Microcredential
AI for Cybersecurity & Threat Detection
This microcredential equips IT professionals with a clear, practical understanding of how AI systems work, where they are embedded in enterprise environments, and how to evaluate, use, and govern them safely.
It is AI capability literacy for IT practitioners.
- Duration: 30 hours
- Delivery Mode: Blended (live + guided labs)
- Level: Applied (Level 2)
- Course Fee: $2,500 per participant (including GST)
Microcredential Overview
This microcredential equips IT and cybersecurity professionals with the skills to apply AI-driven tools for threat detection, anomaly identification, automated response, and risk management.
The focus is on applying and evaluating AI tools in security environments, not on building ML models from scratch.
Learning Outcomes
By the end of this microcredential, participants will be able to:
- Explain how AI and machine learning are used in modern cybersecurity systems
- Apply AI-based tools for anomaly detection and threat monitoring
- Evaluate AI-enhanced SIEM and XDR platforms
- Identify risks associated with adversarial AI and model manipulation
- Design a secure AI-assisted threat detection workflow
- Develop an AI integration roadmap for a cybersecurity function
Prerequisite
- Basic understanding of networking, security principles, and
SIEM tools. - Completion of “Practical AI Literacy for IT Professionals” recommended.
Target Audience:
- SOC analysts
- Cybersecurity engineers
- IT security managers
- Network security specialists
Cloud security professionals
Modules and Topics
Module 1: AI in Modern Cybersecurity Landscapes
Topics and activities:
- Evolution from rule-based detection to AI-driven detection
- Supervised vs unsupervised learning in security
- Behavioural analytics
- AI in endpoint, network, and cloud security
- Limitations and false positives
- Mapping the organisation’s current detection stack and identifying where AI is already embedded.
- Develop a security capability gap summary.
Module 2: AI for Threat Detection & Anomaly Analysis
Topics and activities:
- Anomaly detection models
- User and Entity Behaviour Analytics (UEBA)
- AI-driven phishing detection
- Malware classification using ML
- Real-time threat scoring
- Hands-on lab on analysing simulated network logs
- Identify anomalous patterns
- Compare rule-based vs AI-driven detection outcomes
- Developing a threat detection analysis report.
Module 3: AI-Enhanced Security Operations
Topics and activities:
- AI-powered SIEM platforms
- Extended Detection & Response (XDR)
- Automated incident triage
- Security orchestration and automated response (SOAR)
- Using generative AI in SOC reporting
- Building an AI-assisted incident response workflow.
- Develop an incident response workflow diagram.
Module 4: Risks, Governance & Adversarial AI
Topics and activities:
- Adversarial attacks on AI systems
- Data poisoning
- Model evasion techniques
- AI hallucination risks in security tools
- Governance and compliance considerations
- Human-in-the-loop frameworks
- Risk assessment of an AI-powered security tool.
- Developing an AI security risk matrix.
Assessments
All assessments are applied and workplace relevant.
- Assessment 1 – Threat Detection Case Analysis (25%)
- Assessment 2 – AI Tool Evaluation & Risk Assessment (35%)
- Assessment 3 – Capstone: AI Integration Roadmap (40%)
Have Questions About This Course?
We’re here to help! If you have any questions about the course content, admission requirements, or enrollment process, feel free to reach out to us. Our admissions team will be happy to assist you.
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