- 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
SECTION D – INVESTIGATION CHALLENGES
Prev