WVU Cyber-Resilience Resource Center Tackles AI Security and Data Privacy Risks for West Virginia

If you’ve ever pasted a customer list, a draft contract, or a snippet of code into an AI chatbot to “get a quick answer,” here’s a simple rule that might save you from a major headache: treat anything you type into AI tools as non-private. That’s the straight talk coming from West Virginia University’s Cyber-Resilience Resource Center (CRRC), which is carving out a critical niche helping people and organizations in West Virginia use AI safely—without leaking sensitive data or breaking the rules along the way.

According to recent coverage from WV Metro News, the CRRC is focused on making AI adoption both practical and secure. Their message is refreshingly clear: AI can boost productivity and insight, but it also opens new attack paths. The Center’s guidance emphasizes responsible AI, cloud-aware data privacy, and getting the fundamentals right before flashy tools create big risks.

Below, we’ll unpack why AI security risks are different, what makes WVU’s CRRC approach effective, and the concrete steps any West Virginia business, school, or local government can take to use AI responsibly—starting today.

Why AI Security Risks Are Different—and Growing Fast

Artificial intelligence changes the risk equation in a few key ways:

It concentrates valuable data. AI tools often ingest prompts, files, and chat histories into cloud systems that may be analyzed to improve services, or at minimum, log usage for operational purposes.
It’s easy to overshare. Conversational interfaces encourage users to paste sensitive details that were never intended to leave a private network.
It blends consumer and enterprise use. “Shadow AI”—when staff use public AI tools without approval—can bypass the controls your IT team relies on to protect data.
It expands the attack surface. New techniques like prompt injection and data poisoning can manipulate AI assistants or expose backend systems.
It blurs accountability. Outputs can be convincing but wrong, making it hard to spot when an AI suggestion could lead to compliance violations or security gaps.

These realities demand updated guardrails for privacy, compliance, and cyber hygiene—especially for organizations handling personally identifiable information (PII), healthcare data, financials, intellectual property, or critical infrastructure details.

Meet WVU’s Cyber-Resilience Resource Center (CRRC)

WVU’s CRRC exists to translate these complex AI risks into practical, real-world resilience for West Virginia. The program, highlighted by WV Metro News, draws on the university’s land-grant mission: connect expertise with immediate community needs. Students and faculty work directly with local businesses, organizations, and individuals to:

Map where AI is used and what data it touches
Explain how cloud-based AI tools handle inputs
Design policies for safe AI use
Implement reinforcement security measures and training

The Center’s scope reaches beyond campus. It serves any business, organization, or critical infrastructure operator in West Virginia and collaborates alongside national efforts working on similar problems. Its approach reflects and complements resources from national leaders like the NIST National Cybersecurity Center of Excellence (NCCoE) and the NIST AI Risk Management Framework. Center leadership even brought this message to state lawmakers during WVU Day at the Capitol—spotlighting both the local urgency and the statewide benefit.

The Big Rule: Treat AI Inputs as Non-Private

The most important, easy-to-remember rule the CRRC shares is this: assume that anything you enter into an AI platform is transmitted to a cloud service and is not private.

That means: – Don’t paste PII (names, addresses, Social Security numbers, driver’s licenses, phone numbers, emails, etc.). – Don’t share protected health information (PHI) or anything that could be regulated by laws like HIPAA or FERPA. – Don’t include payment card data, full bank account numbers, or anything that could trigger PCI DSS obligations. – Don’t disclose trade secrets, unreleased financials, contracts, source code, or system credentials.

Yes, some enterprise-grade AI offerings provide data segregation and contractual assurances. But even those require careful configuration, legal review, and policy training to use safely. Until you’ve confirmed those controls, the safest assumption is that AI inputs are leaving your environment and could be stored, logged, or processed outside your organization.

For reference and further reading: – HIPAA privacy rules: HHS HIPAA Summary – Education privacy: FERPA Overview – Payment security: PCI Security Standards – FTC business guidance for AI claims and practices: FTC Business Guidance

Responsible AI in Practice: A Playbook for WV Organizations

Here’s a pragmatic, CRRC-aligned blueprint you can start implementing right away.

1) Inventory your AI usage – Identify where AI shows up: chatbots, coding assistants, office suite copilots, data analytics, marketing content. – Note who’s using which tools (including personal accounts) and what data they handle.

2) Classify and label your data – Mark sensitive data (PII, PHI, financials, IP) and define what can never be pasted into public AI tools. – Create quick-reference labels for staff: Public, Internal, Restricted, Regulated.

3) Approve tools and set minimum standards – Pick a short list of allowed AI services—prefer enterprise plans with admin controls, data-use restrictions, and audit logs. – Disallow unvetted public tools for sensitive work.

4) Configure enterprise-grade controls – Disable training on your data where possible. – Enforce single sign-on (SSO), least privilege, and conditional access. – Turn on detailed logging and retention appropriate to your compliance needs.

5) Update policies and guidance – Write an AI Acceptable Use Policy (AUP) that’s short, clear, and specific to your workflows. – Include do/don’t examples and where to get help.

6) Train your workforce – Quick micro-trainings: what not to paste, how to spot prompt injection, and how to verify outputs. – Teach staff to use “sanitized” prompts and to redact data before submitting.

7) Monitor, measure, and iterate – Review logs for unusual usage or sensitive file uploads. – Run red-team style tests to probe defenses and refine prompts, controls, and policies.

8) Prepare an incident response path – Define how to respond if sensitive data was pasted into an AI tool: who to notify, what logs to collect, and how to contain exposure. – Practice tabletop exercises involving AI misuse scenarios.

Helpful references to align your program: – NIST AI Risk Management Framework – CISA Secure by Design – OWASP Top 10 for LLM Applications

Student-Led, Business-Focused Engagements

One standout advantage of the CRRC is its hands-on model. Students, guided by experienced faculty and practitioners, support West Virginia organizations where it matters:

Rapid AI risk assessments tailored to size and sector
Drafting AI AUPs, data classification guides, and user training snippets
Reviewing cloud and AI service configurations for privacy and access control gaps
Helping teams build “safe prompting” patterns and redaction workflows
Testing for misuse paths like prompt injection and insecure plugins
Preparing leadership briefings and board-ready executive summaries

It’s a win-win: West Virginia gains accessible, real-time help, and the next generation of cybersecurity talent learns by solving live challenges in their own communities.

Common AI Threats the CRRC Helps Address

Data leakage via prompts and uploads

Risk: Sensitive information pasted into chat interfaces may be logged or processed by third-party cloud systems.
Mitigation: Redaction tooling, content filters, enterprise settings disabling training, and clear user guidance.

Shadow AI in the workplace

Risk: Employees quietly use personal AI tools to speed up tasks, potentially bypassing security and compliance.
Mitigation: Offer approved tools that are just as convenient; blocklist high-risk domains; provide a simple request process for new tools.

Prompt injection and model manipulation

Risk: Attackers craft content that “hacks” AI assistants into revealing secrets or executing harmful actions (for example, embedded instructions in a webpage an assistant reads).
Mitigation: Limit tool/plugin permissions, use allowlists, implement robust content validation, and teach users to distrust autonomous actions without confirmation.

Model output reliability (hallucinations)

Risk: Convincing but incorrect outputs can mislead decisions or generate non-compliant language.
Mitigation: Verification workflows, human-in-the-loop review for sensitive tasks, citations and source linking, and domain-specific guardrails.

Compliance pitfalls

Risk: Unintentional HIPAA, FERPA, GLBA, or PCI exposures through AI.
Mitigation: Clear “never paste” lists, robust data classifications, approval gates for regulated workflows, and auditable logs.

Cloud misconfiguration and over-permissioning

Risk: Excessive permissions or misconfigured storage/services increase blast radius if a token is compromised.
Mitigation: Least privilege, segmented identities for AI tools, short-lived tokens, and continuous configuration monitoring.

Third-party and supply chain risk

Risk: Dependencies on AI APIs, plugins, or model providers create inherited risk.
Mitigation: Vendor risk assessments, contractual data-use limits, security attestations, and kill-switch plans.

Quick Safeguards You Can Deploy This Month

Publish a one-page AI Do/Don’t checklist. Keep it short and visible.
Turn off “use data for training” wherever your enterprise subscriptions allow.
Require SSO and multifactor authentication (MFA) for all approved AI tools.
Build a simple prompt sanitation step: redact PII and unique identifiers by default.
Create a “sensitive data safe list”—only certain roles can handle regulated data, and never through public AI tools.
Log and review uploads to AI services. If possible, alert on files with PII patterns.
Pilot test: have a small team validate AI outputs in a controlled use case (e.g., summarizing public documents) before broader rollout.
Empower a single internal owner (or small committee) for AI governance so decisions don’t stall.

How WVU’s Approach Aligns with National Frameworks

The CRRC’s advice is not just common sense—it lines up with leading national guidance:

NIST AI RMF: Emphasizes governance, mapping risks, measuring, and managing them across the AI lifecycle. See: NIST AI RMF
NIST NCCoE: Publishes practical, repeatable cybersecurity solutions and reference architectures. See: NIST NCCoE
CISA Secure by Design: Encourages vendors and implementers to bake in security controls and transparency. See: CISA Secure by Design
OWASP LLM Top 10: Identifies common vulnerabilities in AI/LLM applications. See: OWASP LLM Top 10

By rooting local support in national standards, the CRRC helps West Virginians move quickly without cutting corners.

What This Means for West Virginia’s Critical Infrastructure

From healthcare and utilities to local government and education, AI is already changing daily operations in West Virginia. That makes the CRRC’s work especially timely in sectors like:

Healthcare: Protecting PHI, ensuring clinical decision support is reviewed, and aligning with HIPAA.
Energy and utilities: Guarding operational data, preventing AI-driven automation from introducing unsafe control changes.
Municipalities: Managing records responsibly, using AI for citizen services without exposing resident data.
Education: Navigating FERPA while enabling safe student and faculty use of AI for learning and research.
Financial services: Preserving confidentiality, avoiding model bias in lending contexts, and aligning with security standards.

In each sector, the principle is the same: start with data protection and responsible use, then scale AI thoughtfully.

How to Engage the CRRC

Per WV Metro News, the WVU CRRC is available to any business, organization, or critical infrastructure operator in West Virginia. If you’re exploring AI or already feeling growing pains:

Read the recent coverage for context and contact pathways: WV Metro News article
Connect with WVU and its cybersecurity programs to learn more: West Virginia University
Gather your questions and current tools list so your first conversation is productive.

Expect a practical, no-judgment approach that focuses on quick wins and sustainable safeguards.

FAQs: Responsible AI Use for West Virginia Organizations

Q1) Is it ever safe to put PII into an AI tool? – Only if you are using an enterprise-grade platform that your organization has vetted and configured to prevent training on your data, enforce strict access controls, and meet your compliance obligations—and even then, limit to the minimum necessary. When in doubt, don’t paste it.

Q2) What counts as PII, exactly? – Names, email addresses, phone numbers, home addresses, SSNs, driver’s license numbers, passport numbers, student IDs, patient IDs, precise location data, and combinations of data that can identify a person. When regulated by context (health, student, financial), treat it as highly sensitive.

Q3) Our vendor says “we don’t train on your data.” Are we covered? – It helps, but you still need to confirm where data is stored, who can access it, how long logs are retained, and how incident response works. Contractual terms, technical controls, and auditability all matter.

Q4) How do we prevent staff from using shadow AI? – Provide an approved alternative that’s just as convenient, publish a simple policy with clear examples, and block high-risk services at the network or browser level. Training and quick reference guides go a long way.

Q5) Can we safely use AI to summarize internal documents? – Yes, with guardrails: use an approved enterprise instance, restrict access, sanitize documents to remove sensitive fields where possible, and log interactions. Keep a human in the loop for any decisions or external disclosures.

Q6) How do we detect prompt injection attempts? – Train users to be skeptical of instructions embedded in external content. Limit tool/plugin privileges, adopt allowlists, validate inputs/outputs, and monitor for unusual assistant behaviors. Refer to the OWASP LLM Top 10 for patterns to test.

Q7) What should we log for AI governance? – User, timestamp, tool/version, prompt metadata (sanitized), files uploaded (hashes or titles), output length/type, and error or policy-block events. Protect logs as sensitive and retain them per your compliance needs.

Q8) We’re a small business. Is this overkill? – Not at all. Start small: pick one approved tool, share a one-page do/don’t guide, disable training on your data, and run a 30-minute training. You’ll reduce most of the risk with minimal overhead.

Q9) Who sets the rules—IT, security, legal, or HR? – Treat AI as a team sport. Security and IT handle controls, Legal covers contracts and compliance, HR helps with policy and training, and business units define practical use cases. Designate one accountable owner to keep it moving.

Q10) Where can we learn more? – National frameworks and guidance: NIST AI RMF, NIST NCCoE, CISA Secure by Design, FTC Business Guidance, and OWASP LLM Top 10. For the WVU CRRC context, see the WV Metro News coverage.

The Bottom Line

AI is here to stay—and it can pay off. But without clear guardrails, a single careless paste can expose personal data, violate regulations, or leak your competitive edge. WVU’s Cyber-Resilience Resource Center is stepping in at exactly the right moment to help West Virginians embrace AI confidently and responsibly.

Treat AI inputs as non-private by default. Choose enterprise tools deliberately. Train people to protect data. And when you’re ready to level up, partner with experts who know the local landscape and the national standards. West Virginia can lead in responsible innovation—and the CRRC is a powerful ally on that journey.

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