Ethical and Regulation for Artificial Intelligence Security

Master Ethical AI, Regulatory Compliance, and Security Threat Mitigation in Modern AI Systems

What you’ll learn
✓ Understands AI Triad, Attacks and RAG Vulnerabilities
✓ Implement transparency and explain ability tools for interpretable decision making
✓ Conduct AI auditing and anticipate future trends in trustworthy AI governance
✓ Design secure model development and training procedures resistant to adversarial manipulation

Requirements
● Basic Understanding of Artificial Intelligence
● Python Programming

Description
Artificial Intelligence is rapidly transforming modern society, powering applications in healthcare, finance, smart cities, autonomous systems, cybersecurity, and generative AI platforms. However, as AI adoption increases, concerns related to security vulnerabilities, ethical risks, privacy violations, bias, and regulatory compliance have become critically important.

This comprehensive Udemy course, “Ethical and Regulation for Artificial Intelligence Security,” is designed to provide learners with a complete understanding of how to build, deploy, and govern secure, trustworthy, and regulation-compliant AI systems. This course contains the use of artificial intelligence.

The course uniquely integrates AI Security, Responsible AI Principles, Global Regulatory Frameworks, and Hands-on Python-based Implementations, enabling learners to move beyond theoretical understanding toward practical real-world AI governance and protection strategies.

What You Will Learn

• Foundations of secure and ethical Artificial Intelligence

• Adversarial threats targeting Machine Learning and LLM systems

• End-to-end Machine Learning Security (MLSecOps) practices

• Responsible AI principles including fairness, transparency, and accountability

• Global AI regulations and compliance frameworks

• Bias detection, mitigation, and explainable AI techniques

• Practical simulations of AI attacks and defensive mechanisms using Python

• Secure deployment and monitoring of AI systems

Section 1: Foundations of Secure and Ethical Artificial Intelligence

This section introduces the fundamental relationship between AI Security, Ethics, and Regulation, forming the backbone of trustworthy AI systems. Learners will explore the AI lifecycle and associated risks while understanding core ethical principles such as *Privacy, Fairness, Accountability, and Transparency (FAT)**.

You will examine modern adversarial threats including

• Evasion attacks

• Poisoning attacks

• Inference attacks

• Model extraction attacks

• LLM and Transformer vulnerabilities

• Prompt injection and jailbreaking

• Retrieval-Augmented Generation (RAG) security risks

The section also introduces the MITRE ATLAS framework, helping learners understand structured AI threat modeling.

Section 2: End-to-End Security in Machine Learning

This module focuses on securing AI systems throughout their operational lifecycle using MLSecOps principles and practices.

Key topics include

• Secure data management strategies

• Data provenance and integrity verification

• Secure model development and training

• Safe deployment architectures

• Continuous monitoring and AI incident response mechanisms

Learners will understand how security must be embedded into AI pipelines from data collection to real-time deployment.

Section 3: Responsible AI – Ethics, Bias, Transparency, and Accountability

Responsible AI is essential for building socially acceptable and legally compliant AI solutions. This section explores

• Ethical AI frameworks and governance principles

• Sources, detection, and mitigation of AI bias

• Explainable and transparent AI systems

• Human oversight and accountability mechanisms

• Ethical challenges in Generative and Autonomous AI

The course provides detailed insights into global regulatory ecosystems including

• EU AI Act

• NIST AI Risk Management Framework (USA)

• ISO/IEC 42001 AI Management Standard

• ISO/IEC 23894 AI Risk Standard

• Digital Personal Data Protection (DPDP) Act 2023 – India

• IndiaAI Mission, MeitY, and NITI Aayog initiatives

• Sector-specific AI compliance requirements

• AI auditing methodologies and future trends in trustworthy AI

Section 4: Hands-on AI Security – Attacks, Defences, and Privacy Protection with Python

This practical section transforms theoretical knowledge into real-world skills through guided coding exercises.

Learners will

• Set up AI security environments and ethical toolkits

• Simulate adversarial attacks on ML models

• Perform inference and model extraction attacks

• Test LLM prompt injection and jailbreaking scenarios

• Explore vulnerabilities in RAG-based AI systems

• Implement adversarial training defenses

• Secure APIs and deployment pipelines

• Perform continuous monitoring and incident detection

Additionally, learners will conduct

• Bias identification and mitigation techniques

• Data integrity and provenance verification

By the end of this course, you will be able to design, evaluate, and deploy trustworthy AI systems aligned with international ethical and regulatory standards, while defending them against emerging adversarial threats.

Build the future of AI — securely, ethically, and responsibly.

Who this course is for
■ AI & Machine Learning Engineers Professionals developing AI/ML systems who want to integrate security-by-design and ethics-by-design principles. Engineers working with LLMs, Transformers, RAG systems, and generative AI.
■ Cybersecurity Professionals Security analysts and ethical hackers interested in Adversarial AI, model extraction, evasion, and poisoning attacks. Professionals transitioning into AI security.
■ Data Scientists & AI Researchers Researchers working on fairness, bias mitigation, privacy-preserving ML, and responsible AI frameworks. Ph.D. scholars and faculty members exploring AI governance and compliance research.
■ Compliance Officers & Policy Professionals Professionals working with AI governance, risk assessment, and regulatory frameworks (EU AI Act, GDPR, global AI regulations). Legal experts dealing with AI system accountability and audit requirements.
■ Graduate, Postgraduate, Doctorate students in: Artificial Intelligence Data Science Cybersecurity Computer Science Students preparing for research or industry roles in AI safety.
■ AI Product Managers & Tech Leaders Leaders designing AI-driven products who must ensure ethical deployment and regulatory compliance. Startup founders building AI-based solutions.
■ Government & Public Sector Professionals Officers involved in AI policy implementation, smart city AI deployment, and public AI governance.

https://anonymz.com/?https://www.udemy.com/course/ethical-and-regulation-for-artificial-intelligence-security