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Responsible Ai For Cisos And Cybersecurity Professionals
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 28m | Size: 905 MB [/center] [/center]
Learn how to enforce Responsible AI Principles as a Cybersecurity professional
What you'll learn
Why Responsible AI has become a security and governance problem, not just an ethics discussion
How AI systems differ from traditional software and why this breaks existing security assumptions
The four core pillars of Responsible AI: Fairness, Accountability, Transparency, and Safety-translated into enforceable controls
How to conduct Responsible AI risk assessments for decision-making systems
How to design an AI governance operating model that actually works in real organizations
Role Playing Sessions to test your skills
Requirements
Basic knowledge of IT
No prior knowledge of AI needed
Description
Artificial intelligence is no longer an experimental technology. It is now embedded into security tools, enterprise platforms, HR systems, financial decisioning, customer interactions, and automated workflows. As AI systems increasingly make and influence real-world decisions, failures are no longer technical glitches-they are governance, security, and accountability failures.The "Responsible AI and Governance for Cybersecurity Professionals and CISOs" course is a practical, security-first masterclass designed to help security leaders, GRC professionals, and architects understand how to govern AI systems safely, defensibly, and at scale.This course moves beyond ethical discussions and abstract principles. Instead, it focuses on how Responsible AI becomes a core security discipline, addressing new failure modes such as bias, hallucinations, automation risk, loss of accountability, and AI-driven incidents that fall outside traditional security models.Through real-world case studies, operating models, risk assessments, and governance templates, you will learn how to design and enforce Responsible AI controls across the full AI lifecycle-covering both in-house and third-party AI systems.What You Will LearnWhy Responsible AI has become a security and governance problem, not just an ethics discussionHow AI systems differ from traditional software and why this breaks existing security assumptionsThe four core pillars of Responsible AI: Fairness, Accountability, Transparency, and Safety-translated into enforceable controlsHow to conduct Responsible AI risk assessments for decision-making systemsHow to design an AI governance operating model that actually works in real organizationsHow to apply human-in-the-loop, approval gates, and risk-based automation limitsCourse OutlineUnderstanding AI as a Security ProblemHow AI systems work (without the hype)Why AI outputs are probabilistic and unpredictableNew AI failure modes security teams must understandThe AI Lifecycle and RiskData sourcing and preparation risksModel selection, testing, and deployment challengesMonitoring, drift, and AI system retirementWhy Responsible AI Is Now MandatoryWhy AI failures are business-ending eventsWhy traditional governance models fail for AIThe changing role of the CISO and security leaderResponsible AI Frameworks and StandardsCore Concepts of Responsible AI (Without the Hype)Fairness: avoiding unjust outcomes at scaleAccountability: clear ownership for AI decisionsTransparency: explainability, traceability, and evidenceSafety: preventing harm and limiting blast radiusDesigning an AI Governance Operating ModelAI governance committees and decision authorityRisk assessments as go / no-go gatesAcceptable use policies and human-in-the-loop requirementsEmbedding Responsible AI into existing security cultureResponsible AI in Third-Party and Vendor SystemsCase Studies and Practical ApplicationWho Should Take This CourseThis course is designed for professionals responsible for securing, governing, or approving AI systems, including:CISOs and security leadersCybersecurity and GRC professionalsCloud and enterprise security architectsRisk, compliance, and audit professionalsAI governance and responsible AI specialistsTechnology leaders involved in AI adoptionPre-requisitesA basic understanding of cybersecurity concepts is recommendedNo prior AI engineering experience is requiredInstructorTaimur Ijlal is a multi-award-winning cybersecurity leader with over 20 years of global experience across cyber risk management, AI security, and IT governance. He has been recognized as CISO of the Year and named among the Top 30 CISOs worldwide.Taimur's work has been featured in ISACA Journal, CIO Magazine Middle East, and leading AI security publications. He has trained thousands of professionals globally through his Udemy courses, and his books on AI Security and Cloud Computing have ranked as #1 New Releases on Amazon.
Who this course is for
CISOs and security leaders
Cybersecurity and GRC professionals
Cloud and enterprise security architects
Risk, compliance, and audit professionals
AI governance and responsible AI specialists
Technology leaders involved in AI adoption
Responsible Ai For Cisos And Cybersecurity Professionals
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 28m | Size: 905 MB [/center] [/center]
Learn how to enforce Responsible AI Principles as a Cybersecurity professional
What you'll learn
Why Responsible AI has become a security and governance problem, not just an ethics discussion
How AI systems differ from traditional software and why this breaks existing security assumptions
The four core pillars of Responsible AI: Fairness, Accountability, Transparency, and Safety-translated into enforceable controls
How to conduct Responsible AI risk assessments for decision-making systems
How to design an AI governance operating model that actually works in real organizations
Role Playing Sessions to test your skills
Requirements
Basic knowledge of IT
No prior knowledge of AI needed
Description
Artificial intelligence is no longer an experimental technology. It is now embedded into security tools, enterprise platforms, HR systems, financial decisioning, customer interactions, and automated workflows. As AI systems increasingly make and influence real-world decisions, failures are no longer technical glitches-they are governance, security, and accountability failures.The "Responsible AI and Governance for Cybersecurity Professionals and CISOs" course is a practical, security-first masterclass designed to help security leaders, GRC professionals, and architects understand how to govern AI systems safely, defensibly, and at scale.This course moves beyond ethical discussions and abstract principles. Instead, it focuses on how Responsible AI becomes a core security discipline, addressing new failure modes such as bias, hallucinations, automation risk, loss of accountability, and AI-driven incidents that fall outside traditional security models.Through real-world case studies, operating models, risk assessments, and governance templates, you will learn how to design and enforce Responsible AI controls across the full AI lifecycle-covering both in-house and third-party AI systems.What You Will LearnWhy Responsible AI has become a security and governance problem, not just an ethics discussionHow AI systems differ from traditional software and why this breaks existing security assumptionsThe four core pillars of Responsible AI: Fairness, Accountability, Transparency, and Safety-translated into enforceable controlsHow to conduct Responsible AI risk assessments for decision-making systemsHow to design an AI governance operating model that actually works in real organizationsHow to apply human-in-the-loop, approval gates, and risk-based automation limitsCourse OutlineUnderstanding AI as a Security ProblemHow AI systems work (without the hype)Why AI outputs are probabilistic and unpredictableNew AI failure modes security teams must understandThe AI Lifecycle and RiskData sourcing and preparation risksModel selection, testing, and deployment challengesMonitoring, drift, and AI system retirementWhy Responsible AI Is Now MandatoryWhy AI failures are business-ending eventsWhy traditional governance models fail for AIThe changing role of the CISO and security leaderResponsible AI Frameworks and StandardsCore Concepts of Responsible AI (Without the Hype)Fairness: avoiding unjust outcomes at scaleAccountability: clear ownership for AI decisionsTransparency: explainability, traceability, and evidenceSafety: preventing harm and limiting blast radiusDesigning an AI Governance Operating ModelAI governance committees and decision authorityRisk assessments as go / no-go gatesAcceptable use policies and human-in-the-loop requirementsEmbedding Responsible AI into existing security cultureResponsible AI in Third-Party and Vendor SystemsCase Studies and Practical ApplicationWho Should Take This CourseThis course is designed for professionals responsible for securing, governing, or approving AI systems, including:CISOs and security leadersCybersecurity and GRC professionalsCloud and enterprise security architectsRisk, compliance, and audit professionalsAI governance and responsible AI specialistsTechnology leaders involved in AI adoptionPre-requisitesA basic understanding of cybersecurity concepts is recommendedNo prior AI engineering experience is requiredInstructorTaimur Ijlal is a multi-award-winning cybersecurity leader with over 20 years of global experience across cyber risk management, AI security, and IT governance. He has been recognized as CISO of the Year and named among the Top 30 CISOs worldwide.Taimur's work has been featured in ISACA Journal, CIO Magazine Middle East, and leading AI security publications. He has trained thousands of professionals globally through his Udemy courses, and his books on AI Security and Cloud Computing have ranked as #1 New Releases on Amazon.
Who this course is for
CISOs and security leaders
Cybersecurity and GRC professionals
Cloud and enterprise security architects
Risk, compliance, and audit professionals
AI governance and responsible AI specialists
Technology leaders involved in AI adoption
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