Operational Cybersecurity with AI: From Vision to Threat Hunting by Pearson

Course details

This course is designed to help cybersecurity professionals modernize enterprise defense by integrating AI, automation, and data-driven practices into security operations. Explore foundational concepts in cyber strategy and governance and how both relate to AI fundamentals and cybersecurity. Examine the evolving AI threat landscape and learn how to measure the effectiveness of security operations through meaningful metrics and dashboards. The course emphasizes practical application, guiding you through cyber threat intelligence (CTI), threat hunting, and detection engineering enhanced by AI-driven automation. Through real-world case studies and hands-on guidance, you can develop skills needed to design scalable cyber operations and improve operational visibility. The course concludes by covering forward-looking topics such as predictive threat modeling and human-machine collaboration in security operations centers (SOCs), preparing you to adapt to the future of cyber defense.

Learning objectives
Develop enterprise cyber operations strategies and governance frameworks.
Differentiate between AI, machine learning, and automation in cybersecurity.
Analyze the AI attack surface and identify AI-driven threats.
Build dashboards for operational, tactical, and strategic insights.
Create and manage cyber threat intelligence (CTI) programs.
Apply AI to threat hunting and detection engineering workflows.
Measure cybersecurity effectiveness using relevant metrics.
Describe predictive modeling and future SOC capabilities.

Concepts

Introduction

Operational cybersecurity with AI - Introduction

Vision, Mission, and Strategy of Modern Cyber Operations

Learning objectives
Defining a security operations center (SOC)
SOC landscape
SOC mission
SOC vision
SOC strategic pillars
Enablers for success
Call to action
Lab

AI in Cybersecurity

Learning objectives
The history of AI development
Governance, ethics, and responsible AI in cybersecurity
AI and the SOC
Threat actors and AI
AI and prompt engineering
Deepfakes
Deepfake detection
Lab

AI Attack Surface and Threat Landscape

Learning objectives
AI attack surface
Data poisoning
Model theft and inversion
Prompt injection and retrieval-augmented generation (RAG) attacks
AI agents and autonomous risks
Hugging Face and the supply chain
Secure use of third-party AI
Comprehensive AI security
Lab

Metrics and Measurements

Learning objectives
An overview of metrics
Logging the right data
Tactical metrics
Operational metrics
Strategic metrics
Lab

Cyber Threat Intelligence

Learning objectives
Introduction to cyber threat intelligence (CTI)
Building a CTI program
Tools and platforms for CTI
Operationalizing CTI
CTI challenges and best practices
Lab

Threat Hunting and Detection Engineering

Learning objectives
Building a threat hunting program
Evolution of cyber threats
Fundamentals of threat hunting - Moving beyond reactive security
Detection engineering essentials
Metrics and KPIs
AI and automation in hunting and detection
Challenges and common pitfalls
Lab

Future Trends and AI-Ready SOC

Learning objectives
Future of SOCs and AI - What s next
Tools for building a modern SOC
Action plan for an AI-ready SOC
Lab

Summary

Operational cybersecurity with AI - Summary