Certified Professional in AI Regulated Compliance Assessments
-- viewing nowAI Regulated Compliance Assessments is a certification program designed for professionals working in Artificial Intelligence (AI) and regulatory environments. AI professionals must ensure their models comply with various regulations, such as GDPR and HIPAA.
5,204+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
About this course
This certification program equips learners with the knowledge and skills to conduct compliance assessments and mitigate risks associated with AI.
It covers topics like data privacy, model interpretability, and bias detection.
By obtaining this certification, professionals can demonstrate their expertise in AI regulated compliance and advance their careers.
Explore the world of AI and regulatory compliance today and take the first step towards a career in this exciting field.
100% online
Learn from anywhere
Shareable certificate
Add to your LinkedIn profile
2 months to complete
at 2-3 hours a week
Start anytime
No waiting period
Course details
•
Data Privacy Governance: This unit focuses on the development and implementation of data privacy policies, procedures, and controls to ensure compliance with regulations such as GDPR and CCPA. •
Artificial Intelligence and Machine Learning Ethics: This unit explores the ethical considerations and implications of AI and ML, including bias, transparency, and accountability, and how to develop and deploy AI systems that align with human values. •
Regulatory Frameworks for AI: This unit delves into the regulatory frameworks governing AI, including laws, regulations, and standards that govern AI development, deployment, and use, such as the EU's AI White Paper. •
AI and Data Protection by Design: This unit examines the principles of data protection by design and by default, and how to integrate data protection into AI systems and products, ensuring that AI is developed and deployed in a way that respects user rights. •
Human Oversight and Accountability in AI Systems: This unit discusses the importance of human oversight and accountability in AI systems, including the role of humans in AI decision-making, and how to ensure that AI systems are transparent, explainable, and fair. •
AI and Cybersecurity: This unit explores the intersection of AI and cybersecurity, including the potential risks and threats associated with AI-powered systems, and how to develop and deploy AI systems that are secure and resilient. •
AI for Social Good: This unit examines the potential of AI to drive social good, including applications in healthcare, education, and environmental sustainability, and how to develop and deploy AI systems that promote social impact. •
AI and Bias: This unit discusses the issue of bias in AI systems, including the sources of bias, and how to detect, mitigate, and prevent bias in AI decision-making. •
AI Governance and Risk Management: This unit provides an overview of AI governance and risk management, including the importance of establishing clear governance frameworks, identifying and mitigating risks, and developing effective risk management strategies. •
AI and Transparency: This unit explores the importance of transparency in AI systems, including the need for explainability, interpretability, and accountability, and how to develop and deploy AI systems that are transparent and trustworthy.
Data Privacy Governance: This unit focuses on the development and implementation of data privacy policies, procedures, and controls to ensure compliance with regulations such as GDPR and CCPA. •
Artificial Intelligence and Machine Learning Ethics: This unit explores the ethical considerations and implications of AI and ML, including bias, transparency, and accountability, and how to develop and deploy AI systems that align with human values. •
Regulatory Frameworks for AI: This unit delves into the regulatory frameworks governing AI, including laws, regulations, and standards that govern AI development, deployment, and use, such as the EU's AI White Paper. •
AI and Data Protection by Design: This unit examines the principles of data protection by design and by default, and how to integrate data protection into AI systems and products, ensuring that AI is developed and deployed in a way that respects user rights. •
Human Oversight and Accountability in AI Systems: This unit discusses the importance of human oversight and accountability in AI systems, including the role of humans in AI decision-making, and how to ensure that AI systems are transparent, explainable, and fair. •
AI and Cybersecurity: This unit explores the intersection of AI and cybersecurity, including the potential risks and threats associated with AI-powered systems, and how to develop and deploy AI systems that are secure and resilient. •
AI for Social Good: This unit examines the potential of AI to drive social good, including applications in healthcare, education, and environmental sustainability, and how to develop and deploy AI systems that promote social impact. •
AI and Bias: This unit discusses the issue of bias in AI systems, including the sources of bias, and how to detect, mitigate, and prevent bias in AI decision-making. •
AI Governance and Risk Management: This unit provides an overview of AI governance and risk management, including the importance of establishing clear governance frameworks, identifying and mitigating risks, and developing effective risk management strategies. •
AI and Transparency: This unit explores the importance of transparency in AI systems, including the need for explainability, interpretability, and accountability, and how to develop and deploy AI systems that are transparent and trustworthy.
Career path
| **Role** | **Description** |
|---|---|
| **AI/ML Engineer** | Design and develop intelligent systems that can learn and adapt to new data, using machine learning algorithms and large datasets. |
| **Data Scientist** | Extract insights and knowledge from data using statistical models, machine learning algorithms, and data visualization techniques. |
| **Business Intelligence Developer** | Design and develop business intelligence solutions using data visualization tools, SQL, and data mining techniques. |
| **Quantitative Analyst** | Analyze and model complex financial systems using statistical models, machine learning algorithms, and data visualization techniques. |
| **Computer Vision Engineer** | Develop intelligent systems that can interpret and understand visual data from images and videos using machine learning algorithms and computer vision techniques. |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
Why people choose us for their career
Loading reviews...
Frequently Asked Questions
Debug: False
Course fee
MOST POPULAR
Fast Track
GBP £149
Complete in 1 month
Accelerated Learning Path
- 3-4 hours per week
- Early certificate delivery
- Open enrollment - start anytime
Standard Mode
GBP £99
Complete in 2 months
Flexible Learning Pace
- 2-3 hours per week
- Regular certificate delivery
- Open enrollment - start anytime
What's included in both plans:
- Full course access
- Digital certificate
- Course materials
All-Inclusive Pricing • No hidden fees or additional costs
Get course information
Earn a career certificate
CERTIFIED PROFESSIONAL IN AI REGULATED COMPLIANCE ASSESSMENTS
is awarded to
Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
