Earn 40 CPE Course Credits
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Learners Count
90,000+ Learners

Program Highlights

InfosecTrain offers Certified AI Systems Professional for Cybersecurity to help professionals master AI risks, secure ML systems, and build robust AI security capabilities for modern enterprises. This course is designed to prepare learners for the next era of cyber defense. You will not only understand how AI works, but also learn how to secure it, use it responsibly, and leverage it for both defensive and offensive cybersecurity operations.

Through structured modules, governance frameworks, real-world labs, and hands-on cloud deployments, this training delivers an end-to-end skill set aligned with emerging AI security roles.

40-Hour of Hands-On AI Security Training
AI Basics → Governance → Red & Blue Teaming → Cloud AI
Labs: Adversarial Attacks, AI Red Teaming, LLM Security
Cloud AI using Google AI Studio & Vertex AI
Offensive AI: Recon, Payloads, Phishing, Exploits
Defensive AI: Detection Models, Email & User Security, SIEM
Aligned with NIST AI RMF & ISO 42001
Access to Recording Sessions
Training Schedule
Start Date End Date Start/End Time Batch Type Training Mode Batch Status
28-Jun-2026 08-Aug-2026 19:00 - 23:00 (IST) Weekend Online [ Close ]
29-Aug-2026 11-Oct-2026 19:00 - 23:00 (IST) Weekend Online [ Open ] Enroll
31-Oct-2026 13-Dec-2026 19:00 - 23:00 (IST) Weekend Online [ Open ] Enroll

About Course

InfosecTrain’s Certified AI Systems Professional for Cybersecurity training program offers a comprehensive introduction to securing modern AI systems across the full AI lifecycle. Designed for cybersecurity, cloud, and AI practitioners, this course blends foundational AI learning with practical security applications; ranging from responsible AI design and governance to AI-powered offense, defense, and cloud-based model deployment. Through a structured progression of concepts, guided labs, and real enterprise use cases, participants learn how AI models are built, where they fail, how to defend them, and how to operationalize secure AI workloads in real environments. This training ensures professionals gain the competencies needed to secure ML/LLM systems, enable AI-driven SOC capabilities, and confidently navigate the evolving landscape of AI governance, threats, and enterprise adoption.

Course Curriculum

MODULE 1

Introduction to AI

⏱ 2 Hours
  • Evolution of AI
  • AI Tech Stack and Components of an AI system
  • Demystifying AI - Types, Key Terminologies, Learning Types
  • Types Of Algorithms
  • AI Applications: Predictive AI vs Generative AI
  • Understanding AI Model Development
  • Understanding NLP
  • LLM Architecture
  • Lab: Understanding Generative AI technically via Open AI Playground and LM Studio
MODULE 2

Python Basics for Using AI Frameworks and building AI Models Whole module uses hands on lab

⏱ 6 Hours
  • Understanding Introductory Programming Concepts: Variables, Datatypes, Keywords, Functions (Pre-defined), Printing, User Inputs, Comments, Operators
  • User Defined Functions
  • Creating Program Flow with Conditionals and Loops
  • Advanced Datatypes: Lists, Tuples, Sets, Dictionary
  • Libraries for AI: Data Engineering Phase: Numpy, Pandas, Matplotlib, NLTK (for NLP)
  • Model Engineering Phase: Scikit-learn (Machine Learning), Tensorflow (Deep Learning)
  • How are AI Systems Built - The AI Model Development Lifecycle:
    • Problem Definition and Decision Boundary Identification
    • Data Sourcing, Trust Boundaries, and Data Preparation Pipelines
    • Model Selection, Design Choices, and Dependency Considerations
    • Training and Fine-tuning within Controlled Environments
    • Validation, Risk Assessment, and Approval Gates
    • Deployment and Inference Architecture (APIs, Access, Exposure)
    • Monitoring, Feedback Loops, and Drift Detection
    • Model Updates, Versioning, and Retirement
  • Using No code Low Code Frameworks for AI Model Development: AutoML
  • Using GenAI Tools for AI Model Development
MODULE 3

Considerations for Building a Responsible AI System

⏱ 2 Hours
  • Why AI Governance Matters: Trust, Ethics, Compliance, Risk
  • Key Governance Principles: Safety, Fairness, Explainability, Privacy, Robustness, Auditability
  • Regulatory Frameworks, Standards and Compliance: NIST AI RMF, ISO 42001
  • AI Regulations and Guidelines Worldwide: EU AI Act, OECD AI Principles
MODULE 4

AI Cloud Governance

⏱ 4 Hours
  • Why Cloud Complicates AI Governance: Scalability, Multi-region Data, Opaque AI Services
  • The Shared Responsibility Model: What's Governed by Cloud Provider vs. Customer in AI Workloads
  • Mapping Existing AI Governance Principles (Fairness, Explainability, Privacy) to Cloud Controls (IAM, DLP, Encryption, Audit Logs)
  • Data Governance: Cloud Data Lineage, Provenance, and Labeling Accountability, Managing Data Residency and Sovereignty (Multi-region Storage Policies)
  • Model Governance: Model Versioning, Approval, and Explainability Tracking
  • Cloud Risk, Compliance & Audit Controls
MODULE 5

Using AI for Cyber Offense

⏱ 3 Hours
  • Automated Reconnaissance: Passive Recon Script Generation, Company Profiling
  • Vulnerability Scanning: Automating NMAP Scan Task Generation and Scan Report Assessment
  • Payload Generation & Obfuscation
  • Phishing & Social Engineering: Email Generation and Pretext Building using AI
  • Exploitation Assistance: Explain CVEs, Convert Exploit POCs, Automate Shell Handling using AI
  • Tools: OpenAI, Shell GPT, Open-Source Models from Hugging Face and Ollama
MODULE 6

Pentesting AI Systems

⏱ 4 Hours
  • Evasion, Poisoning and Theft
  • ML Top 10
  • LLM Top 10
  • Lab: Pentesting ML and DL Models with FGSM and ART
  • Lab: LLM Vulnerability Scanning using Garak
MODULE 7

Building AI-based Security Controls using the AI Model

⏱ 6 Hours
  • Security Controls for Network Security
  • Security Controls for Email Security
  • Security Controls for User Security
  • Security Controls for Endpoint Security
MODULE 8

Using AI for Security Analysis

⏱ 4 Hours
  • Integrating Custom Models with SIEM tools (ELK stack)
  • Using AI for Log Analysis
  • Using AI Tools and Models for Security Analysis
  • Agentic AI (Crew AI) for SOC Environment
MODULE 9

Securing AI Systems

⏱ 5 Hours
  • Threat Modelling of AI Systems (Lab: MITRE ATT&CK and ATLAS, STRIDEGPT)
  • Model Versioning and Monitoring (Lab: MLFLOW)
  • Model Explainability (Lab: LIME and SHAP)
  • Model Fairness (Lab: What-if tool)
  • Securing ML and DL Models with Adversarial Training (Lab: ART, Cleverhans)
  • Rate Limiting (Lab: Building Rate Limiter for LLMs using Langchain)
  • Applying Guardrails on LLMs to Protect Against Adversarial Attacks (Lab: LLM-Guard, Guardrails AI, Models from Hugging Face)
MODULE 10

Using the Cloud Environment to Build AI Models

⏱ 4 Hours
  • Fundamentals of using AI in the Cloud and Deploying AI on the Cloud
  • Google AI Studio Essentials
  • Introduction to Vertex AI
  • Vertex AI Pipelines
  • Lab: Building & Deploying an ML Model on GCP using Vertex AI

Target Audience

  • SOC Analysts, Incident Responders, Cybersecurity Professionals
  • Cloud Engineers, Cloud Architects, DevSecOps Teams
  • Penetration Testers & Red Teamers
  • Data Scientists, ML Engineers, AI Practitioners
  • Security Engineers securing ML/LLM systems
  • Developers integrating AI in enterprise apps
  • Anyone preparing for AI security certifications
  • Professionals adopting AI in SOC & security automation

Pre-requisites

  • Solid understanding of core IT and cybersecurity fundamentals such as networking, threat landscape, and security controls
  • Basic programming familiarity is helpful, but not mandatory-programming concepts are covered from the ground up
  • No prior ML or DL experience required; all AI concepts are taught from first principles
  • Strong curiosity to learn AI, build models, and secure AI systems in real-world environments

Course Objectives

  • Build a holistic understanding of AI systems and their security
  • Bridge the gap between AI engineering and cybersecurity
  • Train professionals in Responsible AI practices
  • Equip teams to secure enterprise AI and LLM deployments
  • Enable AI-driven cyber defense capabilities
  • Prepare learners for advanced AI security careers
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Benefits of InfosecTrain's Certified AI Systems Professional for Cybersecurity Training
Master AI security, governance, and LLM protection skills
Hands-on labs for real-world AI offense and defense
Learn to secure AI models across the full lifecycle
Build AI-driven detection and SOC automation capabilities
Gain cloud AI deployment skills with Google AI Studio & Vertex AI
Average Salary
$ 200,000
$ 180,000
$ 150,000
$ 135,000
$ 140,000
AI Security
Engineer
AI/ML Engineer
(Security-Focused)
AI Security Analyst /
LLM Security Analyst
AI-Based SOC Analyst
(L2/L3)
AI Governance &
Risk Specialist
Hiring Companies
Accenture Amazon Web Services (AWS) Deloitte Ernst & Young (EY) Google IBM Microsoft
Source: Glassdoor, PayScale, Indeed
Confused about choosing the right course?
How We Help You Succeed
Vision Vision
Goal Goal
Skill Building Skill-Building
Mentoring Mentoring
Direction Direction
Support Support
Success Success
Our Expert Course Advisors
Avnish
7+ Years of Experience
Information Security | Cloud Security | AI Security | Data Security | Consultant & Trainer
Avnish is an experienced Information security & Cloud Security Consultant and Trainer with over 7 years of expertise, specializing in cloud security, AI-assisted threat detection and securing AI systems. He has delivered tailored training globally across various sectors, equipping professionals with practical cybersecurity, cloud security and AI security skills.
Words Have Power
Waseem Akram Fareed
Canada
I have pursued CISSP, CRISC, and CISM from InfosecTrain. InfosecTrain is my default option when I think about any cybersecurity certification. The trainer's dedication and sincerity towards his classes is something that inspires me a lot personally. You will get 100 percent from InfosecTrain for whichever course you want to pursue. Especially the trainers are outstanding.
Fuzail Ahmed Lohare
UAE
The trainer was very good, with good knowledge and skills to share, and he handled the session with patience. I really enjoyed the training. Selecting InfosecTrain is always a good choice for me. The sales team is very supportive and helped me on this journey.
Rudraram Sai Kiran
United Kingdom
The trainer is a great presenter/tutor and teaches in a relaxing manner. His sense of humor and honesty about the task ahead for the newbie help make the challenging subject matter accessible. Thank you very much! I had been looking forward to this workshop for weeks, and it exceeded my expectations! I have learned a lot.
Jatin Tandon
Canada
Very detailed and organized training, as always, by the best instructors at InfosecTrain. Will come back for more courses after completing my certification.
Yamna Taouss
Morocco
It was an interesting training that could help me succeed in obtaining certificates. I am truly thankful to InfosecTrain for an amazing training. Looking forward to attending more sessions with InfosecTrain.
Why Choose Infosec Train?

Learn from certified trainers & industry experts

Practice with labs, regular assessments, and case studies

Immerse with scenario-based learning across APT domains

Best Quality Training with Best Price Guarantee

Prepare to excel with mock tests, exam tips, and real-world examples

Conquer the world of Penetration Testing

Updated curriculum aligned with the latest Pentesting tools

Choose Flexible Learning options including weekend batches

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Frequently Asked Questions
AI systems security focuses on protecting AI and ML models across their lifecycle, including design, training, deployment, monitoring, and governance. It addresses adversarial attacks, model risks, cloud exposure, and secure operationalization in enterprise environments.ned to help learners secure modern AI, ML, and LLM-based systems across the full AI lifecycle. It covers AI fundamentals, governance, adversarial ML, AI red & blue teaming, cloud AI deployment, and real-world defensive/offensive AI techniques. The course prepares you for emerging job roles in AI security, AI governance, and SOC automation.
Yes, the course includes adversarial machine learning concepts such as evasion, poisoning, and model theft. It also features hands-on labs using FGSM, ART, and adversarial training techniques to secure ML and deep learning models.
AI threat modeling is performed using structured methodologies and practical labs, including mapping threats with MITRE ATT&CK and ATLAS, applying STRIDEGPT, and analyzing vulnerabilities across the AI model development lifecycle.
This certification is designed for SOC analysts, cybersecurity professionals, cloud engineers, DevSecOps teams, penetration testers, ML engineers, AI practitioners, and developers seeking to secure AI/LLM systems in enterprise environments.
Yes, the program includes real-world AI attack scenarios such as adversarial attacks, LLM vulnerability scanning, automated reconnaissance, phishing generation, exploitation assistance, and practical red and blue teaming exercises through guided labs.
Industries adopting enterprise AI, cloud-based ML deployments, SOC automation, and AI-driven cybersecurity operations require AI systems security professionals to manage governance, risk, compliance, and secure AI model deployment.
No prior machine learning or deep learning experience is required. All AI concepts are taught from first principles, and programming basics are covered, though a foundational understanding of IT and cybersecurity fundamentals is recommended.
Yes, the course covers secure AI architecture design through AI model development lifecycle practices, deployment architecture, governance controls, cloud AI pipelines, model monitoring, versioning, guardrails, and enterprise-ready secure AI implementations.
This certification equips cybersecurity professionals to secure ML/LLM systems, implement AI governance, perform red and blue teaming, deploy AI securely in cloud environments, and prepare for advanced roles such as AI Security Engineer and Governance Specialist.
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