Generative AI for Cybersecurity Domain
This course provides a comprehensive understanding of Generative AI’s applications in Cybersecurity.
- Course fee
- Contact us
- Duration
- 5 days
- Group rate
- Ask an advisor
Course overview
This course provides a comprehensive understanding of Generative AI’s applications in Cybersecurity. Participants will explore how AI-driven models, such as GPT, GANs, and Transformers, can be used for threat detection, anomaly detection, malware analysis, automated security tasks, and adversarial AI.
The course will also cover defensive and offensive AI techniques, ethical considerations, and best practices to ensure responsible AI implementation in cybersecurity. Learners will gain hands-on experience using Python-based AI frameworks and security tools to build AI-driven security solutions.
Training methodology
This training is hands-on and industry-focused, combining:
Instructor-led sessions – Concepts explained with real-world case studies.
Live coding & demonstrations – Implementing AI-powered cybersecurity solutions.
Practical hands-on labs – Using Python, AI libraries, and security tools.
Threat simulation exercises – Applying AI for offensive and defensive security.
Capstone project – Building an AI-driven cybersecurity solution.
Prerequisites
- Basic Python programming knowledge.
- Understanding of Cybersecurity concepts (threat detection, SOC operations).
- Familiarity with Machine Learning & AI basics (optional but beneficial).
Target competencies
- By completing this course, participants will develop expertise in:
- Leveraging AI for cybersecurity (threat detection, SOC automation).
- Building AI-powered security models for anomaly and malware detection.
- Understanding adversarial AI (AI-driven cyberattacks & defenses).
- Automating security operations using AI models.
- Developing responsible AI strategies to mitigate security risks.
Course objectives
Upon completing the course, participants will be able to:
- Understand Generative AI’s role in Cybersecurity.
- Use AI models for threat intelligence and malware detection.
- Implement AI-driven SOC automation for incident response.
- Detect and prevent adversarial AI attacks.
- Build custom AI security tools using Python.
- Address ethical, bias, and regulatory concerns in AI cybersecurity.
Course outline
- Overview of Generative AI & Large Language Models (LLMs).
- How AI is transforming Cybersecurity Operations.
- Real-world use cases: Threat detection, fraud prevention, malware analysis.
- Ethical & regulatory considerations in AI-powered cybersecurity.
- Understanding Threat Intelligence and AI’s role in cybersecurity.
- Using NLP (Natural Language Processing) for cyber threat intelligence.
- AI-driven automation for phishing email detection.
- Hands-on: Building an AI-powered threat intelligence tool.
- Introduction to Anomaly Detection with AI.
- Using AI models to detect unusual behavior in network traffic.
- Implementing AI for Insider Threat Detection.
- Hands-on: Building an AI-based Intrusion Detection System (IDS).
- How Generative AI helps in malware detection & classification.
- Using AI for reverse engineering malware behavior.
- Applying Deep Learning for ransomware detection.
- Hands-on: AI-based malware analysis using Python.
- Understanding Adversarial AI and its threats.
- Generative AI for spear-phishing & deepfake attacks.
- Adversarial examples: How hackers manipulate AI security models.
- Hands-on: Simulating an AI-driven cyberattack.
- Automating Security Operations Centers (SOC) with AI.
- AI-powered log analysis and threat hunting.
- Implementing AI chatbots for automated incident response.
- Hands-on: Building an AI-based Security Incident Response System.
- AI in fraud detection and behavioral analytics.
- Risk scoring using Machine Learning models.
- Detecting financial fraud, account takeovers, and credential stuffing.
- Hands-on: Building an AI-powered fraud detection system.
- Introduction to Deepfake Technology.
- Identifying AI-generated fake images, videos, and voices.
- Using AI for fake news and misinformation detection.
- Hands-on: Building an AI tool to detect deepfakes.
- Automating repetitive cybersecurity tasks using AI.
- AI-powered password cracking and brute-force attack prevention.
- Implementing AI in SIEM (Security Information & Event Management) platforms.
- Hands-on: Automating security alerts with Python-based AI models.
- AI bias and security concerns in Cybersecurity.
- Compliance with GDPR, NIST AI Risk Management Framework, and other standards.
- Best practices for ethical AI use in Cybersecurity.
- Future trends in AI-powered security.
- Final project: Building and deploying an AI-driven cybersecurity application.
- Peer review and evaluation of security models.
- Certification and career guidance in AI & Cybersecurity
Morning and evening classes are available. CompuBase Training Center, Abu Dhabi.
