Cyber security and Artificial Intelligence Risk Course

Course Overview

• Explore the critical intersection where advanced Artificial Intelligence and Machine Learning technologies introduce novel cybersecurity vulnerabilities and unique risk profiles.
• Gain a foundational understanding of the dynamic threat landscape specifically targeting AI systems, from data poisoning to model inversion attacks, and how to proactively address them.
• Delve into the strategic imperatives for integrating security considerations throughout the entire lifecycle of AI and ML projects, fostering a secure-by-design approach.
• Identify the ethical dilemmas and compliance challenges inherent in deploying AI, emphasizing responsible development practices that align with evolving regulatory expectations.
• Understand the implications of data security and privacy within AI contexts, focusing on protecting sensitive information used in model training and inference.
• Discover methodologies for conducting comprehensive risk assessments tailored to AI-driven solutions, allowing organizations to prioritize mitigation efforts effectively.
• Learn about the organizational structures and policies required to establish robust AI risk management, ensuring accountability and continuous improvement.
• Unpack the various categories of AI-specific risks, including those related to bias, fairness, transparency, and the potential for unintended consequences in real-world deployments.
• Prepare for future regulatory trends and industry standards concerning AI security and governance, positioning your organization for long-term compliance and trust.
• This course is designed to equip professionals with a holistic perspective on securing AI, moving beyond traditional cybersecurity to address AI-native threats and governance gaps.

Requirements / Prerequisites

• A foundational grasp of core cybersecurity principles, including common attack vectors, defensive strategies, and network security concepts, will be beneficial.
• Familiarity with basic Artificial Intelligence and Machine Learning terminology, such as datasets, models, training, and inference, is recommended, though deep technical expertise is not required.
• An eagerness to understand complex risk management challenges at the confluence of rapidly evolving technologies and their potential societal impact.
• Access to a standard internet-connected computer with a modern web browser to comfortably engage with course materials and online resources.
• No specific software installations or development environments are needed, as the course focuses on conceptual understanding and strategic frameworks.
• A curious mindset and a commitment to learning about emerging technological risks and their proactive mitigation strategies in a fast-paced environment.

Skills Covered / Tools Used

• AI Risk Identification: Develop the capability to pinpoint potential vulnerabilities and attack surfaces unique to machine learning models and AI systems.
• Threat Modeling for AI: Master techniques for systematically identifying, classifying, and prioritizing threats against AI components, from data pipelines to model deployment.
• Data Security in AI Pipelines: Acquire skills in applying data protection strategies, including anonymization, differential privacy, and secure data handling, across the AI lifecycle.
• Adversarial Robustness Evaluation: Learn methods to assess and improve the resilience of AI models against adversarial attacks designed to manipulate their behavior.
• Compliance Framework Implementation: Understand how to interpret and apply relevant regulatory frameworks and industry standards (e.g., NIST AI RMF, ISO 42001, GDPR) to AI governance.
• Ethical AI Risk Mitigation: Develop strategies to identify and mitigate risks related to algorithmic bias, fairness, transparency, and privacy in AI applications.
• Secure MLOps Practices: Gain insights into integrating security checks and best practices into Machine Learning Operations (MLOps) workflows for continuous protection.
• Incident Response Planning for AI: Formulate effective response strategies for security incidents involving compromised AI systems or data breaches.
• Policy Development for AI Governance: Learn to craft internal policies and guidelines that promote responsible AI development and deployment within an organization.
• Stakeholder Communication: Enhance your ability to communicate complex AI cybersecurity risks and mitigation strategies to technical and non-technical stakeholders effectively.
• Risk Quantification & Prioritization: Apply structured approaches to quantify AI-related risks and prioritize resources for their most effective management.
• Audit & Assurance for AI: Understand the principles of auditing AI systems for compliance, security, and ethical adherence, preparing for future regulatory scrutiny.

Benefits / Outcomes

• Enhanced Organizational Resilience: Significantly improve your organization’s ability to identify, assess, and mitigate complex cybersecurity risks introduced by AI and ML technologies.
• Strategic Career Advancement: Position yourself as a specialist in a high-demand, interdisciplinary field, opening doors to advanced roles in AI security, governance, and risk management.
• Informed Decision-Making: Develop the expertise to guide strategic decisions concerning AI adoption, ensuring security and ethical considerations are embedded from inception.
• Proactive Compliance Posture: Equip yourself to navigate the evolving regulatory landscape surrounding AI, ensuring your projects and organization remain compliant and avoid penalties.
• Contribution to Responsible AI: Play a pivotal role in fostering the ethical and secure development and deployment of AI, building trust and safeguarding societal impact.
• Practical Skill Application: Acquire immediately actionable skills that can be applied to real-world AI projects, enhancing their security posture and integrity.
• Reduced Business Exposure: Minimize financial, reputational, and operational risks associated with insecure or non-compliant AI systems.
• Competitive Advantage: Enable your organization to leverage AI innovation securely and responsibly, gaining a significant edge in the marketplace.
• Comprehensive Understanding: Gain a holistic view of the entire AI risk spectrum, from technical vulnerabilities to governance gaps, enabling more robust solutions.
• Confidence in AI Leadership: Develop the confidence to lead discussions and initiatives related to AI security and governance within your team or organization.

PROS

• Timely Content: Features an updated curriculum from November 2025, ensuring relevance to the latest advancements and threats in AI and cybersecurity.
• High Student Satisfaction: Boasts an impressive 4.75/5 rating from over 2,000 students, indicating high quality and value.
• Concise and Efficient: At only 37 minutes, the course offers a highly concentrated learning experience for busy professionals.
• Critical Focus Area: Directly addresses the burgeoning and crucial field of AI governance and risk, a gap many professionals seek to fill.
• Practical Application: Provides actionable insights for establishing governance and implementing controls, which can be applied almost immediately.

CONS

• The brevity of the course might only introduce concepts without delving into deep technical implementation details or extensive case studies.