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Introduction to Cybersecurity Compliance and AI
Course Introduction
Module 1: Overview of Cybersecurity Compliance
1.1.1 The Necessity of Cybersecurity Compliance in Today’s World
1.1.2 Implementing Cybersecurity Compliance in Today’s World
1.1.3 Effects on Cybersecurity Implications
1.1.4 Explanation of Major Compliance Frameworks
1.2.1 International Organization for Standardization (ISO)
1.2.2 European Union Cybersecurity Act
1.3.1 Key Components of an Effective Compliance Program
1.3.2 Compliance Policies and Procedures
1.3.3 Employee Training and Awareness
1.4.1 Roles and Responsibilities
1.4.2 Documentation and Record-Keeping
1.4.3 Compliance Audits and Assessments
1.5.1 Basics of AI and Types of ML
1.5.2 Key Algorithms and Models
1.5.3 Benefits of Integrating AI
1.5.4 Challenges and Limitations
1.6.1 Real-World Examples
1.6.2 Industry Adoption
Module 2: Security and Risk Management with AI
2.1.1 Identifying and Assessing Risks
2.1.2 Risk Mitigation Strategies
2.1.3 NIST Risk Management Framework (RMF)
2.1.4 ISO 31000 Risk Management
2.2.1 Identifying and Evaluating Risks
2.2.2 Impact and Probability Assessment
2.2.3 Mitigation Strategies
2.3.1 Automated Risk Identification
2.3.2 Predictive Risk Analytics
2.4.1 Legal and Regulatory Considerations in AI for Cybersecurity - Part 1
2.4.2 Legal and Regulatory Considerations in AI for Cybersecurity - Part 2
2.4.3 Ethical Implications of AI in Cybersecurity
2.5.1 Automated Incident Detection and Response
2.5.2 AI in Forensic Analysis
2.5.3 Ethical Implications of AI in Cybersecurity
Module 3: Asset Security and AI for Compliance
3.1.1 Data Classification Techniques
3.1.2 Data Encryption and Protection
3.2.1 Privacy-Preserving Technologies
3.2.2 AI and Data Anonymization
3.3.1 Automated Asset Discovery
3.3.2 Purpose, Importance, and Components of Asset Management
3.3.3 AI-Driven Asset Monitoring - Part 1
3.3.4 AI-Driven Asset Monitoring - Part 2
3.4.1 Case Studies and Best Practices
Module 4: Security Architecture and Engineering with AI
4.1.1 Secure System Design
4.1.2 AI-Driven Security Architecture
4.1.3 Advantages and Implications
4.2.1 Cryptographic Techniques
4.2.2 Encryption Types
4.3.1 Automated Vulnerability Scanning - Part 1
4.3.2 Automated Vulnerability Scanning - Part 2
4.4.1 Access Control Models
4.4.2 Logging and Monitoring Security Events
4.4.3 Trust Models with AI Integration - Part 1
4.4.4 Trust Models with AI Integration - Part 2
4.5.1 AI-driven Exploit Generation
4.5.2 Adaptive Penetration Testing
4.5.3 Ethical Implications and Responsible AI Usage
Module 5: Communication and Network Security
5.1.1 Network Protocols and Architecture
5.1.2 Network Threat and Vulnerabilities
5.2.1 Network Traffic Analysis
5.2.2 AI in Intrusion Detection Systems (IDS)
5.3.1 AI in Firewalls and Intrusion Prevention Systems (IPS)
5.3.2 Automated Threat Hunting
5.4.1 Ensuring Network Security Compliance
5.4.2 Reporting and Audit Trails
Module 6: Identity and Access Management (IAM)
6.1.1 Authentication and Authorization
6.1.2 Identity Management Lifecycle
6.2.1 Biometric Authentication Using AI
6.2.2 AI-Enhanced Multi-Factor Authentication (MFA)
6.3.1 Discretionary Access Control (DAC)
6.3.2 Mandatory Access Control (MAC)
6.3.3 Role-Based Access Control (RBAC)
6.3.4 Dynamic Access Control Models
6.4.1 Identity Theft and AI Defenses
6.4.2 AI in Managing Insider Threats
Module 7: Security Assessment and Incident Response with AI
7.1.1 Penetration Testing
7.1.2 Vulnerability Assessment
7.2.1 Automated Testing Tools
7.2.2 AI-Driven Security Assessments
7.3.1 Real-Time Security Monitoring
7.3.2 AI in Threat Intelligence
7.4.1 Incident Response Lifecycle
7.4.2 Creating an Incident Response Plan
7.4.3 Incident Detection
7.4.4 Automated Incident Response
7.5.1 Detection and Analysis
7.5.2 Containment, Eradication, and Recovery
7.5.3 Post-Incident Activities
7.6.1 Reporting Requirements
7.6.2 Cooperation with Law Enforcement
7.6.3 Automated Compliance Checks
7.6.4 AI in Audit Processes
Module 8: Security Operations
8.1.1 SOC Roles and Responsibilities
8.1.2 AI in SOC Operations
8.2.1 Data Sensitivity and Classification
8.2.2 Encryption and Data Masking
8.2.3 Secure Data Disposal
8.3.1 Principles of Data Privacy
8.3.2 Privacy Impact Assessments (PIAs)
8.3.3 Privacy by Design and Default
8.4.1 Purpose, Importance, and Components
8.4.2 AI in Disaster Recovery Planning
8.4.3 AI-Driven Business Continuity
8.5.1 Security Automation
8.5.2 AI in Security Workflow Management
Module 9: Software Development Security and Audit with AI
9.1.1 Secure Coding Practices
9.2.1 Automated Application Scanning
9.2.2 Vulnerability Discovery
9.3.1 Integrating AI in CI/CD Pipelines
9.3.2 AI in Continuous Security Testing
9.4.1 Identifying Software Threats
9.4.2 Mitigating Risks with AI
9.5.1 Preparing for Audits
9.5.2 Conducting Audits
9.5.3 Audit Reporting and Follow-Up
9.6.1 Security Information and Event Management (SIEM)
9.6.2 Continuous Diagnostics and Mitigation (CDM)
9.6.3 Compliance Monitoring Tools
Module 10: Future Trends in AI and Cybersecurity Compliance
10.1.1 Data Management Policy
10.1.2 Password Policy
10.1.3 User Management Policy
10.1.4 Bring Your Own Device (BYOD) Policy
10.1.5 Privacy Policy
10.2.1 Importance of Training
10.2.2 Concepts and Implications of Social Engineering Attacks
10.3.1 AI Advancements
10.3.2 Potential Impact on Cybersecurity Compliance
10.4.1 Ethical AI in Cybersecurity Compliance
10.4.2 Developing AI Governance Frameworks
10.5.1 Practical Simulations with AI Tools
10.5.2 Real-World Scenarios and Problem Solving
Course Summary
Course Summary
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AI+ Security Compliance™ Spanish
