Program Overview :-
Artificial Intelligence (AI) is transforming cyber security and Cyber Crime simultaneously. While AI strengthens threat detection and automation, it also enables sophisticated fraud, deepfakes, and automated social engineering.
This course provides foundational knowledge of:
- AI & Machine Learning basics
- AI applications in cyber defense
- AI-enabled Cyber Crimes
- Investigation challenges and legal considerations
Program Objectives :-
By the end of this course, learners will:
- Explain core AI and ML concepts
- Identify AI-powered cyber threats
- Understand AI-based threat detection mechanisms
- Recognize deepfake and voice cloning risks
- Outline investigative and evidentiary challenges
Course Features
- Lectures 92
- Quizzes 5
- Duration Lifetime access
- Skill level All levels
- Language English
- Students 1
- Certificate Yes
- Assessments Self
- 5 Sections
- 92 Lessons
- Lifetime
Expand all sectionsCollapse all sections
- MODULE 1 – AI & ML FUNDAMENTALS23
- 1.1INTRODUCTION TO ARTIFICIAL INTELLIGENCE
- 1.2EVOLUTION OF ARTIFICIAL INTELLIGENCE
- 1.3WHAT IS MACHINE LEARNING?
- 1.4CORE COMPONENTS OF MACHINE LEARNING
- 1.5TYPES OF MACHINE LEARNING
- 1.6NEURAL NETWORKS
- 1.7NATURAL LANGUAGE PROCESSING (NLP)
- 1.8COMPUTER VISION
- 1.9GENERATIVE AI
- 1.10AI WORKFLOW
- 1.11PERFORMANCE METRICS
- 1.12MODEL BIAS
- 1.13OVERFITTING & UNDERFITTING
- 1.14AI LIMITATIONS
- 1.15AI IN CYBER INVESTIGATION CONTEXT
- 1.16ETHICAL CONSIDERATIONS – 1
- 1.17SECURITY RISKS IN AI SYSTEMS
- 1.18AI VS AUTOMATION
- 1.19AI TERMINOLOGY SUMMARY 1
- 1.20FUTURE TRENDS
- 1.21STRATEGIC IMPORTANCE FOR INVESTIGATORS
- 1.22MODULE 1 SUMMARY
- 1.23SECTION A – AI & ML FUNDAMENTALS10 Minutes5 Questions
- MODULE 2 - ARTIFICIAL INTELLIGENCE IN CYBER SECURITY26
- 2.1MODULE 2 INTRODUCTION
- 2.2EVOLUTION FROM SIGNATURE-BASED SECURITY TO AI
- 2.3EVOLUTION FROM SIGNATURE-BASED SECURITY TO AI
- 2.4AI-BASED THREAT DETECTION
- 2.5AI IN MALWARE DETECTION
- 2.6AI IN NETWORK SECURITY
- 2.7AI IN FRAUD DETECTION
- 2.8AI IN EMAIL SECURITY
- 2.9AI IN ENDPOINT SECURITY
- 2.10AI IN CLOUD SECURITY
- 2.11AI IN THREAT INTELLIGENCE
- 2.12SECURITY ORCHESTRATION & AUTOMATION (SOAR)
- 2.13BENEFITS OF AI IN CYBER SECURITY
- 2.14FALSE POSITIVES & FALSE NEGATIVES
- 2.15MODEL DRIFT
- 2.16ADVERSARIAL ATTACKS ON AI SYSTEMS
- 2.17EXPLAINABILITY CHALLENGE
- 2.18AI AND PRIVACY CONCERNS
- 2.19HUMAN-IN-THE-LOOP MODEL
- 2.20CASE EXAMPLE – AI DETECTING INSIDER THREAT
- 2.21FUTURE OF AI IN CYBER DEFENSE
- 2.22REGULATORY & LEGAL IMPLICATIONS
- 2.23LIMITATIONS OF AI IN CYBER SECURITY
- 2.24STRATEGIC IMPORTANCE FOR INVESTIGATORS 2
- 2.25MODULE 2 SUMMARY
- 2.26SECTION B – AI IN CYBER SECURITY10 Minutes5 Questions
- MODULE 3 - ARTIFICIAL INTELLIGENCE ENABLED CYBER CRIME24
- 3.1MODULE 3 INTRODUCTION
- 3.2SHIFT FROM TRADITIONAL TO AI-POWERED CYBER CRIME
- 3.3DEEPFAKE TECHNOLOGY
- 3.4VOICE CLONING FRAUD
- 3.5AI-GENERATED PHISHING
- 3.6AI-POWERED MALWARE
- 3.7AUTOMATED SOCIAL ENGINEERING
- 3.8SYNTHETIC IDENTITIES
- 3.9AI IN RANSOMWARE OPERATIONS
- 3.10AI-DRIVEN DISINFORMATION CAMPAIGNS
- 3.11DARK WEB & AI TOOLS
- 3.12AUTOMATED BOTNETS
- 3.13CRYPTOCURRENCY FRAUD & AI
- 3.14CHALLENGES IN ATTRIBUTION
- 3.15DEEPFAKE DETECTION TECHNIQUES
- 3.16FORENSIC CHALLENGES
- 3.17LEGAL IMPLICATIONS
- 3.18ETHICAL CONSIDERATIONS – 2
- 3.19CASE EXAMPLE – CEO VOICE FRAUD
- 3.20RISK MITIGATION STRATEGIES 1
- 3.21FUTURE TRENDS IN AI-ENABLED CRIME
- 3.22STRATEGIC IMPORTANCE FOR INVESTIGATORS 3
- 3.23MODULE 3 SUMMARY
- 3.24SECTION C – AI-ENABLED CYBER CRIME10 Minutes5 Questions
- MODULE 4 - INVESTIGATION CHALLENGES IN AI-ENABLED ENVIRONMENTS23
- 4.1MODULE 4 INTRODUCTION
- 4.2SHIFT IN INVESTIGATIVE PARADIGM
- 4.3AUTHENTICITY VERIFICATION CHALLENGES
- 4.4CHAIN OF CUSTODY IN AI CASES
- 4.5ATTRIBUTION PROBLEMS
- 4.6MODEL BIAS & INVESTIGATIVE RISKS
- 4.7EXPLAINABILITY & TRANSPARENCY
- 4.8ADMISSIBILITY OF AI-GENERATED EVIDENCE
- 4.9DATA PRIVACY CONCERNS
- 4.10ADVERSARIAL AI ATTACKS
- 4.11EVIDENCE PRESERVATION IN AI SYSTEMS
- 4.12FORENSIC ANALYSIS OF AI SYSTEMS
- 4.13CROSS-JURISDICTIONAL CHALLENGES
- 4.14ETHICAL INVESTIGATION CONSIDERATIONS
- 4.15RESOURCE & SKILL GAPS
- 4.16CASE EXAMPLE – DEEPFAKE EXTORTION
- 4.17ROLE OF EXPERT WITNESSES
- 4.18DOCUMENTATION REQUIREMENTS
- 4.19FUTURE INVESTIGATIVE ADAPTATION
- 4.20STRATEGIC FRAMEWORK FOR AI INVESTIGATIONS 4
- 4.21RISK MITIGATION STRATEGIES 2
- 4.22MODULE 4 SUMMARY
- 4.23SECTION D – INVESTIGATION CHALLENGES10 Minutes5 Questions
- Final Certification Exam1
