Responsible AI: Data Risks, Liability & Accountability
Tara Punwaney
Business Technology & AI Educator
Avoid costly AI mistakes: understand data risks, confidentiality, and control.
Using AI without understanding what happens to your data can create serious professional and business risks. A single setting, uploaded document, or misunderstood privacy policy can expose confidential information to uses you never intended.
Who can access your data? Is it stored, shared, or used to train models? Who owns the information, and what rights does the provider have? Can it be deleted? What happens if you've already submitted proprietary research, client records, or sensitive business information?
These aren't merely ethical questions. They can involve contractual violations, privacy incidents, lost competitive advantage, financial consequences, and potential legal liability.
This practical course teaches you to recognize hidden AI risks, examine provider settings and data policies, verify AI-generated information, protect confidentiality, and establish accountability.
You'll also learn to evaluate AI resource efficiency without compromising security or accuracy.
Leave with practical safeguards, a provider-risk checklist, and a framework for making informed AI decisions before something goes wrong.
What you’ll learn
From casual AI use to informed decisions that protect sensitive data, limit exposure, and reduce organizational risk.
Identify what AI tools collect, process, retain, and potentially share.
Examine training permissions, retention policies, data controls, and third-party access.
Create a checklist for understanding where information goes before submitting it.
Identify risks involving client information, proprietary research, and business records.
Explore how unauthorized disclosure can create contractual, financial, or legal consequences.
Use a pre-upload risk assessment to identify information that should not be shared.
Distinguish data ownership from provider permissions, retention, and training rights.
Learn what to examine in privacy policies, terms of service, and vendor agreements.
Develop questions to ask before trusting an AI provider with valuable information.
Recognize hallucinations, fabricated citations, and unsupported recommendations.
Apply verification methods to research, analysis, and important business decisions.
Establish human-review procedures for situations involving significant risk.
Understand token usage, AI operating costs, and unnecessary resource consumption.
Evaluate tradeoffs involving efficiency, reliability, privacy, and sustainability.
Choose cost-conscious AI practices without sacrificing security or accuracy.
Evaluate AI providers, settings, permissions, and organizational safeguards.
Establish responsibilities for approving, reviewing, and monitoring AI use.
Create a practical AI risk-management plan for professional or business use.
Learn directly from Tara
Tara Punwaney
AI educator and federal financial professional focused on practical AI skills.
Who this course is for
Business owners concerned about protecting proprietary information, customer data, intellectual property, and competitive advantage.
Professionals handling sensitive information who want to understand AI risks, protect confidentiality, and avoid costly mistakes.
Managers and consultants responsible for evaluating AI providers, managing information risks, and establishing safer AI practices.
What's included
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
AI Data Privacy & Settings Audit
Learn how to examine AI privacy settings, model-training permissions, retention options, and sharing controls. Practice identifying settings that could put sensitive information at risk and develop a repeatable process for reviewing them.
Confidentiality & Liability Risk Checklist
Identify situations where AI use could compromise confidential information, proprietary research, or business data. Learn to recognize potential contractual, financial, privacy, and liability consequences before information is shared.
AI Provider & Data Rights Evaluation Guide
Learn what to examine before adopting an AI platform, including data ownership, permitted uses, storage, retention, deletion, training permissions, and third-party access. Develop practical questions for evaluating providers.
AI Accuracy & Decision Verification Toolkit
Practice identifying hallucinations, unreliable sources, and misleading AI recommendations. Build verification and human-review procedures that help prevent costly errors in professional and business decisions.
Personal AI Risk Management Action Plan
Develop a practical approach for reviewing AI tools, protecting confidential information, assigning responsibilities, monitoring data settings, and balancing AI efficiency with security, reliability, and business value.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Frequently asked questions
Maven for Teams
Reimbursement
Get your company to pay
Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedPrivate cohort
Run a cohort for your org
A dedicated cohort with a custom schedule and curriculum, tailored to your team.
Book a private cohort