Artificial Intelligence (AI)-Based Cybersecurity Defense Strategies

Why Attend

Security teams face more alerts, more attack surface and faster attackers than any analyst team can handle manually. AI and machine learning are now built into most SIEM, EDR and network security products, but they only help if defenders understand how the models work, where they are blind and how to fit them into existing processes.

This LLMI course takes participants from the fundamentals of AI in security to hands-on use. Day by day, they cover supervised and unsupervised learning for threat detection, deep learning for advanced threat analysis, AI-driven incident response and automation, and finally how to integrate AI into an existing cybersecurity framework, with attention to ethics and law. Each day includes an interactive session in which participants build or plan something concrete: a detection model for a sample threat, an AI-assisted incident response plan, an integration roadmap for their organisation.

The focus is practical judgement: knowing when an AI detection can be trusted, how to tune it to reduce false positives, and how adversaries attempt to evade or poison machine-learning defences.

Course Objectives

Explain how AI and machine learning are applied across detection, analysis and response

Distinguish supervised and unsupervised approaches and choose the right one for a threat scenario

Build and evaluate a simple machine-learning model for cyber threat detection

Describe how deep learning supports malware, phishing and network anomaly analysis

Design an AI-assisted incident response plan with clear human decision points

Recognise adversarial attacks on AI systems and plan mitigations

Integrate AI capabilities into existing security frameworks and tooling

Address the legal, privacy and ethical issues of AI-based monitoring

Target Audience

SOC analysts and security operations managers

Cybersecurity engineers and architects

IT managers and system administrators

Incident response and threat intelligence teams

Data scientists moving into security analytics

Information security, risk and compliance officers

Course Methodology

Technical briefings supported by live demonstrations

Case studies of AI-detected and AI-missed attacks

Daily interactive build sessions on detection models and response plans

Group discussion of tooling choices, false-positive tuning and governance

An AI integration plan for each participant's organisation