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AI+ Security Compliance™

AT-230

Empowering Compliance Through AI

The AI+ Security Compliance™ is an advanced course that merges the fundamental principles of cybersecurity compliance with the transformative power of artificial intelligence (AI). Building on the CISSP framework, this course focuses on how AI can enhance compliance processes, improve risk management, and ensure robust security measures in alignment with regulatory standards. This course introduces you to the core principles of cyber security compliances, while exploring the potential of AI to enhance your security posture. This course structure integrates comprehensive cybersecurity compliance principles with advanced AI applications, providing learners with the necessary skills to ensure compliance and enhance security through AI technologies.

Certification Duration: 40 hours (5 Days)

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Prerequisites

  • Basic understanding of cybersecurity principles.
  • Knowledge of networking fundamentals.
  • Familiarity with programming concepts and languages (Python recommended)
  • An introductory course on AI or machine learning is beneficial but not required.

Modules

10

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Recertification Requirements

AI CERTs requires recertification every year to keep your certification valid. Notifications will be sent three months before the due date, and candidates must follow the steps in the candidate handbook to complete the process.

Need Help? If you have any questions or need assistance with recertification, please reach out to our support team at support@aicerts.ai

Certification Modules

  1. 1.1 Overview of Cybersecurity Compliance
  2. 1.2 International Compliance Standards
  3. 1.3 Developing Compliance Programs
  4. 1.4 Implementing Compliance Programs
  5. 1.5 AI in Cybersecurity Compliance
  6. 1.6 Case Studies and Applications
  1. 2.1 Risk Management Frameworks
  2. 2.2 Conducting Risk Assessments
  3. 2.3 AI in Risk Assessment
  4. 2.4 Compliance and AI
  5. 2.5 Incident Response and AI
  1. 3.1 Data Classification and Protection
  2. 3.2 AI in Privacy Protection
  3. 3.3 Asset Management with AI
  4. 3.4 Case Studies and Best Practices
  1. 4.1 Secure Design Principles
  2. 4.2 AI in Cryptography
  3. 4.3 AI in Vulnerability Assessment
  4. 4.4 Security Models and AI
  1. 5.1 Network Security Fundamentals
  2. 5.2 AI in Network Monitoring
  3. 5.3 AI-driven Network Defense
  4. 5.4 Compliance in Network Security
  1. 6.1 IAM Fundamentals
  2. 6.2 AI in Identity Verification
  3. 6.3 Access Control and AI
  4. 6.4 Threats to IAM and AI Solutions
  1. 7.1 Security Testing Techniques
  2. 7.2 AI in Security Testing
  3. 7.3 Continuous Monitoring and AI
  4. 7.4 Incident Response Planning
  5. 7.5 Managing Cybersecurity Incidents
  6. 7.6 Legal and Regulatory Considerations
  1. 8.1 Security Operations Center (SOC)
  2. 8.2 Data Classification and Protection
  3. 8.3 Privacy Compliance
  4. 8.4 Disaster Recovery and AI
  5. 8.5 AI in Security Orchestration
  1. 9.1 Secure Software Development Life Cycle (SDLC)
  2. 9.2 AI in Application Security Testing
  3. 9.3 AI in Secure DevOps
  4. 9.4 Threat Modeling and AI
  5. 9.5 Internal and External Audits
  6. 9.6 Continuous Monitoring
  1. 10.1 Emerging AI Technologies
  2. 10.2 AI in Cyber Threat Intelligence
  3. 10.3 Quantum Computing and AI
  4. 10.4 Ethical Considerations and AI Governance
  5. 10.5 Practical Applications

Certification Modules

  1. 1.1 Overview of Cybersecurity Compliance
  2. 1.2 International Compliance Standards
  3. 1.3 Developing Compliance Programs
  4. 1.4 Implementing Compliance Programs
  5. 1.5 AI in Cybersecurity Compliance
  6. 1.6 Case Studies and Applications
  1. 2.1 Risk Management Frameworks
  2. 2.2 Conducting Risk Assessments
  3. 2.3 AI in Risk Assessment
  4. 2.4 Compliance and AI
  5. 2.5 Incident Response and AI
  1. 3.1 Data Classification and Protection
  2. 3.2 AI in Privacy Protection
  3. 3.3 Asset Management with AI
  4. 3.4 Case Studies and Best Practices
  1. 4.1 Secure Design Principles
  2. 4.2 AI in Cryptography
  3. 4.3 AI in Vulnerability Assessment
  4. 4.4 Security Models and AI
  1. 5.1 Network Security Fundamentals
  2. 5.2 AI in Network Monitoring
  3. 5.3 AI-driven Network Defense
  4. 5.4 Compliance in Network Security
  1. 6.1 IAM Fundamentals
  2. 6.2 AI in Identity Verification
  3. 6.3 Access Control and AI
  4. 6.4 Threats to IAM and AI Solutions
  1. 7.1 Security Testing Techniques
  2. 7.2 AI in Security Testing
  3. 7.3 Continuous Monitoring and AI
  4. 7.4 Incident Response Planning
  5. 7.5 Managing Cybersecurity Incidents
  6. 7.6 Legal and Regulatory Considerations
  1. 8.1 Security Operations Center (SOC)
  2. 8.2 Data Classification and Protection
  3. 8.3 Privacy Compliance
  4. 8.4 Disaster Recovery and AI
  5. 8.5 AI in Security Orchestration
  1. 9.1 Secure Software Development Life Cycle (SDLC)
  2. 9.2 AI in Application Security Testing
  3. 9.3 AI in Secure DevOps
  4. 9.4 Threat Modeling and AI
  5. 9.5 Internal and External Audits
  6. 9.6 Continuous Monitoring
  1. 10.1 Emerging AI Technologies
  2. 10.2 AI in Cyber Threat Intelligence
  3. 10.3 Quantum Computing and AI
  4. 10.4 Ethical Considerations and AI Governance
  5. 10.5 Practical Applications

Tools

Secureframe

LeewayHertz

Securiti

Scytale

Exam Objectives

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AI-Enhanced Compliance Management

Students will be able to integrate AI tools and techniques to streamline and automate compliance processes, ensuring adherence to international cybersecurity standards and regulations.

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Risk Management with AI

Students will develop the ability to use AI for conducting comprehensive risk assessments, identifying potential vulnerabilities, and implementing proactive risk mitigation strategies.

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AI-Driven Security Solutions

Students will gain hands-on experience with AI applications in security, learning how to implement AI-powered tools for incident response, threat detection, and asset security.

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Understanding of Future AI Trends in Cybersecurity

Students will be equipped with knowledge of emerging AI technologies, such as quantum computing, and their implications for cybersecurity, allowing them to stay ahead of evolving threats and innovations.

Career Opportunities Post-Certification

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Median Salaries

$1,30,000
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With AI Skills

$1,60,000
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% Difference

23
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AI Compliance Specialist

Focuses on implementing AI-driven tools to ensure organizational compliance with cybersecurity regulations and standards.

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Cybersecurity Risk Analyst (AI)

Uses AI-based solutions to assess and mitigate cybersecurity risks, ensuring proactive threat detection and management.

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AI Security Operations Manager

Manages security operations using AI technologies, overseeing incident response, network security, and continuous monitoring for potential threats.

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AI Cybersecurity Engineer

Designs and implements AI-powered security solutions, including automated threat detection, response systems, and data protection mechanisms.

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Frequently Asked Questions

This course focuses on how to ensure that AI systems comply with security standards and regulations across industries.

Ideal for security professionals, compliance officers, and AI developers working on security-critical AI projects.

The course covers compliance frameworks, regulatory requirements for AI systems, and secure AI deployment strategies.

You will learn about GDPR, HIPAA, and NIST compliance for AI systems, among others.

This certification demonstrates that you can ensure AI systems comply with industry and regulatory security standards, enhancing your career in both AI and cybersecurity sectors.

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