AI can scan a network in seconds, draft an exploit concept, analyze massive logs, and accelerate security research before you've finished your coffee. It can also hallucinate a CVE, miss a critical business-logic flaw, and lead an autonomous agent straight out of scope — all while sounding completely confident.

Most security content picks a side: either "AI will replace penetration testers" hype or "ignore AI and do it properly" gatekeeping. Neither approach is useful on a real engagement.

Modern Ethical Hacking & Penetration Testing with Artificial Intelligence, written by Divine Chukwu, is the practical handbook for security professionals who need both speed and judgment on the same engagement.

Drawing from the perspective of a software security architect, cybersecurity professional, and practitioner working at the intersection of offensive security and artificial intelligence, Divine presents a structured approach to using AI as a security testing accelerator — without allowing it to replace human verification, professional judgment, or ethical responsibility.

What's inside

Part I — Ground Rules
Authorization, scope, contracts, ethical responsibilities, and the data-discipline rules that keep sensitive client information out of the wrong AI model.
Part II — The AI-Augmented Engagement
The complete penetration-testing lifecycle — from reconnaissance and enumeration to exploitation and reporting — with AI placed exactly where it provides value and clearly restricted where it does not.
Part III — Prompt Engineering for Security Work
A reusable security prompt library and a verification discipline designed to catch hallucinated findings before they reach a client.
Part IV — Building Your AI Security Toolchain
Local vs. hosted models, secure AI workflows, automation pipelines, and practical guardrails for autonomous security agents.
Part V — Testing AI Systems Themselves
The OWASP LLM Top 10 in practice, AI red-teaming methodology, prompt injection testing, and a complete worked engagement involving an AI security weakness and real-world business impact.
Part VI — Reference Playbooks
Practical web, API, network, cloud, and AI testing checklists designed for real engagements.
Part VII — Practice, Proof & Career
A structured 12-week study path, bug bounty strategy, portfolio development, and guidance for building a career in the rapidly growing AI security field.
Part VIII — The Lab Workbook
18 guided and progressively challenging labs — from your first security testing environment to a complete self-directed mock engagement.
Appendices A–I
A signable Rules of Engagement template, AI usage policy, pre-engagement checklists, reference materials, and complete sample findings written to professional client standards.

The principle behind the entire book

Every technique in this handbook follows one fundamental rule:

Nothing generated by an AI model reaches a client, a live target, or a security report until it has been independently verified against evidence outside the model.

That is the difference between a security professional using AI intelligently and someone quietly laundering a language model's guesses into a security engagement.

Whether you're beginning your cybersecurity journey, following the twelve-week learning path, working as a penetration tester, or looking to specialize in the emerging field of AI security and AI red teaming, this handbook gives you a practical framework for working faster without compromising accuracy, safety, or professional standards.

Divine Chukwu is a software security architect, cybersecurity professional, software engineer, and technology entrepreneur with extensive experience building and securing modern software systems. His work spans cybersecurity, backend engineering, artificial intelligence, and security-focused technology solutions.

Through devwalls, Divine focuses on advancing practical security testing, cyber defence, AI security, and security education for professionals and organizations worldwide.

Published by devwalls — Security Testing · Defence · Advisory, delivered worldwide.

Quick Overview

124 pages
8 parts
31 chapters
18 hands-on labs with a progress tracker
Full AI security prompt library for every engagement phase
Complete AI red-teaming methodology
OWASP LLM Top 10 in practical context
Rules of Engagement and AI usage policy templates
Professional sample security findings
12-week cybersecurity and AI security study path
Bug bounty and portfolio strategy
Tool-agnostic methodology designed to remain relevant as AI evolves
From your first command to a full security engagement — and from traditional penetration testing to the AI systems now entering the attack surface.