Ai Security Governance for Businesses
About This Course
This course equips business leaders, managers, governance professionals, and technology teams with the practical knowledge to establish an AI security governance framework for the responsible adoption of Generative AI and agentic AI. Participants will examine how these technologies introduce business risks relating to confidential data, inaccurate outputs, malicious instructions, excessive permissions, third-party integrations, automated actions, intellectual property, privacy, and regulatory compliance.
Learners will develop a structured governance framework covering AI policies, roles and responsibilities, risk ownership, acceptable-use requirements, AI system inventories, risk classification, approval processes, and human oversight. The course explains how governance controls can be applied across the AI lifecycle, from evaluating use cases and selecting vendors to deployment, monitoring, incident response, and system retirement.
Participants will also explore security controls for agentic AI, including identity and access management, least-privilege permissions, tool and data access restrictions, action approval, activity logging, output validation, and emergency suspension procedures. They will learn to assess AI solutions according to their business impact, data sensitivity, autonomy, and potential consequences.
Through practical business scenarios, learners will conduct AI risk assessments, define governance controls, develop organisational guidelines, and create an implementation roadmap. Emphasis is placed on aligning AI adoption with business objectives while maintaining accountability, transparency, fairness, security, and compliance.
By the end of the course, participants will be able to design and implement a business-focused AI security governance framework that enables responsible innovation while reducing the risks associated with Generative AI and autonomous AI agents.
What You'll Learn
LO1: Analyse the societal impacts and ethical risks of biased AI algorithms across different cultural and minority contexts.
LO2: Evaluate ethical trade-offs in generative AI systems, balancing environmental, privacy, and framework considerations.
LO3: Develop ethical strategies and apply AI governance principles to promote fairness, transparency, and sustainability.
LO4: Implement responsible AI practices and champion transparency to reduce societal bias and ethical risks in deployment.
Course Outline:
Learning Outcomes:
LO1: Analyse the societal impacts and ethical risks of biased AI algorithms across different cultural and minority contexts.
LO2: Evaluate ethical trade-offs in generative AI systems, balancing environmental, privacy, and framework considerations.
LO3: Develop ethical strategies and apply AI governance principles to promote fairness, transparency, and sustainability.
LO4: Implement responsible AI practices and champion transparency to reduce societal bias and ethical risks in deployment.
Course Outline:
Topic 1: Generative AI and AI Agent Security, Ethical and Business Risk Assessment
Topic 2: AI Governance Frameworks for Generative AI and AI Agents
Topic 3: Developing Security Policies and Governance Controls for AI Agents
Topic 4: Implementing, Monitoring and Improving Responsible AI Agent Governanc
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Age Group: 21 to 65 years old