
AI FOR TECH LEADERS
AI Adoption and Operationalisation for Technology leaders
Gain insight into Strategic AI implementation, AI security, team skills development, and ethics.
Effective AI risk management involves a systematic approach to identifying, evaluating, and mitigating AI risks within an organisation. It is essential for protecting sensitive data, ensuring compliance, and maintaining seamless AI operations. It also fosters innovation, provides a competitive advantage, and bolsters supply chains.
What you'll learn
DURATION: 4 HOURS
AI Strategy and Implementation for IT Leaders: Focused on guiding AI adoption within the organisation, including planning, deployment, and aligning with business goals.
AI Security and Ethical AI: Specialised training on AI-specific security risks, ethical concerns, and best practices for safe implementation.
Skill Development and Team Building: Courses that outline the technical skills required for AI security, helping Helen identify and develop the necessary competencies in her team.

Course Overview
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Developing a roadmap for AI adoption within IT infrastructure
Aligning AI deployment with overall business objectives
Integrating AI projects with existing systems and workflows
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Technical requirements for implementing AI solutions
Infrastructure and resource planning for AI applications
Managing the lifecycle of AI projects, from pilot to production
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Understanding unique security challenges in AI systems
Protecting data and privacy in AI applications
Mitigation strategies for AI vulnerabilities and attack vectors
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Ethical considerations in AI system design and deployment
Addressing bias and fairness in AI algorithms
Guidelines for implementing transparent and explainable AI
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Identifying required skills for AI support and maintenance
Training on essential AI tools, frameworks, and technologies
Establishing team roles and responsibilities for AI operations
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Fostering collaboration between IT, security, and AI teams
Ensuring secure practices across departments for AI use
Building a culture of awareness and continuous improvement in AI security
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Methods for measuring AI effectiveness and value
Scaling successful AI projects across the organisation
Continuous monitoring and adaptation of AI systems
