Autonomous Cyber Defence Training Course
The Autonomous Cyber Defence Training Course provides advanced knowledge and practical skills for designing, deploying, and managing AI-powered autonomous security systems capable of detecting, analysing, responding to, and recovering from modern cyber threats.
Course Overview
Autonomous Cyber Defence Training Course
Introduction
The Autonomous Cyber Defence Training Course provides advanced knowledge and practical skills for designing, deploying, and managing AI-powered autonomous security systems capable of detecting, analysing, responding to, and recovering from modern cyber threats. This course explores autonomous security operations, self-learning defence mechanisms, AI-driven incident response, automated threat hunting, cyber resilience, zero trust architecture, and intelligent security orchestration to strengthen enterprise protection against evolving cyber risks.
Participants will gain hands-on experience with AI cybersecurity platforms, Security Operations Centre (SOC) automation, Extended Detection and Response (XDR), Security Orchestration Automation and Response (SOAR), behavioural analytics, autonomous malware detection, and predictive cyber defence strategies. Through real-world simulations and case studies, learners will understand how autonomous cyber defence technologies are transforming traditional reactive security models into continuous, intelligent, and adaptive defence frameworks that improve detection speed, response efficiency, and organisational cyber resilience.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand the foundations of autonomous cyber defence and AI-driven security architectures.
- Develop skills in machine learning-based threat detection and automated response systems.
- Implement autonomous Security Operations Centre (SOC) capabilities.
- Apply AI-powered threat intelligence and predictive analytics.
- Design self-learning cybersecurity defence mechanisms.
- Configure SOAR automation workflows for incident response optimisation.
- Use behavioural analytics and anomaly detection techniques.
- Understand autonomous agents for cybersecurity operations.
- Apply zero trust security principles with AI automation.
- Analyse and defend against AI-enabled cyber attacks.
- Improve cyber resilience through continuous monitoring and adaptive defence.
- Integrate XDR, SIEM, and automation technologies into security ecosystems.
- Develop strategies for implementing next-generation autonomous security frameworks.
Target Audience
- Cybersecurity Analysts and Engineers
- Security Operations Centre (SOC) Professionals
- Cyber Threat Intelligence Specialists
- Incident Response Teams
- Network Security Administrators
- AI and Machine Learning Engineers
- Cloud Security Professionals
- Cybersecurity Managers and Risk Leaders
Course Modules
Module 1: Foundations of Autonomous Cyber Defence
- Introduction to autonomous cybersecurity concepts and evolution
- AI, ML, and automation in modern cyber defence
- Autonomous security architecture and components
- Human-led vs machine-driven defence approaches
- Cyber resilience through intelligent automation
- Case Study: How major enterprises use AI-driven security platforms to automate threat detection and response.
Module 2: Artificial Intelligence for Cyber Threat Detection
- Machine learning algorithms for identifying cyber threats
- Behaviour-based detection and anomaly analysis
- AI-powered intrusion detection systems
- Predictive analytics for cyber attack prevention
- Deep learning applications in cybersecurity
- Case Study: Using machine learning models to detect abnormal network activity before data breaches occur.
Module 3: Autonomous Security Operations Centre (SOC)
- Building an AI-enhanced SOC environment
- Automated alert analysis and prioritisation
- Intelligent event correlation using AI
- Autonomous investigation workflows
- Human-AI collaboration in SOC operations
- Case Study: Implementation of AI SOC automation to reduce security analyst workload and improve response times.
Module 4: Autonomous Threat Intelligence and Hunting
- AI-driven cyber threat intelligence platforms
- Automated threat discovery and reconnaissance
- Machine learning-based threat hunting
- Real-time intelligence processing
- Predictive attack modelling
- Case Study: Using autonomous threat hunting to identify advanced persistent threats (APTs).
Module 5: Automated Incident Response and Remediation
- Security Orchestration Automation and Response (SOAR)
- Automated containment and recovery processes
- AI-based incident classification
- Digital forensics automation
- Autonomous remediation strategies
- Case Study: Automated ransomware response workflows that isolate infected systems within seconds.
Module 6: Autonomous Defence Against Advanced Cyber Attacks
- AI-enabled malware detection and prevention
- Defence against automated attacks
- Adversarial AI and machine learning security
- Autonomous vulnerability management
- Adaptive defence mechanisms
- Case Study: Protecting enterprise networks from AI-generated phishing and automated exploitation campaigns.
Module 7: Autonomous Cloud and Zero Trust Security
- AI-driven cloud security monitoring
- Autonomous identity and access management
- Zero Trust security automation
- Cloud workload protection
- Continuous security validation
- Case Study: Applying AI-based Zero Trust controls in a multi-cloud enterprise environment.
Module 8: Future of Autonomous Cyber Defence
- Generative AI in cybersecurity operations
- Autonomous cyber agents and security copilots
- Ethical considerations in autonomous defence
- Building cyber defence ecosystems
- Future trends in intelligent security automation
- Case Study: Enterprise adoption of AI security copilots for next-generation cyber operations.
Training Methodology
- Interactive lectures and presentations.
- Group discussions and brainstorming sessions.
- Hands-on exercises using real-world datasets.
- Role-playing and scenario-based simulations.
- Analysis of case studies to bridge theory and practice.
- Peer-to-peer learning and networking.
- Expert-led Q&A sessions.
- Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.