rulemaking, effective AI impact assessments typically cover:

Purpose and scope
What decision(s) does the AI support or make?
What is the intended use, context, and population?
Risk identification
Potential harms to rights, safety, and economic opportunities
Bias risks across protected characteristics
Error modes, model drift, and robustness concerns
Data lineage and quality issues
Testing and evaluation
Pre‑deployment accuracy and fairness metrics (e.g., false positive/negative rates, calibration, subgroup analyses)
Representative datasets and known gaps
Stress tests for edge cases and distribution shifts
Governance and controls
Human‑in‑the‑loop and escalation pathways
Thresholds and overrides for adverse outcomes
Monitoring plans, retraining cadence, and change management
Vendor and third‑party accountability
Transparency and explainability
What disclosures will be given to individuals?
What explanations are available for adverse decisions?
Mitigation strategies
Bias remediation plans, data augmentation, or constraint‑based training
Post‑decision recourse processes (appeal, review, or human reconsideration)
Safeguards for sensitive attributes and proxy variables

Map this to the NIST AI Risk Management Framework for structure and to speed stakeholder alignment.

Transparency: What you need to tell people (and when)

Virginia requires disclosure that AI is being used in high‑risk contexts. Practical approaches include:

At‑point notice: “This decision may be informed by an automated system (AI). A human reviewer is available upon request.”
Outcome notice: For adverse outcomes (e.g., rejection), provide a concise explanation of key factors and instructions to request human review.
Policy availability: Publish a plain‑language overview of your high‑risk AI governance on your website.

If you already comply with sectoral rules (e.g., adverse action notices under FCRA/ECOA in lending), align your AI notices with those frameworks. Where feasible, provide an “appeal to human” pathway.

For broader context on transparency expectations, see the EU’s approach under the AI Act: EU Council final approval of the AI Act.

Bias and error mitigation: Moving from findings to fixes

Mitigation isn’t a checkbox; it’s an ongoing lifecycle commitment. Core tactics include:

Data governance
Assess sampling bias, label quality, and missingness across groups
Document data lineage and consent/legal bases where applicable
Modeling strategies
Use constraint‑based training or post‑processing to reduce disparate impact
Run subgroup performance tests and set thresholds for acceptable variance
Operational safeguards
Insert human review where error costs are high
Provide clear escalation paths and audits of overrides
Continuous monitoring
Track drift and performance decay; retrain or recalibrate with governance approvals
Log decisions and explanations for auditability

Regulators like the FTC have repeatedly signaled that “unfair” AI practices invite scrutiny. For background, see the FTC’s guidance on AI fairness: Aiming for truth, fairness, and equity in your company’s use of AI.

Enforcement: Penalties and who’s watching

Virginia emphasizes enforcement by the state Attorney General, with civil penalties up to $10,000 per violation. That can add up quickly if counted on a per‑consumer or per‑decision basis. Expect the AG’s office to focus on:

High‑stakes domains (lending, employment, healthcare, criminal justice)
Patterns of harm, especially across protected groups
Inadequate or performative assessments
Failure to disclose meaningful AI use to affected individuals

Keep in mind overlapping enforcement from federal and sectoral agencies (e.g., EEOC on AI in employment, CFPB for credit decisions, HHS/OCR for health data and equity concerns).

You can track evolving federal AI governance in the U.S. via the White House’s 2023 Executive Order: Safe, Secure, and Trustworthy Development and Use of AI and the U.S. AI Safety Institute at NIST.

How Virginia’s law compares to the EU AI Act and Colorado’s AI statute

Risk‑based design
EU AI Act: Comprehensive tiers (prohibited, high‑risk, limited risk), detailed conformity assessments and CE‑like marking in the EU.
Virginia: Targets “high‑risk” systems with impact assessments, transparency, and mitigation; state‑level enforcement via AG.
Scope and specificity
EU: Exhaustive requirements for high‑risk systems (risk management, data governance, technical documentation, incident reporting).
Virginia: Aligns with the spirit of EU risk assessments and transparency; tailored to Virginia’s enforcement and business context.
U.S. State landscape
Colorado: Enacted a state AI law in 2024 focusing on consumer protection against algorithmic discrimination (SB24‑205).
California: Advancing AI governance via executive action and proposed legislation; expect further developments.

Bottom line: If you’re aligning with the EU AI Act and NIST AI RMF, you’ll be well along the path for Virginia—but you still need state‑specific disclosures, assessment timing, and documentation.

A practical compliance roadmap (that won’t stall your roadmap)

You can meet Virginia’s obligations while keeping your release velocity. Here’s a starter plan you can execute in 90 days.

Days 1–15: Inventory and triage

Inventory AI systems and features touching decisions about people
Classify which are likely “high‑risk” by impact, domain, and potential harm
Assign system owners, legal/regulatory leads, and risk partners

Deliverable: A live AI inventory with risk classification and owners.

Days 16–45: Build your assessment and documentation muscle

Stand up an AI Impact Assessment (AIA) template mapped to NIST AI RMF
Draft model cards or system cards (purpose, data, metrics, limits, intended uses)
Establish fairness and accuracy metrics with acceptable thresholds
Define disclosure language and when it triggers (pre‑use, post‑decision)

Deliverable: AIA template, model/system card templates, baseline metric thresholds, approved disclosure language.

Days 46–75: Test, mitigate, and operationalize

Run subgroup performance testing and bias analyses on high‑risk systems
Implement mitigation strategies (data fixes, constraints, human‑in‑the‑loop)
Configure decision logging and explanation capture
Train frontline and compliance teams on escalation and appeals

Deliverable: Completed AIAs for top high‑risk systems with documented mitigations and operational runbooks.

Days 76–90: Monitor and govern

Set up drift monitoring, review cadences, and change‑control workflows
Create an AI risk review committee or embed into existing risk forums
Validate disclosures live in product and customer communications
Prepare an “AI Governance Overview” page for your website

Deliverable: Monitoring dashboard, governance charter, public transparency page.

Developer and vendor responsibilities: Don’t ship a black box

If you build high‑risk AI products for customers in Virginia:

Provide robust documentation
Intended use, limitations, training data provenance (to the extent shareable), testing methodology, and known risks
Enable customer testing
Offer APIs or reports for fairness/accuracy evaluation and audit logs
Contract for accountability
Allocate responsibilities for bias testing, monitoring, and incident escalation
Versioning and changes
Notify customers of model updates with expected impact and recommended re‑testing

Deployers: bake these expectations into your RFPs and MSAs.

Balancing innovation and compliance: What startups and enterprises should consider

Startups
Opportunity: Build trust‑by‑design as a market differentiator; documentation and testing can accelerate enterprise sales.
Risk: Compliance overhead feels heavy—standardize templates and tooling early to keep overhead low.
Enterprises
Opportunity: Harmonize AI controls across jurisdictions (NIST AI RMF + EU AI Act alignment + Virginia specifics).
Risk: Shadow AI and vendor creep—centralize inventory, standardize impact assessments, and enforce procurement gates.
Across the board
Treat explainability and recourse as product features.
Automate what you can: dataset checks, metric dashboards, and change‑control workflows.
Empower a cross‑functional “AI Guild” (product, data science, legal, compliance, security, ethics).

What this signals for the U.S. AI landscape

State leadership is here to stay. Expect more states to adopt risk‑based AI rules anchored in impact assessments and transparency.
Convergence around frameworks. NIST AI RMF is fast becoming the lingua franca for U.S. AI governance programs.
Interplay with sectoral regulators. EEOC, CFPB, HHS/OCR, and FTC will shape enforcement norms, especially where AI amplifies existing legal duties.
Pressure for federal harmonization. The more state‑level variance, the stronger the call for national baselines to support innovation and interstate commerce.

For broader context on global convergence, review the EU’s structure, which many U.S. organizations are referencing as a north star: EU AI Act overview.

Action checklist: Are you Virginia‑ready?

Have we identified high‑risk AI systems and owners?
Do we have a documented AI impact assessment for each?
Are fairness and accuracy metrics defined—and tested by subgroup?
Is there a mitigation plan for identified risks?
Do we disclose AI use to affected individuals in plain language?
Can individuals request human review or appeal an adverse decision?
Are monitoring, drift detection, and change‑control in place?
Are vendor responsibilities and documentation contractually required?
Is our legal team prepared for AG inquiries with an audit‑ready dossier?

If you can’t answer “yes” to most of the above, prioritize remediation now.

Helpful resources

Securiti’s February 2025 roundup: Virginia Passes New Law Regulating High-Risk AI Systems
EU AI Act final approval (EU Council): Artificial Intelligence Act
Colorado 2024 AI legislation: SB24‑205
NIST AI Risk Management Framework: AI RMF
U.S. AI Safety Institute (NIST): AISI
White House AI Executive Order (2023): Safe, Secure, Trustworthy AI
FTC guidance on AI fairness: Truth, fairness, equity in AI
EEOC AI landing page: AI and the EEOC
Virginia Attorney General’s Office: OAG Virginia

FAQs: Virginia’s high‑risk AI law

What is considered a “high‑risk” AI system under the Virginia law?
Systems with potential to cause substantial harm to individuals’ rights, safety, or economic opportunities. Examples include AI used for hiring, lending and credit, healthcare diagnostics, and criminal justice decisioning.
Who has obligations—just developers, or also businesses that use AI?
Both. Developers must provide documentation and disclose known risks. Deployers must conduct impact assessments, disclose AI use to affected individuals, and implement mitigation strategies.
Does this apply only to companies physically in Virginia?
Generally, laws like this focus on activities affecting people in the state. If your AI system impacts individuals in Virginia (e.g., you screen Virginia job applicants or serve Virginia borrowers), expect to be in scope regardless of where you’re headquartered.
What has to be in the impact assessment?
Expect to document purpose and scope, risks (including bias and error), testing and metrics, governance and controls, transparency plans, and mitigation strategies. Aligning with the NIST AI RMF is a practical way to structure your assessment.
How big are the penalties?
Civil penalties are up to $10,000 per violation, enforced by the Virginia Attorney General. Depending on how “violation” is interpreted, exposure can scale quickly.
Is there a private right of action?
The law emphasizes enforcement by the state Attorney General. Organizations should review the final statutory text with counsel to understand any additional remedies or rights.
When do we need to comply?
Review the enacted text for effective dates and any phased timelines. Begin assessments and disclosures now to avoid last‑minute gaps.
What do disclosures to individuals need to say?
They should make clear that an AI system is in use for a high‑risk decision or decision support, and provide a straightforward path to request human review or appeal.
How does this interact with existing laws (e.g., FCRA/ECOA/Title VII/HIPAA)?
Virginia’s AI law sits on top of sectoral obligations. You still must follow existing federal and state laws on employment discrimination, credit decisions, consumer reporting, and health privacy. Coordinate with legal and compliance to align notices, testing, and record‑keeping.
We use a third‑party AI vendor. Are we covered?
Yes. Deployers remain responsible for impact assessments, disclosures, and mitigations. Contract with vendors for necessary documentation, testing access, and change notifications.
We’re a startup—how can we do this without grinding to a halt?
Standardize templates (AIA, model cards), automate testing dashboards, and keep a lean but real governance loop. Treat documentation as an accelerant for enterprise sales, not a tax.

The takeaway

Virginia just made high‑risk AI governance table stakes. If your AI can materially affect jobs, money, health, or safety, you now need to assess risks up front, tell people when AI is in play, and actively mitigate bias and errors—backed by documentation you can hand to a regulator.

Don’t wait for a federal fix. Inventory your AI, classify what’s high‑risk, run impact assessments, and operationalize mitigation and disclosure. Align with NIST AI RMF and keep an eye on EU AI Act‑style controls—those playbooks will help you scale across the emerging patchwork.

The companies that win in the next wave won’t just build powerful AI. They’ll build trustworthy AI—measurably, repeatably, and compliantly. Virginia just put that on the roadmap. Now it’s your move.

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Virginia’s Landmark High‑Risk AI Law: What It Means for Developers, Deployers, and Every Business Touching AI

Quick take: The core of Virginia’s new high‑risk AI law

Why Virginia’s law matters now