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    EW-AiRM: Managing AI Black Swan Risks in Enterprise Risk Management

    10 min
    |
    |
    11. Juni 2026
    AIBusinessTechnology

    Learn why traditional ERM frameworks like ISO 31000 and COSO fail to address AI Black Swan risks and how EW-AiRM provides a resilient blueprint for AI governance.

    EW-AiRM: Managing AI Black Swan Risks in Enterprise Risk Management

    Bestes Zitat aus EW-AiRM: Managing AI Black Swan Risks in Enterprise Risk Management

    “

    AI risks are not siloed; they are systemic. This is about shifting your mindset from managing a tool to governing a self-learning system that possesses the speed and scale to cause devastating consequences if left on autopilot.

    ”

    Diese Audiolektion wurde von einem BeFreed-Community-Mitglied erstellt

    Eingabefrage

    Explain Enterprise-wide AI Risk Management (EW-AiRM) by Prof. Markus Krebsz, focusing on his frameworks and methodology for managing AI risks across organizations.

    Moderatorstimmen
    Lenaplay
    Lernstil
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    Wissensquellen
    Enterprise-wide AI Risk Management (EW-AiRM): A Practical Framework for Managing AI Risks in any Organisation: Amazon.co.uk: Krebsz, Markus: 9781394446476: Books
    link
    https://www.amazon.co.uk/Enterprise-wide-Risk-Management-EW-AiRM-Organisation/dp/1394446470
    Resilience in the Age of AI: How EW-AiRM© future-proves ...
    link
    https://www.grcworldforums.com/governance-risk-and-compliance/resilience-in-the-age-of-ai-how-ew-airm-future-proves-enterprise-risk-management/10723.article
    HAiPECR - OECD AI Observatory
    link
    https://oecd.ai/en/catalogue/tools/haipecr
    AI Risk Governance & Enterprise Risk Management
    link
    https://www.theirm.org/training/public-courses/ai-risk-governance-enterprise-risk-management/
    What Pope Leo XIV's New Encyclical Means for AI Risk Practitioners
    link
    https://www.linkedin.com/pulse/babel-jerusalem-what-pope-leo-xivs-8tqye

    Häufig gestellte Fragen

    EW-AiRM, or Enterprise-wide AI Risk Management, is a specialized framework designed to address the unique challenges posed by artificial intelligence that traditional systems like ISO 31000 or COSO cannot handle. Unlike linear risk models, EW-AiRM accounts for the fact that AI can drift and shift from high-performing to non-compliant overnight. It serves as an essential blueprint for Chief Risk Officers and board members to ensure the long-term survival and going concern of their firms in an unpredictable technological landscape.

    Traditional enterprise risks are often slow-moving and well-understood, allowing for point-in-time assessments and predictable failure models. In contrast, AI Black Swan events have no historical precedent and can trigger sudden, massive disruptions that existing risk management frameworks were never designed to manage. Because AI learns and evolves constantly, it creates a state of uncertainty galore, making it a foundational pillar of organizational risk rather than just a minor IT sub-topic.

    Standard frameworks like ISO 31000 and COSO were built for linear, predictable risks and assume that failures can be predicted before they happen. These systems are often point-in-time assessments that struggle with the dynamic nature of AI, which can change its behavior and compliance status literally overnight. As 95% of firms adopt AI, the gap between these legacy systems and the need for a robust, resilient approach makes traditional ERM a potential liability for the modern executive.

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    Kernaussagen

    1

    The Hidden Fragility of Your Enterprise AI Strategy

    0:00
    2

    Why Your Current Risk Taxonomy Is Failing the AI Test

    1:23
    3

    The Three Layers of Comprehensive AI Governance

    2:46
    4

    Navigating the MIT Risk Taxonomy and its Eight Hundred Controls

    4:10
    5

    Anchoring Technology in Human Accountability with HAiPECR

    5:29
    6

    Preparing for the AI Black Swan and Systemic Collapse

    6:51
    7

    A Practical Playbook for Implementing EW-AiRM Today

    8:12
    8

    Leading with Confidence in an Uncertain AI Future

    9:34

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