Program Highlights
The Cybersecurity AI Foundation Program provided by InfosecTrain is a cutting-edge program designed to enhance your expertise in AI-driven threat detection and prevention. This comprehensive AI Security Course covers advanced cybersecurity techniques, AI applications, and practical strategies to protect digital assets. Perfect for professionals aiming to stay ahead in the evolving cybersecurity landscape, the course empowers you to master AI-based solutions.
40-Hour of Instructor-led Training
Learn AI by Building It
Attack AI models
Secure AI Models
AI Governance Mapping
AI-Powered Security Operations
AI-Driven Pentesting
Hands-On Labs Throughout the course
Extended Post Training Support
Training Schedule
- upcoming classes
- corporate training
- 1 on 1 training
| Start - End Date | Training Mode | Batch Type | Start - End Time | Batch Status | |
|---|---|---|---|---|---|
| 26 Jul - 20 Sep | Online | Weekend | 09:00 - 13:00 IST | BATCH OPEN | |
| 03 Oct - 15 Nov | Online | Weekend | 09:00 - 13:00 IST | BATCH OPEN |
Why Choose Our Corporate Training Solution
- Upskill your team on the latest tech
- Highly customized solutions
- Free Training Needs Analysis
- Skill-specific training delivery
- Secure your organizations inside-out
Why Choose 1-on-1 Training
- Get personalized attention
- Customized content
- Learn at your dedicated hour
- Instant clarification of doubt
- Guaranteed to run
About Course
InfosecTrain’s Cybersecurity AI Foundation Program is a comprehensive program tailored to meet the demands of today’s rapidly evolving digital landscape. This beginner level course delves into integrating Artificial Intelligence with cybersecurity, providing participants with advanced skills to detect, analyze, and counter cyber threats efficiently. The course bridges the gap between traditional cybersecurity and modern AI security with hands-on coverage, which is very crucial for participants.
Through hands-on exercises, case studies, and industry-relevant scenarios, learners understand how AI systems actually work under the hood. Designed to explain how AI is secured across the full lifecycle: Data → Model → Deployment → Operations, this course provides exposure to real attack techniques and defensive controls.
How is this course different from other courses?
- This course teaches learners to understand, build, attack, secure, govern, and use AI systems for both defensive and offensive security – with hands-on labs at every layer.
- Full lifecycle coverage across three parts:
- Understanding AI and building AI
- Attacking, securing, and governing AI systems
- Using AI for defensive and offensive security
- The threat-modeling coverage is updated. MITRE ATLAS plus MAESTRO for agentic systems, OWASP ML Top 10 plus LLM Top 10 plus Agentic Top 10
- Governance is hands-on, not theoretical, mapping controls to NIST AI RMF and ISO 42001 Annex A, applying EU AI Act risk tiers, and reviewing AI supply chain security.
- Built for every security professional – defensive, offensive and GRC- combining AI fluency with the skills to defend AI systems your organization is already deploying.
Course Curriculum
-
Part 1 – AI Fundamentals
- Module 1: Introduction to AI
- What is Artificial Intelligence: AI, Machine Learning, Deep Learning
- Components of an AI System
- Brief history and evolution: from rule-based systems to modern deep neural networks
- How Machines Learn: Supervised learning, Unsupervised learning, Reinforcement learning
- Types of AI Models and What They Do: Classification models, Regression models, Clustering models, Language models
- AI Model development lifecycle
- AI Tools Landscape: Huggingface, OpenWebUI, GenAI platforms, Open-source model platforms for running models locally: Ollama, LM Studio
- Prompt Engineering Techniques
- Module 2: Python Basics for Using AI Frameworks
- Python Fundamentals for AI
- Lab Setup: Linux VM, Using Jupyter Notebook
- Google Colab: free cloud compute for coding and AI/ML experiments
- Python Libraries for Data Analysis and Visualisation: Pandas, NumPy, Matplotlib
- Types of Data and using them for AI Model development
- Secure and Responsible use of Data in AI models and Applications
- Module 3: Machine Learning for Security
- Libraries and frameworks for Machine Learning and Deep Learning
- Data Collection
- Data Processing (Including Feature Extraction and Normalization)
- Algorithm selection and Model Training
- TEVV: Test, Evaluation, Verification, and Validation and Fine Tuning
- Model Deployment, Model Monitoring and Retraining
- Practical Lab: Building AI (ML and DL) models for Network Security by using GenAI
- Module 4: Natural Language Processing (NLP)
- Libraries and frameworks for NLP
- Text Processing for NLP: Tokenization, Stop words removal, Embeddings
- NLP Feature Engineering Types
- Converting Emails and Logs to Vectors
- Practical Lab: Building a Phishing Mail Detector
- Module 5: Generative AI, LLMs and Agentic AI
- Generative Adversarial Networks
- Transformer Architecture
- System Prompts and User Prompts
- Prompt Engineering Techniques
- Foundational model and fine-tuned model
- Retrieval Augmented Generation (RAG)
- Agentic AI: Autonomous agents, Planning, Reasoning, Action: tool use, and multi-agent orchestration frameworks
- Agentic AI Frameworks: Langchain, CrewAI
- Model Context Protocol (MCP)
- Practical Lab: Building a Gen AI Based Custom Chatbot and Agent
- Module 6: Attacking AI
- Poisoning: Data and Model Poisoning
- Backdoor Attacks with Trigger-Based Poisoning
- Evasion Attacks: Evading AI-Based Detectors
- Model and Data Theft
- Inversion and Membership Inference Attacks
- OWASP Machine Learning Security Top 10
- Prompt Injection: Direct and Indirect
- Jailbreaking Techniques
- OWASP Top 10 for Large Language Model Applications
- OWASP Top 10 for Agentic Applications
- Practical Lab: Automated LLM Pentesting using Garak
- Module 7: Securing AI
- Threat Modelling: MITRE ATLAS, MAESTRO Framework
- Defense-in-Depth for AI
- Secure Infrastructure Architecture for AI Systems
- Data Security in AI Systems
- Adversarial Training and Bias Checking
- Guardrails for LLMs: System Prompt Hardening, Input and Output Guards
- Open-Source Guardrail Tools: LLM-Guard, Guardrails-AI, LlamaGuard
- AI Gateway: LiteLLM
- Monitoring and Observability for AI Systems: MLFlow, Arize Phoenix
- Rate Limiting & Cost Budgeting
- Data and Model Versioning
- Access control for AI
- Data and Model Integrity
- AI Supply Chain Security
- Practical Lab: Integrating guardrails with AI models
- Module 8: AI Governance
- AI Ethics & Properties of a Responsible AI System
- Data Governance & Model Governance
- NIST AI RMF (Govern/Map/Measure/Manage) and ISO 42001 Annex A Controls
- EU AI Act risk tiers
- Security Roles & Operating Model for AI
- Practical Lab: Governance control mapping exercise
- Module 9: Offensive Security using AI
- AI in the Penetration Testing Lifecycle: Reconnaissance, Scanning, Exploitation, Reporting
- Reconnaissance using AI: OSINT Tools, AI-Generated Recon Scripts
- Intelligent Scanning and Enumeration: Nmap with AI, AI-Generated Scan Profiles
- AI-Enhanced Password Attacks: Wordlist Generation, Brute Force Automation
- Web Application Pentesting with AI
- Social Engineering with AI: AI-Generated Phishing Emails
- Covering Tracks and Maintaining Access with AI-Generated Scripts
- Practical Lab: AI-Assisted Reconnaissance, Network Scan, Exploitation, and Pentest Report Writing
- Module 10: Security Operations using AI
- Security Operations: Structure, Roles, and Core Functions
- Signature based detection principles
- AI Integration in Security Operations
- Using ML+Signatures for best results
- AI for Log Analysis: Windows Event Logs, Network Logs
- AI for Alert Triage and False Positive Reduction
- Threat Intelligence: IOCs, MITRE ATT&CK, AI-Assisted IOC Correlation
- Vulnerability Management with AI: CVE Explanation, Prioritization, Remediation
- Incident Response Lifecycle with AI-Guided Playbooks
- User and Entity Behaviour Analytics (UEBA)
- Malware detection using AI
- Using AI with SIEM tools (ELK Stack)
- Practical Lab: Analysis of IDS Logs using Generative AI and Agentic AI
Part 2 – AI Security and Governance
Part 3 – Security Operations and Offensive Security using AI
Target Audience
- Cybersecurity Professionals – Security Analysts, SOC Analysts, Security Engineers, Detection Engineers
- Offensive Security Professionals
- GRC Professionals and Security Auditors who want to understand how AI systems work
- Security Professionals who want to learn how to leverage Open-source AI tools and LLMs securely in their workflow
- Anyone who wants to understand how AI models as a security control are built
- Anyone who wants to transition to AI Security Roles
Pre-requisites
- Fundamental Knowledge of Security Concepts
- No coding or AI Knowledge required
Course Objectives
After completion of this course, you will be able to:
- Understand how AI systems work and how they apply to security
- Build AI-based Security Systems
- Identify and Exploit AI Vulnerabilities
- Secure AI Systems
- Implement Governance and Compliance for AI
- Use AI for Defensive (SOC) and Offensive Security
Vision
Goal
Skill-Building
Mentoring
Direction
Support
Success
Global cybersecurity workforce gap underscores high demand for skilled professionals.
Of cybersecurity teams are already utilizing AI in their tools, highlighting the importance of AI proficiency in modern cybersecurity roles.
Cybersecurity professionals report significant skills gaps in their organizations, highlighting the need for training programs.
Tech professionals face an AI security skills shortage, emphasizing specialized training demand in AI-driven cybersecurity.
Technology Firms
Healthcare
Retail
Government
Manufacturing
Finance
I’m grateful to have joined my third training with Infosec Train. The AI-Powered Cybersecurity sessions were comprehensive and well-structured, covering AI, ML, and DL with great clarity and depth.
An excellent and informative session! The AI-Powered Cybersecurity course content kept me engaged throughout. Truly a valuable experience.
The AI-Powered Cybersecurity training was in-depth and covered a wide range of AI topics. The trainer’s instructions were clear and effectively incorporated participant feedback. The coordination and execution of the sessions were seamless, making it a great overall learning experience.
The trainer was amazing and demonstrated in-depth knowledge of the AI-Powered Cybersecurity Training. The sessions were engaging and provided valuable insights into AI concepts, prompts, and practical applications.
The AI-Powered Cybersecurity training was really good and provided valuable insights. It also encouraged a deeper approach to analyzing emerging trends and understanding their practical impact.
Frequently Asked Questions
What is the Cybersecurity AI Foundation Program?
The Cybersecurity AI Foundation Program is a comprehensive program that integrates artificial intelligence with cybersecurity practices. It equips participants with the skills to leverage AI tools while preparing learners to tackle modern cybersecurity challenges effectively.
What career opportunities can this course prepare me for?
Career opportunities after this course:
- Security Analyst with an AI focus
- Security Engineer (Security Aware)
- AI Governance/Compliance Analyst
- Junior Threat Analyst
- Security Operations Center (SOC) Analyst
- AI-assisted Penetration Tester
How can this course support my career transition?
This course bridges the gap between AI and cybersecurity by:
- Establishing a strong foundation in both domains
- Offering hands-on experience with industry-relevant tools
- Teaching current best practices and techniques
This course equips you with essential skills to excel in the growing field of AI-driven cybersecurity.
I have no prior experience with AI or Machine Learning. Will I be able to keep up?
Yes! This course is designed for beginners, starting with fundamental AI concepts and gradually progressing to more advanced topics. The initial modules focus on building a solid foundation, ensuring you can confidently keep up and succeed.
Can I take this course if I'm new to both AI and cybersecurity?
Yes! This course is beginner-friendly and designed to help you get started in both fields. It:
- Covers the AI Security Fundamentals.
- Provides supplementary resources for better understanding.
- Uses practical examples to bridge the gap between the two domains.
Is programming knowledge required?
No, programming knowledge is not mandatory. The course starts with the basics and builds your skills as you progress. Having prior experience may help you understand programming exercises more quickly, but it is not a prerequisite.
Does the course include hands-on training?
Yes! The course perfectly balances theory and hands-on practice with a 50:50 approach. Each module starts with a theoretical foundation, followed by practical applications. Beginning with basic Python exercises, it gradually progresses to real-world problem-solving. Modules include:
- Interactive demonstrations
- Guided lab exercises
- Real-world security scenarios
- Practical tool implementation
Does this course include LLM pentesting?
The course provides a theoretical overview of threats to LLMs, explores the OWASP Top 10 for LLMs, and introduces the basics of performing pentesting on LLMs.
How can I enroll in the Cybersecurity AI Foundation Program?
In order to enroll in the Cybersecurity AI Foundation Program at InfosecTrain:
- Visit the InfosecTrain website, www.infosectrain.com, and navigate the Cybersecurity AI Foundation Program page.
- Fill out the registration form.
- You will get a confirmation email with further instructions.
- Book your free demo with Expert.
- You can also directly drop mail with your requirements at sales@infosectrain.com.