The cybersecurity is facing a a landscape of increasingly sophisticated threats. In this context, the Artificial Intelligence (AI) emerges as a an indispensable tool for anticipate, detect and respond to cyberattacks.
The AI course in cybersecurity it immerses you in the advanced applications of machine learning and deep learning models for strengthen digital defences. You will learn how to implement innovative solutions, from the intelligent anomaly detection up to the automation of incident response.
We train you to to lead the transformation of cybersecurity, using cutting-edge platforms and tools. By choosing this course, you will position yourself at the forefront of digital protection, getting ready for the most complex challenges of today’s world.
AI in Cybersecurity
AI Academy
30 hours
Introduction
Objectives
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Understanding advanced AI frameworks for cyber defence and cyber attack.
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Implement AI models for the threat detection and prevention.
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Automating vulnerability management and incident response.
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Ensuring the protection of privacy and data by means of Advanced AI.
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Applying AI to cybersecurity in cloud environments and critical infrastructure.
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Developing cyber resilience strategies AI-assisted.
Table of Contents
TEACHING UNIT 1. FUNDAMENTALS OF AI APPLIED TO CYBERSECURITY
A review of key AI architectures in the context of digital security
Challenges and opportunities presented by AI in system and data protection
Data sources and pre-processing for cybersecurity models
Google Cloud AI as a platform and tool for AI development in cybersecurity
TEACHING UNIT 2. INTELLIGENT THREAT AND ANOMALY DETECTION
Machine learning models for detecting malware and viruses
AI-powered network and user behaviour analysis (UEBA)
Anomaly-based intrusion detection using neural networks
Identification of zero-day attacks and advanced persistent threats (APTs)
Unsupervised learning techniques for the detection of unknown threats
TEACHING UNIT 3. AI FOR VULNERABILITY AND PATCH MANAGEMENT
Prioritising vulnerabilities using predictive models
Automation of the identification and classification of security faults
Predicting exploits and attack surfaces using AI
Optimisation of risk-based patching strategies
AI-assisted static and dynamic code analysis
TEACHING UNIT 4. PUBLIC SAFETY AND EMERGENCY RESPONSE USING AI
Generating adversarial attacks for stress testing
Automation of penetration testing and vulnerability scanning
Development of intelligent honeypots to deceive attackers
Automated incident response and threat containment
Reinforcement learning models for cyber defence strategies
TEACHING UNIT 5. PRIVACY AND DATA PROTECTION WITH AI
Anonymisation and de-identification of sensitive data using AI
AI-assisted data loss prevention (DLP)
Security in the processing of personal data using AI techniques
Differential privacy and homomorphic encryption applied to AI
Intelligent Identity and Access Management (IAM)
TEACHING UNIT 6. AI IN CYBERSECURITY, THE CLOUD AND INFRASTRUCTURE
Threat monitoring and detection in cloud environments
Container and microservice security using AI
Protection of critical infrastructure and industrial control systems (ICS/SCADA)
Risk and compliance analysis in multi-cloud environments
Implementing AI for software supply chain security
TEACHING UNIT 7. CYBER RESILIENCE AND AI-ASSISTED RECOVERY
Assessment of systems’ resilience to cyber-attacks
Planning and simulating crisis scenarios using AI
Automated disaster recovery and system restoration
Digital forensic analysis and persistence detection using AI
Optimisation of post-attack business continuity plans
TEACHING UNIT 8. THE FUTURE OF AI IN CYBERSECURITY
Explainable AI (XAI) in security decision-making
Adversarial attacks against AI models and countermeasures
The role of quantum computing in cybersecurity
Emerging trends at the intersection of AI and cybersecurity
Human-AI collaboration in cybersecurity teams (Google Security Operations)