Basics of AI with cybersecurity

Learning Objective – The goal of this module is to gain basic understanding of cybersecurity with AI, its importance in real-world scenarios, various platforms to learn about AI, applications of cybersecurity with AI in day-to-day examples, applying AI to the basic security hygiene which the engineers should keep in mind for an application development and speeding up the operation.

Audience – Any intermediate level professional in cybersecurity/software development/solutions architecture/devsecops

Pre-Requisite: Prerequisites include understanding cybersecurity fundamentals encompassing the CIA triad (confidentiality, integrity, availability), recognizing common threats, attacks, and best practices. Familiarity with networking basics like TCP/IP, DNS, DHCP, and HTTP, including network security essentials and data transmission principles, is essential. Proficiency in web development basics such as HTML, CSS, and JavaScript, along with comprehension of web application workings and vulnerabilities like XSS, CSRF, and SQL injection is crucial. Additionally, knowledge of common attack scenarios spanning application, network, and operating system domains is necessary. Secure coding practices involving input validation, output encoding, and parameterized queries to mitigate risks should be understood, along with adopting secure software development lifecycle (SDLC) practices like automated security testing, code review, and vulnerability management throughout development.

Fundamentals of Artificial Intelligence (4 hours)

· Machine Learning for Cybersecurity

· Deep Learning for Cybersecurity

· Natural Language Processing (NLP) in Cybersecurity

· Computer Vision in Cybersecurity

Introduction to Large Language Models (10 hours)

· Definition of LLMs

· Importance and Applications in Various Fields

· Evolution and Development of LLMs

· Natural Language Understanding

· Text Generation and Content Creation

· Conversational Agents (Chatbots)

· Information Retrieval and Search

Cybersecurity with AI (4 hours)

· Security Policy and Procedure Automation

· Natural Language Understanding for Security Policies

· Natural Language Generation for Reporting and Documentation

Prevent common security flaws with AI (12 hours)

· Common security risks and vulnerabilities in applications

· How to present security risks in application

· Using AI to detect and prevent following.

o detecting anomalies in user behaviour patterns,

o Identification of unauthorized access attempts.

o Cryptographic key management

o Detecting weaknesses in cryptographic implementations.

o Detect suspicious activities and potential security breaches.

Secure Software Development Lifecycle (SDLC) with AI (4 hours)

· Automated security testing,

· Code review, and

· Vulnerability management throughout the SDLC.

Get started with LLM for Cybersecurity (6 hours)

· Application of AI in Cybersecurity

· Getting started with common prompts for effective security knowledge.

· Use of GenAI for automation.