AI Security Level 1 Executive Summary

2. TABLE OF CONTENTS Introduction............................................................................................................. 1 Certification Goals and Learning Outcomes..................................................... 2 The Impact of AI on Modern Business Practices............................................. 3 What is Next for AI?............................................................................................... 4 How AI Transforms Cybersecurity....................................................................... 4 How AI Addresses Current Challenges in Cybersecurity............................... 5 How Cybersecurity Industries are Adopting AI................................................ 5 How to Integrate AI in Cybersecurity................................................................. 6 Module 1: Introduction to Cyber Security.......................................................... 7 Module 2: Operating System Fundamentals..................................................... 7 Module 3: Networking Fundamentals................................................................ 7 AI+ Security Level 1 Module 4: Threats, Vulnerabilities, and Exploits............................................. 8 Module 5: Understanding of AI and ML.............................................................. 8 Module 6: Python Programming Fundamentals.............................................. 8 Module 7: Applications of AI in Cybersecurity................................................. 9 Module 8: Incident Response and Disaster Recovery .................................... 9 Module 9: Open-Source Security Tools.............................................................. 9 Module 10: Securing the Future........................................................................... 10 Module 11: Capstone Project................................................................................. 10 How Can AI CERTs Help in Building an AI-Ready Culture?............................ 10 Copyright© 2024. All rights reserved

3. Introduction Acquiring an understanding of the interaction between cybersecurity and Artificial Intelligence (AI) is becoming more and more important as AI plays a key role in improving security protocols. Vast amounts of data may be processed by AI-driven technologies, which can then predict dangers, identify abnormalities, and automate responses with previously unheard-of speed and precision. The AI+ Security V0 Certification gives you the fundamental know-how and abilities to handle this complex intersection. The capacity to use AI technology to enhance threat detection, response capabilities, and overall security posture is validated by a certification in this field. In addition to showcasing proficiency in fusing AI with cybersecurity tactics, it puts experts at the forefront of a developing industry, making them invaluable resources for enterprises looking to defend against advanced cyber threats. The rising sophistication and frequency of cyber threats in today’s environment make the AI+ Security course extremely important. It gives professionals the know-how to use AI for anomaly detection, proactive threat detection, and real-time response—all critical for safeguarding sensitive information and systems. Organizations can improve their defenses, adjust to changing threats, and guarantee strong security infrastructures by combining AI with cybersecurity. This course is crucial for staying ahead in the quickly evolving digital landscape because it tackles the growing need for sophisticated cybersecurity solutions. AI+ Security Level 1 The following topics are analyzed in detail in this certification. Essential Cyber Security Concepts Core Operating System Fundamentals Networking Fundamentals Basic AI and ML Concepts Threat Analysis and AI Vulnerability Management and AI Open-Source Security Tools Python Programming The Future of AI+ Security Certification Prerequisites Interest in AI Technologies: Interest in learning about AI technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). Tech Comfort: Basic knowledge about the fundamentals of computer science. Learning Mindset: Curiosity and openness to learn about new concepts and technologies. Ethical Awareness: Willingness to explore ethical considerations and legal frameworks surrounding the use of AI and data privacy. 1 Copyright© 2024. All rights reserved

4. Who Should Enroll? Cybersecurity Professionals and Analysts: Stay updated on the latest AI-driven tools and techniques. Penetration Testers: Gain insights into how AI can enhance their ethical hacking practices. Security Consultants: Integrate AI into security solutions to advise on cutting- edge technologies. Incident Responders: Learn how AI can streamline incident analysis and improve response efficiency. Security Engineers: Develop expertise in developing AI-based technologies and infrastructures. Threat Hunters: Enhance capabilities in using AI and ML for proactive threat identification. Compliance Auditors: Understand AI’s role in ensuring compliance and performing risk assessments. Network Security Administrators: Gain skills in implementing AI-driven network security measures. Forensic Analysts: Explore how AI can aid in digital investigations and enhance forensic analysis. IT Professionals and System Administrators: Use AI to detect and respond to threats more effectively and efficiently. Risk Management Specialists: Implement AI to better assess and mitigate risks. Business Leaders and Decision Makers: Understand AI in cybersecurity to make informed decisions about investments and strategies. Software Developers: Understand AI-integration engaged in security tools and applications. AI+ Security Level 1 Certification Goals and Learning Outcomes Demonstrate a thorough understanding of fundamental cybersecurity principles and effectively apply these concepts to protect data, networks, and systems from a wide range of digital threats. Understand fundamental Operating System (OS) functions and the associated security considerations necessary for protecting OS environments. Leverage AI and ML techniques to enhance threat detection, automate security tasks, and improve response capabilities, applying these technologies to real- world security challenges and evaluating their effectiveness. Recognize essential networking concepts and their critical roles in maintaining network security, including how they interact with various security measures. Master Python to create custom security tools, automate tasks, and analyze data, including network communication, cryptography, and log analysis. 2 Copyright© 2024. All rights reserved

5. Evaluate different types of threat actors, their motives, and their impact on security, enabling a deeper understanding of their behavior and strategies. Design and implement effective incident response plans and disaster recovery strategies, utilizing open-source tools and best practices to manage and mitigate the impact of security breaches and ensure business continuity. The Impact of AI on Modern Security Practices With an estimated market size of $102 billion in 2032, it is no secret that AI technology is revolutionizing every business. It has significantly contributed to technical and economic growth over the past decade. With its progression from simple rule-based systems to sophisticated ML and DL models, AI has profoundly changed contemporary business. AI was once limited by small amounts of data and computer capacity, but today, it can leverage enormous datasets and potent algorithms to promote efficiency and creativity. AI helps businesses make data- driven decisions, automate processes, and provide individualized customer experiences, all of which boost output and give them a competitive edge. The influence of AI on operational procedures and strategic insights will only increase as it develops, posing both new opportunities and challenges for businesses. AI+ Security Level 1 Figure 1: AI Cybersecurity Market Source: Markets and Markets 3 Copyright© 2024. All rights reserved

6. A report by Markets and Markets estimates that the market for AI in cybersecurity was worth $22.4 billion in 2023 and is projected to expand at a Compound Annual Growth Rate (CAGR) of 21.9% from 2023 to 2028. According to the prediction, its revenue is expected to reach $60.6 billion by 2028. This quick expansion highlights AI’s growing influence and significance in the field of cybersecurity. What is Next for AI? Thanks to developments in ML, NLP, and robotics, AI is expected to undergo revolutionary growth in the future. AI systems are likely to get better at comprehending context and reasoning as they develop in sophistication, which will result in more naturalistic human-computer interactions. The landscape will be shaped by innovations like ethical AI frameworks to ensure responsible usage and generalized AI, which strives to perform any intellectual task that a human can. Furthermore, it is anticipated that AI’s integration with cutting-edge technologies like 5G and quantum computing could open up new possibilities, from transforming entire industries to solving difficult global issues. The emphasis will also be on developing AI systems that minimize risks and maximize benefits in a transparent, equitable, and human values-aligned manner. AI+ Security Level 1 How AI Transforms Cybersecurity AI is transforming cybersecurity by improving several facets of threat identification, response, and defense in general. It impacts cybersecurity by using advanced ML and data analysis to quickly identify and respond to threats. By compiling and analyzing data from several sources, it improves threat intelligence and expedites incident response to deal with breaches. AI also provides a dynamic and robust defense by adapting to changing threats. AI algorithms can analyze vast amounts of data in real-time to identify and respond to threats faster. Real-Time Threat Detection AI enhances threat intelligence by aggregating and analyzing data from multiple sources. Advanced Threat Intelligence AI can automate routine security tasks and responses, allowing security teams to focus on more complex issues. Automated Incident Response AI systems can monitor user and system behavior to identify deviations that may indicate malicious activity. Behavioral Analysis AI-driven methods, such as biometric recognition, improve user authentication processes. Enhanced User Authentication Proactive Defense and Vulnerability Management AI can predict vulnerabilities by analyzing historical data and scanning for weaknesses. Figure 2: Transformation of Cybersecurity with AI 4 Copyright© 2024. All rights reserved

7. In essence, AI transforms cybersecurity by making threat detection more accurate and responsive, automating critical tasks, and providing deeper insights into potential threats and vulnerabilities. How AI Addresses Current Challenges in Cybersecurity AI is transforming the cybersecurity space by resolving some of its most significant issues. Traditional methods may not be able to keep up with the complexity and speed of modern threats, which can lead to issues like high false positive rates, delays in real-time detection, and difficulties managing insider threats. AI enhances cybersecurity by providing state-of-the-art solutions for automating incident response, reducing false positives using ML, integrating threat intelligence, and identifying threats in real time. Identifying and responding to cyber threats as they occur can be difficult due to the high volume of network traffic and potential attack vectors. AI algorithms analyze network traffic and user behavior to identify and alert on suspicious activities in real-time, enhancing immediate threat detection. Real-Time Threat Detection AI Solution Traditional security systems often generate numerous false positives, overwhelming security teams and causing alert fatigue. False Positives in Alerts AI systems use ML to refine threat detection models, reducing the frequency of false positives and improving the accuracy of security alerts. AI+ Security Level 1 AI Solution Complexity of Threat Intelligence Aggregating and analyzing diverse sources of threat intelligence can be overwhelming and time- consuming, leading to incomplete threat assessments. AI consolidates and analyzes data from multiple threat intelligence sources to provide actionable insights and a comprehensive view of emerging threats. AI Solution Incident Response Speed Manual incident response can be slow and reactive, prolonging the time systems remain vulnerable after a breach. AI-driven automation responds to incidents swiftly by isolating affected systems and deploying countermeasures, reducing the time to mitigate threats. AI Solution Detecting malicious or negligent actions by insiders is challenging due to their access to legitimate systems and data. AI monitors and analyzes user behavior patterns to detect anomalies indicative of insider threats, enabling early intervention and prevention. Managing Insider Threats AI Solution Figure 3: Addressing Current Business Challenges in Cybersecurity Using AI Organizations may greatly increase their defense against complex attacks, expedite security procedures, and react to new threats with greater efficacy by utilizing AI. How Cybersecurity Industries are Adopting AI Businesses in the cybersecurity industry are increasingly employing AI to better combat new and advanced threats. Modern cyberattacks are often more sophisticated and numerous than older security systems can handle, which causes a higher rate of false positives and lengthier reaction times. AI is drastically altering this environment since it provides advanced skills for identifying, responding to, and managing threats. 5 Copyright© 2024. All rights reserved

8. The application of AI-powered threat detection systems to enhance responses is one well-known example. For instance, Darktrace employs AI to monitor network behavior in real-time, identifying deviations that may signal cyber threats like insider attacks or advanced persistent threats. Similarly, CrowdStrike’s Falcon platform uses ML to detect malware and malicious activity on endpoints, outperforming traditional methods that rely solely on known signatures. Phishing detection and automated incident response are also benefiting from AI advancements. Proofpoint uses AI to scrutinize emails for phishing attempts, blocking malicious messages before they reach users. On the response side, IBM QRadar leverages AI to automate incident response workflows, streamlining the process by correlating security data, prioritizing alerts, and suggesting automated actions to mitigate threats efficiently. Moreover, AI is enhancing vulnerability management and behavioral analytics. Sumo Logic applies AI to user and entity behavior analytics, identifying suspicious activities that deviate from normal patterns, such as unusual data access by an employee. Qualys integrates AI to prioritize vulnerabilities based on their likelihood of exploitation, allowing organizations to address the most critical issues promptly. AI+ Security Level 1 These innovations collectively strengthen cybersecurity defenses, making AI an indispensable tool in the modern security landscape. How to Integrate AI in Cybersecurity In order to guarantee successful deployment and optimize benefits, integrating AI into cybersecurity operations requires a number of strategic measures. This is a how- to guide that can aid businesses incorporate AI into this field: Define Objectives Assess Current Security Infrastructure Select AI Tools and Platforms Collect and Prepare the Data Develop and Train Models Integrate AI Models Monitor and Optimize Ensure Security and Compliance Train and Educate Teams Evaluate and Iterate Figure 4: Integrating AI in Cybersecurity By following these steps, businesses can effectively integrate AI into their cybersecurity protocols, enhancing capabilities and deriving greater value and insight from their investments. 6 Copyright© 2024. All rights reserved

9. A Brief Summary of AI+ Security L 1 Certification At AI CERTs, we empower organizations to unlock the potential of AI with our industry-leading suite of role-based certifications. The AI+ Security VO modules present a holistic insight into this evolving domain, ensuring agile preparedness for modern cybersecurity challenges and emerging threats. Module 1: Introduction to Cybersecurity This module provides a comprehensive overview of cybersecurity, emphasizing its role in protecting data, networks, and systems from digital threats. It covers key domains such as information, network, application, and cloud security. Historical milestones in cybersecurity are reviewed to provide context. The module also explores major cybersecurity frameworks and examines relevant laws and regulations, focusing on compliance and legal implications. It addresses the critical role of cybersecurity in maintaining business continuity, reputation, and financial stability. The module concludes with an overview of career opportunities in cybersecurity, including roles, required skills, certifications, and professional development pathways. AI+ Security Level 1 Module 2: Operating System Fundamentals This module covers fundamental Operating System (OS) functions and security practices. It begins with an exploration of core OS functions and then shifts to user account management, emphasizing the importance of least privilege, strong passwords, and regular audits. It also examines various access control mechanisms highlighting their benefits and appropriate use cases. Security features and configurations are also addressed. The module covers OS hardening techniques, virtualization and containerization security, secure boot processes, and remote access protocols. It concludes with strategies for identifying and managing OS vulnerabilities, ensuring a robust system security posture. Module 3: Networking Fundamentals This module explores network design, security, and management, starting with fundamental network topologies and protocols. Understanding these frameworks and common protocols such as HTTP and DNS is crucial for effective network communication. Key network devices like routers, switches, and firewalls are examined, along with their roles in managing traffic and securing connections. 7 Copyright© 2024. All rights reserved

10. The module also covers network security devices, including IDS/IPS and UTM solutions, focusing on their configurations and functions. Further topics include network segmentation, wireless network security, and VPN technologies. Finally, the module addresses basic network troubleshooting, providing practical skills and tools like Ping and Wireshark for diagnosing network issues. Module 4: Threats, Vulnerabilities, and Exploits This module provides an in-depth look at threat actors, threat-hunting methodologies, and security practices. It begins by examining various threat actors and their tactics. It then explores how to better manage and counteract these threats. It covers threat-hunting methodologies and how AI aids in detecting threats through behavioral analysis and threat intelligence. Key AI tools are reviewed to enhance threat detection and response capabilities. The module also introduces OSINT techniques and tools, discusses vulnerabilities and their lifecycle, and integrates security into the SDLC with practices like DevSecOps. Finally, it explores vulnerability scanning tools, including hands-on labs with tools like Metasploit for practical experience in identifying and mitigating vulnerabilities. AI+ Security Level 1 Module 5: Understanding of AI and ML This module introduces AI and its integration into cybersecurity, starting with foundational concepts, history, and branches such as Machine Learning (ML) and Natural Language Processing (NLP). It also addresses ethical concerns related to AI, including bias and data privacy. It explores various AI types and applications, as well as their risks and mitigation strategies. It highlights how AI enhances security infrastructure through improved threat detection, anomaly detection, and adaptive defense mechanisms, supported by real-world case studies. Additionally, the module covers the role of AI in cybersecurity, focusing on its applications in threat intelligence, predictive analytics, and automation. It emphasizes continuous learning and improvement, and discusses the use of ML for intrusion detection, malware prevention, and phishing defense, concluding with AI- enhanced threat intelligence and data protection techniques. Module 6: Python Programming Fundamentals This module introduces Python programming basics, including syntax, data types, control structures, functions, and error handling. It covers essential Python libraries, such as NumPy for numerical tasks, Pandas for data analysis, Matplotlib for visualization, and Requests for web interactions. It explores Python’s application in cybersecurity. The module also covers automating cybersecurity tasks, such as vulnerability scanning, incident response, and integrating with security tools. 8 Copyright© 2024. All rights reserved

11. Further, it focuses on data analysis with Python, covering data import/export, cleaning, transformation, and visualization. Finally, it addresses developing security tools like custom scanners, brute force tools, keyloggers, forensics tools, and malware analysis scripts, with a special emphasis on ethical considerations and practical use. Module 7: Applications of AI in Cybersecurity This module covers how ML enhances cybersecurity. It begins with ML basics and its use cases, including anomaly detection and malware classification. Key areas include model training, feature engineering, and data preprocessing. It explores techniques for anomaly detection and behavior analysis, with a focus on real-time monitoring using tools like ELK Stack and Splunk. Proactive defense strategies using ML, threat intelligence integration, and AI-driven incident response are also discussed. The module addresses ML applications in email threat detection, phishing, and spam filtering, and covers autonomous threat identification and response. It also explores advanced malware detection and analysis improvements, and AI-powered penetration testing. Case studies provide practical examples of these applications. with AI, user authentication AI+ Security Level 1 Module 8: Incident Response and Disaster Recovery This module outlines the incident response process, focusing on identification, containment, eradication, and recovery. It covers preparing and maintaining an incident response plan, including defining roles, communication protocols, and regular testing. Post-incident reviews help improve response strategies and update security policies. The module also addresses digital forensics, disaster recovery, and legal considerations, emphasizing evidence collection, recovery planning, and regulatory compliance to manage security incidents effectively. Module 9: Open-Source Security Tools This module introduces open-source security tools, highlighting their benefits such as cost-effectiveness, flexibility, and community support, while also addressing challenges like integration and security concerns. It covers the evaluation of open- source tools and compares them with commercial options. Popular open-source tools discussed include Wireshark for network analysis, Nmap for scanning, and Metasploit for penetration testing. The module further explores deploying these tools in organizations, including strategies for integration, maintenance, and user training. It also covers SIEM tools like ELK Stack and forensics tools such as Autopsy, emphasizing their role in comprehensive security management and incident response. 9 Copyright© 2024. All rights reserved

12. Module 10: Securing the Future This module addresses emerging cyber threats and trends, covering advanced threats like ransomware and APTs, and the evolving attack vectors such as zero-day exploits and supply chain attacks. It emphasizes the need for proactive measures, including threat intelligence and continuous monitoring. It explores the role of AI and ML in enhancing cybersecurity, detailing their applications, challenges, and future trends. The module also covers blockchain technology’s potential to improve security through secure transactions and smart contracts, alongside its limitations. Additionally, the module discusses IoT and cloud security, highlighting the unique challenges and protective measures required for these environments. It also touches on the impact of quantum computing on cryptography, the importance of securing critical infrastructure, and the need for ongoing cybersecurity training and awareness. Continuous security monitoring and improvement are stressed as essential for adapting to new threats and maintaining robust defenses. Module 11: Capstone Project The Capstone Project is essential to the program, synthesizing course material. This project helps learners set goals, prepare methods, and design their final project. The module covers the capstone project, detailing its purpose to integrate and apply course knowledge through defined phases: planning, research, implementation, and presentation. It emphasizes teamwork, communication for project success. AI+ Security Level 1 resource utilization, and effective It explores the application of AI in cybersecurity, highlighting its use in threat detection, incident response, vulnerability management, security automation, and threat intelligence. Finally, it focuses on presenting the project outcome, including thorough documentation, effective presentation skills, and the importance of feedback and reflection. It aims to help students communicate their findings clearly and assess their learning and growth throughout the project. How Can AI CERTs Help in Building an AI-Ready Culture? Businesses face numerous challenges when implementing AI technologies in cybersecurity, despite the advantages they offer. Implementing AI is frequently hampered by integration problems, skill shortages, and complex data. We at AI Certs are aware of these difficulties and have tailored our certification programs to assist businesses in successfully resolving them. 10 Copyright© 2024. All rights reserved

13. Share of employers saying lacking Al skills is a barrier to adopt Al (2023) Why do companies struggle to adopt Al technologies? (2023) %of Employer 33% Skills Gap 48% 49% 47% 37% 41% 42% ◄ 42% 33% Data Complexity 25% Integration 22% Challenges 21% Lack of Tools Germany Ireland Canada France USA Austria UK 21% Cost Barrier employers of the major 42% economies believe a shortage of Al skills %of Employer Figure 5: Factors driving the lack of adoption of AI Technologies Source: OCED and IBM Bridging the AI Skills Gap Challenge: It is challenging to find professionals who are conversant and comfortable with the application of AI in cybersecurity, which makes it difficult for businesses to design, create, and oversee AI-driven security solutions. Solution: AI CERTs provide comprehensive training programs encompassing foundational AI disciplines like ML, data analysis, NLP, and AI ethics. Benefit: This structured learning equips your workforce with the knowledge and skills necessary to understand, implement, and manage AI solutions within your organization, effectively bridging the critical AI skills gap AI+ Security Level 1 Continuous Learning for Long Term Success Challenge: Organizations face continuous challenges with AI, including changing threat landscapes, data management, and a significant requirement for computational power and experience. Solution: Knowing that AI is a rapidly evolving field, AI CERTs offer ongoing learning opportunities through advanced courses, workshops, and seminars. Benefit: By continuously staying current on AI trends and technologies, your workforce maintains its competitive edge, promoting long-term success in the ever-changing AI landscape. At AI CERTs, we offer a strategic solution, fostering a culture primed for AI integration and innovation. Our premium AI certification programs provide the comprehensive training and industry-recognized credentials needed to empower your workforce and propel your organization toward an AI-driven future. 11 Copyright© 2024. All rights reserved

14. AI CERTs Cultivate AI Culture in Several Ways: Our structured curriculum promotes a deep understanding of AI concepts and applications, making AI less intimidating and more accessible. Our commitment to lifelong learning ensures your workforce remains current on the latest AI trends, maintaining a competitive edge. By fostering collaboration through teamwork and cross-functional projects, AI CERTs programs encourage knowledge sharing and break down departmental silos – critical aspects for successful AI implementation. AI CERTs: Your Pathway to Becoming AI-Ready The future of business belongs to Bitcoin users. Tailored for Success: Our programs are not one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles. Actionable Expertise: Forget theory alone. We focus on practical, hands-on learning through real-world projects and case studies. This ensures your team graduates with the skills and confidence to implement and utilize AI technologies effectively, driving innovation and tangible results for your organization. AI+ Security Level 1 Become an AI Leader: Do not just keep pace with the AI revolution, lead it. Partner with AI CERTs and invest in your workforce's future. Let us build an AI-inclusive culture together, where your team is equipped to unlock the transformative potential of AI and propel your organization to the forefront 12 Copyright© 2024. All rights reserved

15. Get Started Our extensive portfolio of AI and Blockchain can help you make future ready. Essentials Professional Certification Portfolio Business Design & Creative Learning & Education AI+ Security Level 1 Specialization Data & Robotics Technology Certification Portfolio Development Security Cloud Blockchain & Bitcoin For more details visit: AI CERTs 13 Copyright© 2024. All rights reserved

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