Final Exam: Enhancing Enterprise Security with Generative AI and AI

WHAT YOU WILL LEARN

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  Recognize the growing role artificial intelligence (ai) is playing in cybersecurity
  

  identify ethical considerations when employing ai in cybersecurity
  

  formulate strategies for integrating ai into existing cybersecurity frameworks
  

  outline the role of ai in enhancing various aspects of cybersecurity, such as threat detection and prevention
  

  classify the types of threats that ai and generative ai can help mitigate
  

  outline a basic plan for implementing ai in a hypothetical cybersecurity scenario
  

  use various prompts to get answers in huggingchat
  

  use ai to enhance an existing security tool or process
  

  list commonly used tools in cybersecurity and their primary functions
  

  troubleshoot common issues in ai-enhanced security tools
  

  compare ai-enhanced security tools to a traditional one handling the same threat
  

  interpret the output of a generative ai model in a security scenario
  

  integrate a simple ai model with a common security tool
  

  design a migration plan for transitioning from a traditional security tool to an ai-enhanced one
  

  formulate strategies for maintaining and updating ai-enhanced security tools
  

  outline potential challenges and solutions when integrating ai with existing security tools
  

  classify various types of ai models suitable for different security tools and contexts
  

  identify strategies for integrating ai into existing identity security frameworks
  

  recognize ethical and privacy considerations relate to the use of ai for identity security
  

  describe how generative ai can have a real-world impact on many industries
  

  predict future trends in ai-powered identity security based on current industry advancements
  

  compare ai-powered and traditional identity verification methods in action
  

  outline the potential of ai in preventing identity theft and other related security threats
  

  use ai to detect potential identity threats
  

  outline how to use ai for identity verification
  

  discuss how ai email security can help reduce administrative efforts and strengthen an organizations security posture
  

  outline various ai approaches suitable for enhancing email security
  

  demonstrate how an ai model can detect spam and phishing emails and enhance predictive capabilities
  

  discuss challenges and risks associated with an ever evolving email threat landscape
  

  exhibit a comparison of ai-powered and traditional email security measures in action
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  list common benefits when automating email security including greater cost savings and cyber resilience
  

  discuss the privacy implications and potential drawbacks of using ai for email security
  

  list common email threats to organizations including url phishing, spear phishing, brand impersonation, malware, and spam
  

  show how to ensure ai data model integrity
  

  formulate strategies for integrating ai into existing data protection and validation frameworks
  

  discuss ethical considerations and potential bias when using ai for user data protection
  

  analyze the potential benefits and risks of leveraging ai for user data protection and validation
  

  provide an overview of data protection and validation
  

  exhibit a comparison of ai-powered and traditional methods of user data protection in action
  

  describe how to protect sensitive data and ai models
  

  discuss factors when designing ai with data privacy
  

  identify key areas where ai can enhance user data protection and validation
  

  differentiate between data protection and data privacy
  

  list common authentication vulnerabilities including sql injection, username enumeration, and weak login credentials
  

  describe ai's role in authentication
  

  outline how adaptive authentication can be used to verify user identity
  

  provide an overview of behavioral biometrics and how it can analyze a user’s digital physical and cognitive behavior to determine threats
  

  outline how continuous authentication assesses user behavior patterns on an ongoing basis to identify potential risk factors
  

  describe how ai authentication systems can help mitigate ai bias
  

  outline how speech recognition has become a significant part of ai applications
  

  describe how anomaly detection can be used to identify unusual or suspicious activity
  

  discuss the three key roles ai plays in cybersecurity: prediction, detection, response
  

  discuss challenges of ai-based intrusion detection
  

  recognize how ai can detect and prevent security threats
  

  differentiate between intrusion detection systems, vulnerability management systems, behavioral analytics, and security auditing systems
  

  discuss concerns and possible future trends as they related to security threats
  

  provide an overview of intrusion detection system (ids)
  

  differentiate between intrusion detection systems including network intrusion, network node, host intrusion, protocol based, and application protocol-based intrusion detection systems
  

  describe intrusion detection system methods including signature-based intrusion, anomaly-based, and hybrid intrusion detection
  

  list benefits of ai-based intrusion detection