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AI Security and Governance AI Cybersecurity

AI Security and Governance

AI cybersecurity and governance illustration featuring a digital shield, secure data, AI neural networks, and compliance symbols.

This course introduces AI security, privacy protection, risk assessment, regulatory compliance, and responsible AI governance. Learners explore threats to machine learning systems and generative AI applications, including prompt injection, data leakage, model manipulation, and unauthorized access. Through practical exercises and case studies, students learn to develop AI security policies, evaluate risks, establish governance controls, and support trustworthy AI implementation.

Duration
4 months
Level
Intermediate
Format
Online
Language
English / Hindi
Time commitment
6–8 hours per week
Course code
AI-SEC-GOV-101

Who this course is for

IT professionals, cybersecurity analysts, AI developers, data scientists, compliance professionals, risk managers, technology consultants, and students interested in AI security and governance.

What you will learn

  • - Identify security threats affecting AI and machine learning systems.
  • - Explain AI governance principles, accountability, and responsible AI practices.
  • - Assess AI privacy, security, and operational risks.
  • - Recognize prompt injection, data poisoning, and model manipulation threats.
  • - Apply access controls and data protection principles to AI applications.
  • - Develop AI risk assessment and incident response plans.
  • - Establish AI usage policies and governance frameworks.
  • - Evaluate AI systems for transparency, fairness, and reliability.
  • - Understand relevant AI regulations, standards, and compliance considerations.
  • - Prepare an AI security and governance implementation plan.

Course curriculum

Module 1: Introduction to AI Security and Governance Module 2: AI Threats and Vulnerabilities Module 3: Data Privacy and Protection Module 4: Securing Generative AI and AI Agents Module 5: AI Risk Management Module 6: AI Governance and Compliance Module 7: AI Security Testing and Auditing Module 8: Capstone Project

Prerequisites

Basic computer and internet skills. General understanding of software, data, or AI concepts is helpful. Basic cybersecurity knowledge is recommended but not mandatory.

Projects and practical work

AI risk assessment for a sample business application. Prompt injection and AI output security evaluation in a controlled lab. AI data privacy and access-control checklist. AI governance policy and acceptable-use guidelines. Final project: AI security audit and governance implementation plan.

Certificate

Participants who successfully complete the course requirements receive an institute-issued Certificate of Completion. This is not an official NIST, ISO, or vendor certification.