Mastering AI Security

Name: Mastering AI Security
Rating: 4.2 (22 reviews)

A Comprehensive Guide to AI Security

Created byHanniel H. Jafaru

Last updated 9/2024

English

English [Auto],

What you'll learn

Design and implement security measures tailored to the unique challenges of AI systems.
Identify and assess potential security threats and vulnerabilities in AI systems.
Implement robust data security measures and privacy-preserving techniques to protect AI datasets and ensure compliance with privacy standards.
Apply best practices for secure AI development, deployment, and monitoring.

Course content

5 sections • 14 lectures • 1h 50m total length

Intersection between Cybersecurity and AI9:06
Explore how artificial intelligence intersects with cybersecurity, addressing AI-driven threats like autonomous attacks and malware, and leveraging AI for threat detection, incident response, and secure AI development.
Security Concerns in AI Systems7:22
Explore AI security by examining algorithm explainability and biases, data privacy and poisoning, model theft and adversarial attacks, insider risks, regulatory compliance, and ethical, fair automated decision making.
CIA Triad in AI Systems6:01
Apply the CIA triad to AI systems by safeguarding data, models, and algorithms against unauthorized access, tampering, and disruption in real-time applications.

AI Attack Threats7:28
Identify AI attack threats, including adversarial attack, bias exploitation, supply chain attack, model inversion, backdoor attack, jailbreaking, evasion attack, model extraction, DDoS, prompt ingestion, and data poisoning.
Sources of Vulnerabilities in AI Systems - Part 110:36
Explore the sources of vulnerabilities in AI systems, including data quality, third-party dependencies, model obsolescence and drift, overfitting and memorization, data quantity, bias, transparency, and data integrity.
Sources of Vulnerabilities in AI Systems - Part 22:11
Identify vulnerabilities in ai systems, including exposure of sensitive data, differential privacy and data anonymization, secure deployment, access controls, and audits.

Adversarial Attack4:51
Expose the vulnerabilities of AI systems by showing how adversarial attacks subtly perturb inputs to mislead models, enabling targeted or non-targeted misclassifications across images, text, and audio.
Adversarial Training4:19
Adversarial training strengthens AI model robustness by exposing it to adversarial examples during training, then validating against unseen attacks to improve generalization.
Strategies for Mitigating Adversarial Attacks5:57
Learn robust model training with data augmentation and adversarial training, defense distillation, input pre-processing, and detection mechanisms to shield AI models from adversarial threats.

Data Privacy Techniques in AI7:59
Explore essential privacy techniques for AI training, including data anonymization, data minimization, informed consent, data retention policies, data segregation and classification, privacy by design, third-party sharing, and pseudonymisation.
Techniques for Data Integrity11:19
Master techniques for AI data integrity, including encryption for data at rest and in transit, versioning, auditing, checksums, hashing, redundancy, access control, and data lineage.

AI Security Best Practices - Part 111:00
Explore data pre-processing, secure storage, and continuous monitoring to build trustworthy AI systems. Learn bias mitigation, explainability, human oversight, and regulatory compliance across the AI lifecycle.
AI Security Best Practices - Part 29:53
Engage stakeholders and experts to align AI with needs and ethics, using transparent communication, look back system, version control, reproducibility, logging, api security, and privacy by design.
Future Security Trend in AI12:48
Explore future AI security trends, including AI driven cyber security defense, adversarial AI resilience, explainable AI, AI governance, supply chain security, federated learning, and zero trust.

Requirements

No programming experience is required for this course

Description

Artificial Intelligence (AI) is transforming industries and reshaping the future. However, with this rapid advancement comes a new frontier of security challenges. AI systems are not immune to threats—adversarial attacks, data breaches, and exploitation of vulnerabilities increasingly target them. Securing AI models, data, and infrastructure has never been more critical.

This course will teach you how to safeguard AI systems from emerging cybersecurity threats. Whether you're a cybersecurity professional, AI engineer, or tech enthusiast, this course is designed to equip you with the skills to identify and defend against the unique risks AI poses.

Through real-world examples and hands-on strategies, you’ll discover:

The Intersection of AI and Cybersecurity: Understand how traditional cybersecurity principles like the CIA Triad (Confidentiality, Integrity, Availability) apply to AI.
AI-Specific Threats and Vulnerabilities: Learn about threats targeting AI models, including data poisoning, adversarial attacks, and vulnerabilities within AI ecosystems.
Adversarial Attacks and Defense Mechanisms: Dive deep into adversarial attacks on AI systems and explore cutting-edge strategies for mitigating these risks.
Data Integrity and Privacy in AI: Master the techniques for protecting the integrity of your AI data using encryption, blockchain, and privacy-preserving methods.
AI Security Best Practices: Explore the latest best practices for developing, deploying, and monitoring secure AI systems, and stay ahead of future AI security trends.

In a world where AI is becoming integral to business and society, understanding how to protect these systems from malicious actors is essential. This course offers a deep dive into the intersection of AI and cybersecurity, empowering you to create robust, secure AI systems and defend against the evolving landscape of AI threats.

Who this course is for:

Cybersecurity Professionals looking to expand their expertise into the specific realm of AI security, understanding how traditional cybersecurity principles apply to AI technologies.
AI Engineers and Developers involved in designing, developing, and deploying AI models who need to ensure their systems are secure from adversarial threats and data breaches.
IT & Security Managers responsible for overseeing IT and security teams who need to understand the risks and best practices related to AI security.
Tech Enthuasiast who want to gain a comprehensive understanding of AI security issues and solutions.
Founders and Tech Entrepreneurs developing AI-driven products and services who need to ensure their innovations are secure and compliant with industry standards.

Mastering AI Security

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 22min

AI Threats & Vulnerabilities3 lectures • 20min

Adversarial Attack3 lectures • 15min

Data Security and Privacy in AI Systems2 lectures • 19min

AI Security Best Practices and Future Security Trend3 lectures • 34min

Requirements

Description

Who this course is for: