Advanced Certificate in AI Security for IT Professionals
-- viewing nowArtificial Intelligence Security is a rapidly evolving field that requires specialized knowledge to protect against cyber threats. Designed specifically for IT professionals, the Advanced Certificate in AI Security equips learners with the skills to identify and mitigate vulnerabilities in AI systems.
6,081+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
About this course
Learn how to apply security best practices to AI-powered systems, ensuring data integrity and preventing unauthorized access.
Gain expertise in AI security frameworks, threat analysis, and incident response to stay ahead in the industry.
Take the first step towards securing AI systems and protect your organization's sensitive data.
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
•
Artificial Intelligence (AI) Fundamentals: This unit covers the basics of AI, including machine learning, deep learning, and natural language processing. It provides an understanding of the AI ecosystem, including the different types of AI models, algorithms, and techniques. •
Machine Learning (ML) for AI Security: This unit focuses on the application of machine learning in AI security, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. It also covers common machine learning algorithms used in AI security. •
Deep Learning for AI Security: This unit delves into the world of deep learning, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. It covers the application of deep learning in AI security, including image recognition, speech recognition, and natural language processing. •
AI Security Threats and Vulnerabilities: This unit explores the various threats and vulnerabilities associated with AI systems, including adversarial attacks, data poisoning, and model stealing. It also covers the different types of AI security threats, including insider threats, external threats, and physical threats. •
AI Security Frameworks and Standards: This unit covers the various frameworks and standards used in AI security, including NIST Cybersecurity Framework, ISO 27001, and PCI-DSS. It provides an understanding of the different standards and frameworks used in AI security, including data protection, access control, and incident response. •
AI Security Governance and Compliance: This unit focuses on the governance and compliance aspects of AI security, including data governance, access governance, and incident response governance. It covers the different regulations and laws related to AI security, including GDPR, CCPA, and HIPAA. •
AI Security Testing and Evaluation: This unit covers the various testing and evaluation methods used in AI security, including black-box testing, white-box testing, and gray-box testing. It also covers the different evaluation metrics used in AI security, including accuracy, precision, recall, and F1-score. •
AI Security for Cloud and Edge Computing: This unit explores the AI security challenges associated with cloud and edge computing, including data privacy, data protection, and model security. It covers the different security measures used in cloud and edge computing, including encryption, access control, and authentication. •
AI Security for Internet of Things (IoT): This unit focuses on the AI security challenges associated with IoT devices, including device security, data security, and network security. It covers the different security measures used in IoT devices, including encryption, access control, and authentication. •
AI Security for Cybersecurity: This unit covers the various AI security applications used in cybersecurity, including threat detection, incident response, and security information and event management (SIEM). It provides an understanding of the different AI security tools and technologies used in cybersecurity, including machine learning, deep learning, and natural language processing.
Artificial Intelligence (AI) Fundamentals: This unit covers the basics of AI, including machine learning, deep learning, and natural language processing. It provides an understanding of the AI ecosystem, including the different types of AI models, algorithms, and techniques. •
Machine Learning (ML) for AI Security: This unit focuses on the application of machine learning in AI security, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. It also covers common machine learning algorithms used in AI security. •
Deep Learning for AI Security: This unit delves into the world of deep learning, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. It covers the application of deep learning in AI security, including image recognition, speech recognition, and natural language processing. •
AI Security Threats and Vulnerabilities: This unit explores the various threats and vulnerabilities associated with AI systems, including adversarial attacks, data poisoning, and model stealing. It also covers the different types of AI security threats, including insider threats, external threats, and physical threats. •
AI Security Frameworks and Standards: This unit covers the various frameworks and standards used in AI security, including NIST Cybersecurity Framework, ISO 27001, and PCI-DSS. It provides an understanding of the different standards and frameworks used in AI security, including data protection, access control, and incident response. •
AI Security Governance and Compliance: This unit focuses on the governance and compliance aspects of AI security, including data governance, access governance, and incident response governance. It covers the different regulations and laws related to AI security, including GDPR, CCPA, and HIPAA. •
AI Security Testing and Evaluation: This unit covers the various testing and evaluation methods used in AI security, including black-box testing, white-box testing, and gray-box testing. It also covers the different evaluation metrics used in AI security, including accuracy, precision, recall, and F1-score. •
AI Security for Cloud and Edge Computing: This unit explores the AI security challenges associated with cloud and edge computing, including data privacy, data protection, and model security. It covers the different security measures used in cloud and edge computing, including encryption, access control, and authentication. •
AI Security for Internet of Things (IoT): This unit focuses on the AI security challenges associated with IoT devices, including device security, data security, and network security. It covers the different security measures used in IoT devices, including encryption, access control, and authentication. •
AI Security for Cybersecurity: This unit covers the various AI security applications used in cybersecurity, including threat detection, incident response, and security information and event management (SIEM). It provides an understanding of the different AI security tools and technologies used in cybersecurity, including machine learning, deep learning, and natural language processing.
Career path
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
Skills you'll gain
AI Risk Management
Security Compliance
AI Ethical Hacking
Advanced Cryptography
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
ADVANCED CERTIFICATE IN AI SECURITY FOR IT PROFESSIONALS
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.
