The AI in Cyber Security (AICS) course provides practical knowledge of how Artificial Intelligence and Machine Learning are transforming modern cyber security. Designed for security professionals, SOC teams, IT leaders, and red and blue team practitioners, the course explores how AI can help organisations move from reactive security operations towards faster, more proactive, and intelligent defence.
AI is increasingly being used across the cyber security lifecycle, from threat detection and malware analysis to incident response, digital forensics, threat hunting, and security monitoring. This course helps learners understand these applications while also examining the limitations, risks, ethical considerations, and regulatory requirements associated with using AI in security environments.
The programme includes 10 comprehensive modules and hands-on practical labs, giving learners the opportunity to move beyond theory and explore real-world applications of AI in cyber security. Participants can build a simple anomaly detection model, deploy an AI-powered intrusion detection system, and analyse real-world cyber attacks.
The course also covers adversarial AI, helping learners understand how attackers can target AI systems through techniques such as poisoning and evasion attacks. This provides an important perspective for security professionals who need to protect AI-enabled environments and understand the changing threat landscape.
Responsible and compliant AI use is another important component of the course. Learners explore GDPR, CCPA, transparency, explainability, the NIST Cybersecurity Framework, and ISO/IEC 27001, helping them understand the governance and regulatory considerations involved in deploying AI for cyber security.
Whether you are working in a Security Operations Centre, leading an IT or security function, or participating in offensive and defensive security exercises, AICS provides practical knowledge to help you understand and apply AI across modern cyber security operations.
Why Should You Attend?
- Understand how AI and machine learning are changing cyber security.
- Learn how AI can improve real-time threat detection.
- Explore AI-powered malware and behavioural analysis.
- Learn how to analyse network traffic using AI techniques.
- Understand AI applications in intrusion detection.
- Explore AI-assisted incident response and management.
- Learn how AI can support digital forensics.
- Understand AI-enhanced SIEM, event correlation, and incident prediction.
- Explore AI-powered proactive threat hunting.
- Understand adversarial AI techniques and defensive strategies.
- Identify the limitations and challenges of AI in cyber security.
- Develop awareness of AI bias, data quality, scalability, and explainability.
- Understand ethical, legal, and regulatory considerations.
- Explore NIST and ISO/IEC 27001 approaches to responsible AI security.
- Gain practical experience through hands-on labs and real-world scenarios.
Who Should Attend?
The course is suitable for professionals who want to understand and apply AI within cyber security, including:
- Security Analysts
- SOC Analysts and SOC Teams
- Cyber Security Professionals
- Information Security Professionals
- IT Security Professionals
- CISOs and Security Leaders
- IT Managers and Directors
- Red Team Practitioners
- Blue Team Practitioners
- Threat Intelligence Professionals
- Incident Response Professionals
- Digital Forensics Professionals
- Security Engineers
- AI and Security Professionals
The course is particularly valuable for professionals who want to use AI to detect threats faster, automate security operations, improve incident response, and strengthen proactive defence capabilities.
Learning Objectives:
After completing the course, participants will be able to:
- Explain the fundamentals of AI and its role in cyber security.
- Understand key machine learning techniques used in security.
- Identify AI applications across the cyber security lifecycle.
- Apply AI concepts to threat detection.
- Understand anomaly-based intrusion detection.
- Explore AI-driven malware and behavioural analysis.
- Analyse network traffic for suspicious activity.
- Understand AI applications in incident response.
- Explore AI-assisted digital forensics.
- Understand AI-enhanced SIEM and event correlation.
- Identify challenges and limitations associated with AI in security.
- Understand adversarial AI attacks and defensive approaches.
- Recognise ethical and legal considerations when deploying AI.
- Understand data quality, bias, transparency, and explainability issues.
- Apply relevant cyber security frameworks and standards.
- Explore future trends and emerging technologies in AI-powered cyber security.
Course Modules:
Module 1: Introduction to AI in Cyber Security
Understand the relationship between Artificial Intelligence and cyber security and explore how AI is changing traditional approaches to protecting digital environments.
Module 2: Fundamentals of Cyber Security
Build a foundation in key cyber security concepts and understand the security challenges that AI-powered technologies can help address.
Module 3: Machine Learning Techniques in Cyber Security
Explore machine learning techniques and their applications in security monitoring, classification, anomaly detection, and threat identification.
Module 4: AI-Powered Threat Detection
Learn how AI can detect suspicious activity, identify anomalies, analyse malware behaviour, and examine network traffic for threats such as DDoS attacks, data exfiltration, and botnet activity.
Module 5: AI in Incident Response & Management
Explore how AI can support incident response through automated playbooks, data collection, log analysis, evidence analysis, event correlation, and incident prediction.
Module 6: Challenges & Limitations of AI in Cyber Security
Understand the practical challenges of implementing AI in security environments, including data quality, scalability, bias, reliability, and other limitations.
Module 7: Ethical & Legal Considerations
Explore responsible AI adoption and understand areas such as GDPR, CCPA, transparency, explainability, governance, and accountability.
Module 8: Future Trends & Emerging Technologies
Explore emerging developments in AI and cyber security and understand how new technologies may influence the future of security operations.
Module 9: Hands-On Labs & Practical Exercises
Apply your knowledge through practical exercises, including building an anomaly detection model, deploying an AI-powered intrusion detection system, and analysing real-world cyber attacks such as WannaCry and SolarWinds.
Module 10: Conclusion & Next Steps
Consolidate your knowledge and identify opportunities to continue developing AI-powered cyber security capabilities in professional environments.
Hands-On Practical Learning
AICS places a strong emphasis on practical application. Learners can work through hands-on activities designed to demonstrate how AI techniques can be applied to real security challenges.
Practical activities include:
- Building an anomaly detection model using security data.
- Deploying an AI-powered intrusion detection system.
- Analysing real-world cyber attacks.
- Exploring AI-driven threat detection.
- Applying security frameworks to AI deployment.
- Examining defensive approaches against adversarial AI.
AI-Powered Threat Detection
Traditional security approaches can struggle to keep pace with the volume and complexity of modern cyber threats. AI can help security teams analyse large amounts of security data, identify unusual patterns, detect potential threats, and support proactive threat hunting.
The course explores AI applications including anomaly-based intrusion detection, behavioural malware analysis, network traffic analysis, DDoS detection, botnet identification, and data exfiltration monitoring.
Incident Response & Digital Forensics
AI can also support security teams after an incident occurs. The course explores how AI can assist with automated response playbooks, data collection, log and evidence analysis, event correlation, and incident prediction.
These capabilities can help security teams improve response times while allowing analysts to focus on investigation, decision-making, and more complex security events.
Adversarial AI
As organisations increasingly adopt AI, attackers are also developing methods to target AI-enabled systems. The course introduces adversarial AI, including poisoning and evasion attacks, and explores strategies for defending AI systems against these threats.
Learners also consider challenges involving data quality, bias, scalability, and the reliability of AI-driven security solutions.
Ethical, Legal & Regulatory Considerations
AI-powered cyber security must be implemented responsibly. AICS explores important considerations around privacy, transparency, explainability, governance, and regulatory compliance.
Learners are introduced to GDPR, CCPA, the NIST Cybersecurity Framework, and ISO/IEC 27001, helping them understand how established frameworks and regulatory requirements can support responsible AI deployment in cyber security.
Career Relevance
The skills covered in this course can support professionals working across:
- Cyber Security
- Information Security
- Security Operations
- Threat Detection
- Incident Response
- Digital Forensics
- Threat Intelligence
- Security Engineering
- AI Security
- Risk and Compliance
- IT Security Management
