SECAICOCompTIACO-SECAI1.0<ul> <li>Apply foundational and advanced AI concepts to strengthen organisational cybersecurity.</li><li>Implement robust security controls and best practices for protecting AI systems and data.</li><li>Leverage AI-driven tools to enhance threat detection, response, and automation of security operations.</li><li>Navigate global governance, risk, and compliance frameworks to ensure responsible AI adoption.</li></ul><p>This is equivalent to 3&ndash;4 years of IT experience with approximately 2 years of hands-on cybersecurity experience.</p><h4>Module 1 &mdash; AI and Data Concepts for Cybersecurity</h4><ul> <li>AI concepts and core AI types</li><li>Generative AI and transformers</li><li>Machine learning and deep learning</li><li>Natural language processing</li><li>AI model training approaches</li><li>Prompt engineering fundamentals</li><li>Model security considerations</li><li>AI data types and data security techniques</li><li>RAG (Retrieval Augmented Generation) concepts</li><li>Data integrity and processing controls</li></ul><h4>Module 2 &mdash; Threat Modeling and Securing AI Systems</h4><ul> <li>AI threat modeling fundamentals</li><li>Threat modeling processes and prerequisites</li><li>AI threat modeling frameworks</li><li>AI security control types</li><li>Model guardrails and prompt templates</li><li>Gateway and interface controls</li><li>Usage quotas and limitation controls</li><li>Security control testing</li></ul><h4>Module 3 &mdash; Access Controls for AI</h4><ul> <li>AI access control principles and models</li><li>Model and agent access controls</li><li>API and network access security</li><li>AI data security controls</li><li>Encryption and data safety measures</li><li>Monitoring and logging AI systems</li><li>Performance and cost monitoring</li><li>AI auditing and compliance monitoring</li></ul><h4>Module 4 &mdash; AI Threats and Compensating Controls</h4><ul> <li>AI lifecycle security</li><li>Ethical AI design considerations</li><li>AI attack types and techniques</li><li>Backdoor and trojan model attacks</li><li>Model poisoning and inversion</li><li>Model theft risks</li><li>Compensating control strategies</li><li>Post-incident AI analysis</li></ul><h4>Module 5 &mdash; Leveraging AI in Security Operations</h4><ul> <li>AI-enabled security tools</li><li>AI use cases in detection and analysis</li><li>AI for vulnerability assessment</li><li>AI-enhanced attack vectors</li><li>AI for social engineering and deception</li><li>AI reconnaissance techniques</li><li>AI-driven automation</li><li>AI in DevSecOps workflows</li><li>AI scripting and summarisation</li></ul><h4>Module 6 &mdash; AI Governance, Risk, and Compliance</h4><ul> <li>AI governance structures</li><li>AI organisational roles</li><li>Responsible AI principles</li><li>AI risk identification and assessment</li><li>AI regulatory themes</li><li>Compliance frameworks for AI</li><li>Organisational AI policy design</li><li>Compliance reporting</li></ul>- Apply foundational and advanced AI concepts to strengthen organisational cybersecurity. - Implement robust security controls and best practices for protecting AI systems and data. - Leverage AI-driven tools to enhance threat detection, response, and automation of security operations. - Navigate global governance, risk, and compliance frameworks to ensure responsible AI adoption.This is equivalent to 3–4 years of IT experience with approximately 2 years of hands-on cybersecurity experience.Module 1 — AI and Data Concepts for Cybersecurity - AI concepts and core AI types - Generative AI and transformers - Machine learning and deep learning - Natural language processing - AI model training approaches - Prompt engineering fundamentals - Model security considerations - AI data types and data security techniques - RAG (Retrieval Augmented Generation) concepts - Data integrity and processing controls Module 2 — Threat Modeling and Securing AI Systems - AI threat modeling fundamentals - Threat modeling processes and prerequisites - AI threat modeling frameworks - AI security control types - Model guardrails and prompt templates - Gateway and interface controls - Usage quotas and limitation controls - Security control testing Module 3 — Access Controls for AI - AI access control principles and models - Model and agent access controls - API and network access security - AI data security controls - Encryption and data safety measures - Monitoring and logging AI systems - Performance and cost monitoring - AI auditing and compliance monitoring Module 4 — AI Threats and Compensating Controls - AI lifecycle security - Ethical AI design considerations - AI attack types and techniques - Backdoor and trojan model attacks - Model poisoning and inversion - Model theft risks - Compensating control strategies - Post-incident AI analysis Module 5 — Leveraging AI in Security Operations - AI-enabled security tools - AI use cases in detection and analysis - AI for vulnerability assessment - AI-enhanced attack vectors - AI for social engineering and deception - AI reconnaissance techniques - AI-driven automation - AI in DevSecOps workflows - AI scripting and summarisation Module 6 — AI Governance, Risk, and Compliance - AI governance structures - AI organisational roles - Responsible AI principles - AI risk identification and assessment - AI regulatory themes - Compliance frameworks for AI - Organisational AI policy design - Compliance reporting3 days3500.002089.002089.002089.002089.002089.003500.00