AI+ Security™: Level 1
What's included?
- Certification Blueprint
- E-courseware
- Course Resources
- Certification
- Proctoring 365
$300.00
- modules: 11
- Examination: 1
- Examination Time: 50 MCQs, 90 Minutes
- Passing Score: 70%
About Certification
Our comprehensive course, AI+ Security level 1 offers professionals a thorough exploration of the integration of AI and Cybersecurity. Beginning with fundamental Python programming tailored for AI and Cybersecurity applications, participants delve into essential AI principles before applying machine learning techniques to detect and mitigate cyber threats, including email threats, malware, and network anomalies. Advanced topics such as user authentication using AI algorithms and the application of Generative Adversarial Networks (GANs) for Cybersecurity purposes are also covered, ensuring participants are equipped with cutting-edge knowledge. Practical application is emphasized throughout, culminating in a Capstone Project where attendees synthesize their skills to address real-world cybersecurity challenges, leaving them adept in leveraging AI to safeguard digital assets effectively.
Prerequisites
- Interest in learning about machine learning, deep learning, and natural language processing.
- Basic knowledge computer science, no technical knowledge required
- Curiosity and openness to learning about new concepts and technologies
- Willingness to explore ethical considerations and legal frameworks surrounding the use of AI and data privacy
Certification Modules
- 1.1 Definition and Scope of Cyber Security
- 1.2 Key Cybersecurity Concepts
- 1.3 CIA Triad (Confidentiality, Integrity, Availability)
- 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
- 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
- 1.6 Importance of Cybersecurity in Modern Enterprises
- 1.7 Careers in Cyber Security
- 2.1 Core OS Functions (Memory Management, Process Management)
- 2.2 User Accounts and Privileges
- 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
- 2.4 OS Security Features and Configurations
- 2.5 Hardening OS Security (Patching, Disabling
Unnecessary Services) - 2.6 Virtualization and Containerization Security
Considerations - 2.7 Secure Boot and Secure Remote Access
- 2.8 OS Vulnerabilities and Mitigations
- 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
- 3.2 Network Devices and Their Roles (Routers, Switches,
Firewalls) - 3.3 Network Security Devices (Firewalls, IDS/IPS
- 3.4 Network Segmentation and Zoning
- 3.5 Wireless Network Security (WPA2, Open WEP
vulnerabilities) - 3.6 VPN Technologies and Use Cases
- 3.7 Network Address Translation (NAT)
- 3.8 Basic Network Troubleshooting
- 4.1 Types of Threat Actors (Script Kiddies, Hacktivists,
Nation-States) - 4.2 Threat Hunting Methodologies using AI
- 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS
- 4.4 Open-Source Intelligence (OSINT) Techniques
- 4.5 Introduction to Vulnerabilities
- 4.6 Software Development Life Cycle (SDLC) and Security
Integration with AI - 4.7 Zero-Day Attacks and Patch Management Strategies
- 4.8 Vulnerability Scanning Tools and Techniques using AI
- 4.9 Exploiting Vulnerabilities (Hands-on Labs)
- 5.1 An Introduction to AI
- Types and Applications of AI
- 5.3 Identifying and Mitigating Risks in Real-Life
- 5.4 Building a Resilient and Adaptive Security Infrastructure
with AI - 5.5 Enhancing Digital Defenses using CSAI
- 5.6 Application of Machine Learning in Cybersecurity
- 5.7 Safeguarding Sensitive Data and Systems Against
Diverse Cyber Threats - 5.8 Threat Intelligence and Threat Hunting Concepts
- 6.1 Introduction to Python Programming
- 6.2 Understanding of Python Libraries
- 6.3 Python Programming Language for Cybersecurity
Applications - 6.4 AI Scripting for Automation in Cybersecurity Tasks
- 6.5 Data Analysis and Manipulation Using Python
- 6.6 Developing Security Tools with Python
- 7.1 Understanding the Application of Machine Learning in
Cybersecurity - 7.2 Anomaly Detection to Behavior Analysis
- 7.3 Dynamic and Proactive Defense using Machine
Learning - 7.4 Utilizing Machine Learning for Email Threat Detection
- 7.5 Enhancing Phishing Detection with A
- 7.6 Autonomous Identification and Thwarting of Email
Threats - 7.7 Employing Advanced Algorithms and AI in Malware
Threat Detection - 7.8 Identifying, Analyzing, and Mitigating Malicious
Software - 7.9 Enhancing User Authentication with AI Techniques
- 7.10 Penetration Testing with AI
- 8.1 Incident Response Process (Identification, Containment,
Eradication, Recovery) - 8.2 Incident Response Lifecycle
- 8.3 Preparing an Incident Response Plan
- 8.4 Detecting and Analyzing Incidents
- 8.5 Containment, Eradication, and Recovery
- 8.6 Post-Incident Activities
- 8.7 Digital Forensics and Evidence Collection
- 8.8 Disaster Recovery Planning (Backups, Business
Continuity) - 8.9 Penetration Testing and Vulnerability Assessments
- 8.10 Legal and Regulatory Considerations of Security
Incidents
- 9.1 Introduction to Open-Source Security Tools
- 9.2 Popular Open Source Security Tools
- 9.3 Benefits and Challenges of Using Open-Source Tools
- 9.4 Implementing Open Source Solutions in Organizations
- 9.5 Community Support and Resources
- 9.6 Network Security Scanning and Vulnerability Detection
- 9.7 Security Information and Event Management (SIEM)
Tools (Open-Source options) - 9.8 Open-Source Packet Filtering Firewalls
- 9.9 Password Hashing and Cracking Tools (Ethical Use)
- 9.10 Open-Source Forensics Tools
- 10.1 Emerging Cyber Threats and Trends
- 10.2 Artificial Intelligence and Machine Learning in
Cybersecurity - 10.3 Blockchain for Security
- 10.4 Internet of Things (IoT) Security
- 10.5 Cloud Security
- 10.6 Quantum Computing and its Impact on Security
- 10.7 Cybersecurity in Critical Infrastructure
- 10.8 Cryptography and Secure Hashing
- 10.9 Cyber Security Awareness and Training for Users
- 10.10 Continuous Security Monitoring and Improvement
- 11.1 Introduction
- 11.2 Use Cases: AI in Cybersecurity
- 11.3 Outcome Presentation
Certification outcome
Upon successful completion of the AI+ Security level 1 course, participants will be awarded a certificate attesting to their proficiency in Python programming for AI and Cybersecurity applications, mastery in applying machine learning techniques to identify and mitigate cyber threats, including email threats, malware, and network anomalies, familiarity with advanced AI techniques such as Generative Adversarial Networks (GANs) for cybersecurity enhancement, practical skills in conducting penetration testing using AI methodologies, and the ability to synthesize acquired knowledge through a Capstone Project addressing real-world cybersecurity challenges. This certificate validates the participant's competence in leveraging Artificial Intelligence to fortify cybersecurity measures and their preparedness to confront the dynamic complexities of modern digital security landscapes.
Market Insight
AI and Cybersecurity integration is booming as organizations adapt to evolving cyber threats. The global AI in cybersecurity market is set to expand significantly, driving demand for skilled professionals. Initiatives like "Introduction to AI and Cyber Security" are pivotal in preparing professionals to harness AI for robust cyber defense.
Value Proposition
AI+ Cybersecurity empowers professionals with essential skills to protect against evolving cyber threats. By merging AI principles with cybersecurity practices, participants gain practical expertise in Python programming, machine learning, and advanced AI algorithms. Stay ahead in today's digital landscape with our hands-on training and drive innovation within your organization.
Additional Features
Alongside comprehensive AI and cybersecurity training, AI+ Cybersecurity offers interactive labs, expert-led discussions, and career development resources for professional growth within the cybersecurity field. Ongoing support from instructors and access to the latest tools ensure participants stay updated and equipped to address.
evolving cyber threats while driving innovation
