Training: AI inzetten voor Enterprise Security

Generative artificial intelligence (AI) is cutting-edge technology that is commonly used to optimize content creation, product design, and customer experience enhancement for everyday businesses. In this course, you will explore strategies that help leverage the power of generative AI to reshape your organization's cybersecurity solutions and processes. Discover basic concepts of artificial intelligence and discuss the effectiveness of various AI-based security measures based on real-life case studies. Consider the strengths and weaknesses of AI and generative AI in security-related scenarios and discover how to classify types of threats that AI and generative AI can help mitigate. Learn how AI can be used for threat classification, detection, and prevention, and explore ethical considerations when employing AI in cybersecurity. Lastly, look at implementing AI in hypothetical cybersecurity scenarios, and discover future trends that may intersect AI and cybersecurity based on current industry advancements.

Generative AI is artificial intelligence technology commonly geared towards creating content, however, it also has the potential to impact everyday business activities in many areas. In this course, you'll learn about commonly used tools in cybersecurity, their primary functions, and the potential benefits of enhancing cybersecurity tools with AI and generative AI. Explore the limitations of traditional security tools that can be addressed with AI and how to classify various types of AI models suitable for different security tools and contexts. Finally, discover AI integration challenges, tool maintenance, and migration planning, and explore how AI can enhance the predictive capabilities of common security tools leading to more robust and proactive cybersecurity measures.

Identity security is used to secure access to digital information or services based on the authenticated identity of an entity. Many identity security solutions can leverage artificial intelligence (AI) to help streamline processes and provide actionable insights to administrators and users. In this course, you'll discover common use cases for identity security, including securing DevOps, enabling access, and enforcing privilege. You will explore the key benefits and possible challenges of identity security that can be addressed by AI and ethical and privacy considerations when using AI for identity security. Next, you will discover strategies for integrating AI into existing identity security frameworks and how to predict future trends in AI-powered identity security based on current industry advancements. Lastly, you will explore the potential of AI in preventing identity theft and other related security threats.

Organizations go to great lengths to protect email accounts and communications from unauthorized access, loss, or compromise. Prioritizing email security helps ensure confidentiality, data protection, business continuity, and malware defense. In this course, you will explore common email threats to organizations including URL phishing, spear phishing, brand impersonation, malware, and spam. You'll discover how email security enhanced by artificial intelligence (AI) can help reduce administrative efforts and strengthen an organization's security posture. Next, you will explore various AI approaches suitable for enhancing email security, and discover challenges and risks associated with an ever-evolving email threat landscape. Then, you will learn the benefits of automating email security, including greater cost savings and cyber resilience, and discover privacy implications and potential drawbacks when using AI for email security. Lastly, you will explore future trends in AI-powered email security based on current industry advancements and discover the importance of education and awareness when it comes to email security.

Data protection principles are fundamental guidelines that ensure the security, privacy, and integrity of data. As organizations increasingly rely on data analytics to extract valuable insights, it becomes crucial to prioritize data protection. In this course, you'll learn how artificial intelligence (AI) can be leveraged to enhance data protection and validation. Explore the principles of data protection including data availability, data life cycle management, and information life cycle management, and discover key areas where AI can enhance user data protection and validation. Next, consider the potential benefits and risks of leveraging AI for user data protection and validation and discover how to protect sensitive data and AI models. Lastly, explore common AI security risks such as AI model attacks, data security risks, code maintainability, and supply chain complexity, and consider how data integrity measures and AI can work hand in hand to improve an organization's security posture.

Authentication refers to the process of validating the identity of a registered user or process to ensure a subject is who they claim to be. In this course, you will explore the role of artificial intelligence (AI) in authentication and how it can be leveraged to confirm a user's identity through a selfie, fingerprint, or voice recognition. Discover common authentication vulnerabilities, including SQL injection, username enumeration, and weak login credentials, and investigate how behavioral biometrics can analyze a user's physical and cognitive behavior to determine threats. Learn about other areas to consider when building a secure authentication solution, including anomaly detection, adaptive authentication, voice recognition, and facial recognition. Finally, find out how to detect security threats using AI and examine how AI models can streamline a common authentication process.

Instruction detection systems (IDSs) can help organizations monitor networks or systems for malicious activity or policy violations and alert them when such activity is discovered. In this course, you will explore the key roles artificial intelligence (AI) plays in cybersecurity, including prediction, detection, and response. Then you will discover key differences between intrusion detection systems, vulnerability management systems, behavioral analytics, and security auditing systems. Next, you will investigate intrusion detection system types like network intrusion, network node, host intrusion, protocol-based, and application protocol-based intrusion detection systems. Examine intrusion detection system methods, such as signature-based intrusion, anomaly-based, and hybrid intrusion detection, and take a look at AI-based intrusion detection benefits and challenges. Finally, focus on how AI can detect and prevent security threats, and dig into possible future trends related to security threats.

Final Exam: Enhancing Enterprise Security with Generative AI and AI will test your knowledge and application of the topics presented throughout the Enhancing Enterprise Security with Generative AI and AI journey.