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Darius Bufnea,
Laura Dioșan,
and
Adrian Sterca
Students will gain a solid understanding of cybersecurity fundamentals, including common attack vectors and vulnerabilities, as well as an introduction to the defense mechanisms used to protect systems. In the first parts, the course presents the core AI techniques applicable to cybersecurity from a theoretical point of view, with a focus on machine learning (ML) methods such as supervised, unsupervised and reinforcement learning.
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Students will gain a solid understanding of cybersecurity fundamentals, including common attack vectors and vulnerabilities, as well as an introduction to the defense mechanisms used to protect systems. In the first parts, the course presents the core AI techniques applicable to cybersecurity from a theoretical point of view, with a focus on machine learning (ML) methods such as supervised, unsupervised and reinforcement learning.
Read more
Students will gain a solid understanding of cybersecurity fundamentals, including common attack vectors and vulnerabilities, as well as an introduction to the defense mechanisms used to protect systems. In the first parts, the course presents the core AI techniques applicable to cybersecurity from a theoretical point of view, with a focus on machine learning (ML) methods such as supervised, unsupervised and reinforcement learning.
In subsequent parts, students will learn how to apply ML techniques practically to specific cybersecurity challenges like malware detection and classification, intrusion detection, and email spam filtering. They will explore the process of implementing ML models for these tasks, training and evaluating them on data using the Python programming language.
Overall, this course equips students with both the theoretical understanding and practical skills needed to apply AI methods in order to protect systems against evolving cyber threats.
Register for this course and see more details by visiting:
OpenCourser.com/course/i2pl0f/ai
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What's inside
Syllabus
Introduction to AI and Cybersecurity
Welcome to the introductory part of the AI and Cybersecurity course! During the 5 video lectures and 2 readings of this module you will find various definitions of the Artificial Intelligence, the evolution of this domain and the classification of the AI algorithms in search-based algorithms and intelligent systems. The domains where AI is successfully used are presented, with focus on the use of AI in cybersecurity related tasks (e.g.: network analysis, intrusion detection, malicious web link detection, anomaly detection or malware classification ). Afterwards, the basic concepts of cybersecurity will be introduced, and the classification of security threats at endpoint level or internet level. You will discover types of cybersecurity threats and how they can be defended.
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Syllabus
Introduction to AI and Cybersecurity
Welcome to the introductory part of the AI and Cybersecurity course! During the 5 video lectures and 2 readings of this module you will find various definitions of the Artificial Intelligence, the evolution of this domain and the classification of the AI algorithms in search-based algorithms and intelligent systems. The domains where AI is successfully used are presented, with focus on the use of AI in cybersecurity related tasks (e.g.: network analysis, intrusion detection, malicious web link detection, anomaly detection or malware classification ). Afterwards, the basic concepts of cybersecurity will be introduced, and the classification of security threats at endpoint level or internet level. You will discover types of cybersecurity threats and how they can be defended.
Read more
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AI for Cybersecurity.
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Career center
Learners who complete AI for Cybersecurity will develop knowledge and skills
that may be useful to these careers:
Security Data Scientist
Security Data Scientist
A Security Data Scientist leverages data science and machine learning to develop advanced solutions for cybersecurity challenges. This AI for Cybersecurity course is exceptionally well-suited for this role, as it focuses on applying machine learning techniques practically to specific cybersecurity challenges such as malware detection, intrusion detection, and email spam filtering. Learners will explore implementing, training, and evaluating ML models on data using Python, providing the exact theoretical understanding and practical skills needed for a Security Data Scientist.
Machine Learning Engineer Cybersecurity
Machine Learning Engineer Cybersecurity
A Machine Learning Engineer Cybersecurity designs, develops, and deploys machine learning models specifically tailored for security applications. This AI for Cybersecurity course provides the core competencies required, covering fundamental ML techniques—supervised, unsupervised, and reinforcement learning—and their practical application. Learners gain hands-on experience implementing, training, and evaluating ML models in Python for critical cybersecurity tasks like malware detection and intrusion detection, essential for success as a Machine Learning Engineer Cybersecurity.
Intrusion Detection Specialist
Intrusion Detection Specialist
An Intrusion Detection Specialist monitors systems and networks for unauthorized access and malicious activity, responding to identified threats. This AI for Cybersecurity course is exceptionally well-suited as it focuses explicitly on the application of machine learning techniques for intrusion detection. Learners gain practical skills in implementing and evaluating ML models to identify suspicious patterns, which is a fundamental responsibility for an Intrusion Detection Specialist ensuring the continuous security of digital environments.
Cybersecurity Analyst
Cybersecurity Analyst
A Cybersecurity Analyst identifies, assesses, and mitigates cyber threats to an organization's systems and data. This comprehensive AI for Cybersecurity course directly prepares learners by providing a solid understanding of cybersecurity fundamentals, including common attack vectors and defense mechanisms. Learners gain practical skills in applying machine learning methods for tasks like intrusion detection and malware classification, which are core responsibilities of a Cybersecurity Analyst. The course's focus on using AI to protect systems against evolving cyber threats is highly relevant for effective threat analysis and response.
Malware Analyst
Malware Analyst
A Malware Analyst investigates malicious software, understanding its behavior, origins, and impact, and develops countermeasures. This AI for Cybersecurity course directly addresses a core aspect of this role by teaching learners how to apply machine learning techniques for malware detection and classification. The course provides practical skills in implementing, training, and evaluating ML models for these tasks using Python, equipping future Malware Analyst professionals with cutting-edge tools to combat evolving threats.
Threat Intelligence Analyst
Threat Intelligence Analyst
A Threat Intelligence Analyst gathers and analyzes information on cyber threats to predict and prevent attacks. This course is highly relevant, introducing learners to AI applications in network analysis, intrusion detection, anomaly detection, and malware classification, all crucial for understanding actor tactics. The material on malicious web link detection and Domain Generation Algorithms further strengthens the ability to identify emerging threats. This foundational knowledge helps a Threat Intelligence Analyst in proactively informing defensive strategies.
Incident Response Analyst
Incident Response Analyst
An Incident Response Analyst is at the forefront of detecting, analyzing, and responding to cybersecurity breaches and incidents. This AI for Cybersecurity course directly enhances these capabilities by providing practical skills in applying machine learning techniques for intrusion detection and anomaly detection. These abilities are crucial for rapidly identifying indicators of compromise, speeding up investigation, and enabling an Incident Response Analyst to effectively mitigate damage from evolving cyber threats.
Network Security Engineer
Network Security Engineer
A Network Security Engineer designs, implements, and manages security solutions to protect an organization's network infrastructure. This AI for Cybersecurity course is highly relevant, as it explicitly discusses the distinction between host and network security and introduces AI applications in network analysis and intrusion detection. Learners gain both theoretical understanding and practical skills in applying AI methods to safeguard networks against persistent and evolving cyber threats, which is a key responsibility for a Network Security Engineer.
Security Engineer
Security Engineer
A Security Engineer designs, builds, and maintains robust security systems and infrastructure. This course provides a strong foundation by introducing cybersecurity fundamentals, including common attack vectors and defense mechanisms. Crucially, it equips learners with both the theoretical understanding and practical skills to apply AI methods, most specifically machine learning, to protect systems against evolving cyber threats, which is increasingly vital for a modern Security Engineer.
AI Ethics and Governance Specialist
AI Ethics and Governance Specialist
An AI Ethics and Governance Specialist ensures that artificial intelligence systems are developed and used responsibly, adhering to ethical standards and legal frameworks. The AI for Cybersecurity course's final module directly addresses these critical aspects, exploring ethical challenges, legal frameworks like the EU AI Act, and introducing the AI4People framework. This specialized knowledge is foundational for an AI Ethics and Governance Specialist navigating the complex intersection of AI, security, and societal impact. This role typically requires an advanced degree.
Security Consultant
Security Consultant
A Security Consultant advises organizations on strategies to enhance their cybersecurity posture and mitigate risks. This AI for Cybersecurity course, by equipping learners with a solid understanding of cybersecurity fundamentals and practical skills in applying AI methods, most specifically machine learning, to protect systems, may be useful. Consultants can leverage this knowledge to recommend advanced AI-driven security solutions and articulate how to defend against evolving cyber threats, supporting a Security Consultant's client engagements.
Cloud Security Engineer
Cloud Security Engineer
A Cloud Security Engineer protects data, applications, and infrastructure within cloud environments. While this course does not specifically mention cloud security, its comprehensive introduction to applying AI methods, such as machine learning for anomaly and intrusion detection, may be useful for securing cloud resources. The ability to understand attack vectors and implement AI-driven defense mechanisms can be transferred to the unique challenges faced by a Cloud Security Engineer.
Application Security Engineer
Application Security Engineer
An Application Security Engineer focuses on protecting software applications from design to deployment. While not exclusively centered on application code, the AI for Cybersecurity course’s focus on detecting malicious web links, understanding attack vectors, and applying AI for anomaly detection may be useful for identifying vulnerabilities within applications or securing their interactions. The skills in leveraging machine learning to identify threats can aid in building more resilient and secure software for an Application Security Engineer.
Digital Forensic Investigator
Digital Forensic Investigator
A Digital Forensic Investigator collects, analyzes, and preserves digital evidence following cyber incidents. The AI for Cybersecurity course, with its focus on machine learning for anomaly detection and understanding various cyber threats, may be useful for efficiently processing large datasets and identifying suspicious activities. This analytical foundation can help a Digital Forensic Investigator piece together attack timelines and identify compromised systems more effectively. This role typically requires an advanced degree.
DevSecOps Engineer
DevSecOps Engineer
A DevSecOps Engineer integrates security practices into every phase of the software development and operations lifecycle, promoting automated and continuous security. The AI for Cybersecurity course’s focus on applying AI techniques for detecting and mitigating online threats, such as malicious web links or potentially vulnerable code patterns, may be useful. This helps a DevSecOps Engineer build tools and processes that proactively identify and address security concerns early in the pipeline, enhancing overall system resilience.
For more career information including salaries, visit:
OpenCourser.com/course/i2pl0f/ai
Reading list
We haven't picked any books for this reading list yet.
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A highly cited and influential book that focuses on deep learning, a subfield of AI concerned with constructing models for complex data. Covers theoretical concepts, popular algorithms, and practical applications.
A textbook that presents AI from a computational perspective, covering topics such as agents, knowledge representation, reasoning, and planning. Suitable for readers with a background in computer science or mathematics.
A classic textbook on reinforcement learning, a subfield of AI concerned with learning from interaction with the environment. Covers both theoretical concepts and practical algorithms, with a focus on real-world applications.
A practical guide to natural language processing (NLP) using Python, covering topics such as text classification, sentiment analysis, and machine translation. Suitable for beginners with some programming experience.
A free online book that provides a gentle introduction to machine learning, covering topics such as linear regression, logistic regression, and neural networks. Suitable for beginners with no prior knowledge of AI or machine learning.
A comprehensive textbook that covers probabilistic graphical models (PGMs), a powerful tool for representing and reasoning about complex systems. Suitable for advanced learners with a background in probability and statistics.
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A non-technical book that explores the global AI landscape, the impact of AI on society, and the ethical challenges that arise from the rapid development of AI technologies.
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A thought-provoking book that explores the potential implications of AI for human society, such as the possibility of a technological singularity and the need for new ethical frameworks.
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A popular science book that discusses the development of AI algorithms and their potential impact on various aspects of human life, such as healthcare, transportation, and finance.
A short but powerful book that explores the potential benefits and risks of AI, as well as the ethical dilemmas that need to be addressed as AI becomes more advanced.
A comprehensive German-language textbook that provides a broad overview of AI, covering topics such as search, knowledge representation, and machine learning. Suitable for both beginners and advanced learners.
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A French-language textbook that focuses on machine learning, a subfield of AI. Covers topics such as supervised learning, unsupervised learning, and deep learning. Suitable for beginners with some programming experience.
Provides a comprehensive overview of cybersecurity, covering the threats, risks, and challenges facing governments and businesses. It is written by two former Secretaries of Homeland Security and provides insights from their experience in leading the nation's cybersecurity efforts.
Provides a practical guide to cybersecurity for businesses. It covers a wide range of topics, including risk assessment, threat intelligence, and incident response. It is written by a cybersecurity expert with over 15 years of experience.
Provides a comprehensive guide to web application security. It covers a wide range of topics, from basic web application security principles to advanced exploitation techniques. It is written by two cybersecurity experts with over 15 years of experience.
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Provides a comprehensive overview of computer security. It covers a wide range of topics, from basic security principles to advanced topics such as cryptography and network security. It is written by a renowned cybersecurity expert with over 40 years of experience.
Provides a practical guide to cybersecurity for beginners. It covers a wide range of topics, from basic cybersecurity principles to specific threats such as phishing and malware. It is written by a cybersecurity expert with over 10 years of experience.
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Provides a comprehensive overview of security engineering. It covers a wide range of topics, from basic security principles to advanced topics such as cryptography and network security. It is written by a renowned security expert with over 30 years of experience.
Provides a comprehensive overview of network security. It covers a wide range of topics, from basic security principles to advanced topics such as firewalls, intrusion detection systems, and virtual private networks. It is written by a renowned security expert with over 40 years of experience.
A comprehensive textbook that provides a broad overview of the field, covering topics such as problem-solving, learning, machine learning, and natural language processing. Suitable for both beginners and advanced learners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i2pl0f/ai
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Similar courses
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Level
Intermediate
Via
Coursera
Institution
EIT Digital
Instructors
Darius Bufnea
Laura Dioșan
Adrian Sterca
Session
November 10, 2025
- December 15, 2025
Language
English
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