Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Ethical Hacking and AI Security combines classic offensive security with the emerging discipline of securing AI, taught strictly for authorised, legal use. You work through the ethical-hacking lifecycle — reconnaissance, scanning, exploitation and reporting — on deliberately vulnerable lab targets using standard tooling. The course then turns to the machine-learning attack surface: adversarial examples, model evasion, data poisoning, model inversion and prompt injection against LLMs, together with the defences for each. You leave able to assess and harden both conventional systems and the AI components increasingly embedded in them. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers ethical hacking fundamentals and AI security — authorised reconnaissance, exploitation and defence, plus attacks and defences specific to machine-learning systems.
📋 Course Objectives
1. Carry out the ethical-hacking lifecycle within a legal framework.
2. Use standard tools for authorised reconnaissance and testing.
3. Explain adversarial examples, poisoning and model inversion.
4. Assess and defend LLMs against prompt injection.
5. Report findings and recommend concrete defences.
👥 Who Should Enroll?
• Security professionals extending into AI security
• Developers hardening AI-enabled applications
• IT and infrastructure staff learning authorised testing
• Students specialising in cybersecurity
🚀 Key Learning Outcomes
• The ability to run an authorised security assessment.
• Understanding of attacks unique to ML systems.
• A defensive mindset for AI-enabled applications.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Ethical Hacking Foundations
Develop a comprehensive understanding of AI and machine learning fundamentals, including supervised and unsupervised learning techniques • Analyze the mathematical prerequisites for AI, including linear algebra, calculus, and probability theory • Design and implement basic AI models using popular libraries and frameworks, such as TensorFlow and PyTorch
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for AI model training, including data cleaning, preprocessing, and feature engineering • Evaluate and select appropriate data preprocessing techniques, such as normalization, feature scaling, and encoding • Implement data pipelines using popular tools and technologies, such as Apache Beam, Apache Spark, and AWS Glue
Model Architecture, Algorithm Design, and Ethical Hacking Methods
Design and implement deep learning models, including convolutional neural networks, recurrent neural networks, and transformers • Analyze and evaluate the performance of AI models, including metrics such as accuracy, precision, recall, and F1 score • Develop and implement ethical hacking techniques, including penetration testing, vulnerability assessment, and security auditing
Training, Hyperparameter Optimization, and Evaluation
Train and optimize AI models using popular optimization algorithms, such as stochastic gradient descent and Adam • Evaluate and select appropriate hyperparameters for AI models, including learning rate, batch size, and regularization techniques • Implement and manage AI model training pipelines, including data parallelism, model parallelism, and distributed training
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments, including cloud, on-premises, and edge deployments • Implement and manage MLOps workflows, including model monitoring, logging, and alerting • Develop and implement continuous integration and continuous deployment (CI/CD) pipelines for AI models
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and evaluate the ethical implications of AI systems, including bias, fairness, and transparency • Develop and implement strategies for bias mitigation and fairness in AI systems • Design and implement responsible AI practices, including explainability, interpretability, and accountability
Industry Integration, Business Applications, and Case Studies
Evaluate and select appropriate AI solutions for business problems, including computer vision, natural language processing, and predictive analytics • Develop and implement AI-powered business applications, including chatbots, virtual assistants, and recommender systems • Analyze and discuss real-world case studies of AI adoption and implementation in various industries
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Apache Spark |
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