AI for risk leaders

AI Governance, Risk Management and Compliance for Risk leaders

Gain insight into AI risk assessment, compliance, regulatory frameworks, and AI risk management best practices.

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

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

    • Overview of AI risks and how they differ from traditional technology risks

    • Key compliance issues specific to AI, including data privacy and security

    • Understanding the potential impact of AI risks on the organisation

    • Methods for identifying and assessing AI-related risks in business processes

    • Techniques for managing operational, financial, and reputational risks associated with AI

    • Developing risk mitigation plans for AI implementations

    • Overview of current AI regulatory requirements and compliance standards

    • Best practices for aligning AI practices with legal and industry standards

    • Preparing for evolving regulations in AI technology and governance

    • Australian Federal Government 10 Guardrails

    • Establishing AI governance structures and policies within the organisation

    • Creating guidelines for responsible and ethical AI use

    • Roles and responsibilities in AI risk governance

    • Practical approaches for adding AI-specific risks to traditional risk registers

    • Adapting existing risk frameworks to include AI-related considerations

    • Case studies on updating risk frameworks to account for AI

    • Preparing for future regulatory changes affecting AI practices

    • Anticipating compliance challenges with AI advancements

    • Strategies for maintaining flexibility and adaptability in risk management

    • Setting up monitoring processes for AI-related risks and compliance

    • Regularly reviewing and updating AI risk management approaches

    • Using metrics and KPIs to track AI risk management performance

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