Data Privacy and Security in AI Systems
Course 3305
2 Hours
$199
Compliance isn’t an afterthought – it’s the foundation of successful AI adoption in healthcare. This course equips healthcare leaders with the tools to develop a compliance-first AI strategy, ensuring alignment with regulations, ethical integrity, and organizational goals. Learn how to mitigate risks and build trust through compliance.
Target Audience
- Compliance officers
- Healthcare executives and administrators
- IT and AI project managers
- Legal and risk management professionals
Course Format:
Live Virtual Lectures
Interactive Activities:
- Case studies, scenario analyses, and hands-on workshops to apply learning.
Resources and Materials:
- Compliance checklists and strategy templates.
- Links to regulatory resources and tools for monitoring compliance.
Certificate of Completion
Payment Information: We accept credit card payments.
Course Objectives
- Understand the unique privacy and security challenges associated with AI in healthcare.
- Learn best practices for protecting sensitive data, including encryption, de-identification, and access controls.
- Gain tools to develop robust data governance policies for AI systems.
- Build strategies to monitor and address emerging data privacy risks as AI technologies evolve.
Course Modules Overview:
Understanding Data Privacy Challenges in AI
- How AI uses and processes sensitive healthcare data.
- Overview of regulatory requirements for data privacy, including HIPAA and GDPR.
- Common data privacy risks in AI applications.
Data Security Best Practices for AI Systems
- Implementing encryption and secure data storage for AI tools.
- Building secure data pipelines for AI model training and deployment.
- Access control measures to prevent unauthorized data use.
De-Identification and Anonymization Techniques
- How to de-identify healthcare data for AI while preserving utility.
- Balancing data usability with privacy protection.
- Tools and techniques for effective data anonymization.
Monitoring and Responding to Privacy Breaches
- Setting up systems to detect and respond to data breaches.
- Steps to take in the event of a privacy violation.
- Building a response framework for regulatory reporting and remediation.
Group Rates Available!
Bring your team and save – perfect for healthcare organizations looking to train multiple leaders.
Discounts available for organizations enrolling 5+ participants.
Class Schedule
Rates below are for each participant.
January 10, 2025
4:00 PM – 6:00 PM EST
Data Privacy and Security in AI Systems, Virtual, EST
$199
Class ID: 3305
February 13, 2025
5:00 PM – 7:00 PM EST
Data Privacy and Security in AI Systems, Virtual, EST
$199
Class ID: 3305
March 12, 2025
12:00 PM – 2:00 PM EST
Data Privacy and Security in AI Systems, Virtual, EST
$199
Class ID: 3305
Frequently Asked Questions
Why is data privacy particularly important in AI systems?
AI systems often process sensitive healthcare data at scale, making them vulnerable to breaches and misuse. Ensuring data privacy protects patient trust, complies with regulations, and prevents legal and reputational risks.
What are the common privacy risks in AI systems?
Risks include unauthorized access, data breaches, improper de-identification of patient data, and biases in how data is used. This course explores strategies to mitigate these risks effectively.
Does this course cover specific data privacy regulations like HIPAA and GDPR?
Yes, the course provides an overview of key regulations and how they apply to AI systems, with actionable guidance for maintaining compliance in healthcare settings.
How can I ensure that de-identified data is still useful for AI models?
The course covers techniques to balance data privacy with usability, ensuring de-identified data retains enough detail for training effective AI models while meeting privacy standards.
What tools will I learn to use for securing AI data pipelines?
You’ll learn about tools for encryption, access control, and secure data storage, as well as monitoring systems to detect unauthorized access or anomalies.
What is the role of ethical AI practices in data privacy?
Ethical AI practices, such as transparency in data usage and decision-making, enhance trust and accountability while reducing the likelihood of privacy violations.
How does this course address third-party data sharing?
The course discusses best practices for managing data shared with AI vendors, including contract negotiations, data governance, and ensuring vendor compliance with regulations.
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