AI in Cybersecurity – Automation of Penetration Testing and Vulnerability Analysis

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

AI for penetration testing - You will learn how to use AI models to support penetration testing, so you can spot vulnerabilities faster, plan assessments more effectively, and review findings with greater confidence.

Stronger vulnerability detection - You will practice identifying critical web and API flaws, including SQL Injection, XSS, SSRF, CSRF, IDOR, and common weaknesses in authentication and access control.

Source code security analysis - You will learn how to use AI to review source code for dangerous functions, input validation issues, exposed secrets, tokens, and risks hidden in external libraries and dependencies.

Attack surface mapping - You will learn to analyze application architecture, identify technologies, map endpoints and APIs, and highlight the areas that should be prioritized during security testing.

Security test automation - You will see how to generate test scripts with LLMs and Python, build regression checks for discovered issues, and connect security testing with real CI/CD delivery workflows.

Safer API testing - You will master REST API security analysis, authorization checks, JWT and OAuth testing, and the review of API responses to detect weaknesses, anomalies, and unsafe behavior.

Fewer false positives - You will learn how to separate real vulnerabilities from scanner noise, verify findings safely in a test environment, and assess their actual impact on business and technical risk.

Better reports and fixes - You will learn how to create clear penetration test reports, document evidence properly, write practical remediation guidance, and use AI to speed up security documentation work.

Training programme

1. Introduction to AI-supported penetration testing

fundamentals of Ethical Hacking and penetration testing,
the role of artificial intelligence in identifying vulnerabilities,
differences between traditional security tests and AI-supported tests,
stages of penetration testing and work methodology,
OWASP, PTES and NIST standards,
scope of authorized tests and principles of safe testing.

2. AI tools used in security testing

ChatGPT, Claude and LLM models in security analysis,
the use of AI for code and technical documentation analysis,
GitHub Copilot and AI assistants in identifying programming errors,
integration of AI with security tools,
Burp Suite, OWASP ZAP and Nmap – application in the testing process,
automation of the interpretation of scanning results,
limitations of AI models in vulnerability assessment.

3. Application recognition and attack surface identification

analysis of the architecture of web applications,
identification of technologies, frameworks and components,
mapping of entry points and API interfaces,
analysis of application and service configuration,
use of AI for analysis of reconnaissance results,
detection of potentially dangerous configurations,
prioritization of areas requiring testing.

4. Detection of web application vulnerabilities using AI

OWASP Top 10 – the most important application threats,
SQL Injection and database query handling errors,
Cross-Site Scripting (XSS),
Cross-Site Request Forgery (CSRF),
Server-Side Request Forgery (SSRF),
authentication and session management errors,
improper access control and IDOR,
input data validation and processing errors,
use of AI for vulnerability analysis and classification.

5. AI-based source code security analysis

automatic code review for vulnerabilities,
identification of dangerous functions and libraries,
analysis of authentication and authorization mechanisms,
detection of data handling and exception handling errors,
identification of secrets, tokens, and credentials,
analysis of dependencies and external components,
verification of the security of code generated by AI,
generation of proposed fixes and their verification.

6. API Security Testing

REST API security fundamentals,
OWASP API Security Top 10,
analysis of endpoints and HTTP methods,
authorization errors and access to resources,
security of JWT tokens and OAuth mechanisms,
testing validation of parameters and input data,
generating test cases using AI,
analysis of API responses and identification of irregularities.

7. Automation of security testing using AI

generation of test scripts using LLM models,
use of Python to automate security checks,
preparation of regression tests for detected vulnerabilities,
analysis and classification of security scanner results,
automation of report processing,
integration of security tests with CI/CD processes,
use of AI to reduce the number of false alarms,
verification of the correctness and security of scripts generated by AI.

8. Vulnerability verification and risk assessment

distinguishing real vulnerabilities from false positives,
safe verification of vulnerabilities in a test environment,
analysis of scenarios of potential exploitation of vulnerabilities,
assessment of the impact of vulnerabilities on confidentiality, integrity and availability,
risk classification using CVSS,
identification of the root causes of errors,
prioritization of remedial actions.

9. Reporting test results and security recommendations

the structure of a penetration testing report,
documenting vulnerabilities and evidence of their occurrence,
the use of AI to develop technical reports,
preparing reports for developers and administrators,
formulating recommendations for removing vulnerabilities,
generating checklists and remediation plans,
retesting security measures after implementing fixes,
responsible use of AI when developing security reports.

What are the prerequisites for participating in the training?

Web application basics - You should understand how a web application works, including HTTP requests, forms, sessions, and basic login mechanisms, so you can follow the hands-on examples with ease.

Networking and API basics - You should know the basics of network communication, addressing, HTTP methods, and how REST APIs work, so you can analyze traffic, endpoints, and service responses correctly.

Basic programming skills - You should be able to read simple code and understand core programming constructs, because during the training you will review scripts, app fragments, and application logic.

IT security fundamentals - You should know core IT security terms such as vulnerability, authentication, authorization, and risk, so you can better understand testing scenarios and analysis results.