AI Risk Management: Defining, Measuring, & Mitigating the Risks of AI

AI is merging into the modern workplace at roughly the pace computers did in the 1980s, and the risks are evolving just as fast. IBM and Ponemon found that 97% of organizations hit by an AI-related security incident lacked basic access controls, and 63% had no AI governance policy at all.
In this video, Yakir breaks down the seven categories of AI risk every GRC leader needs to understand, and what separates knowing you have a control gap from knowing what it will cost you.

We cover:

• The seven categories of AI risk (cybersecurity, operational, bias and ethical, privacy, regulatory and compliance, reputational, and societal) and why real incidents almost always span several at once
• How MITRE ATLAS maps the tactics adversaries actually use against AI systems, from supply chain compromise to training data poisoning
• The difference between an AI risk assessment and AI risk quantification, and why assessment alone leaves the most important question unanswered
• How Monte Carlo modeling and loss exceedance curves turn AI exposure into figures that support investment prioritization, board reporting, and insurance negotiations

Schedule a demo: https://www.kovrr.com/ai-risk-assessment-demo

Learn more about the AI Security and Governance Platform: https://www.kovrr.com/ai-governance

Read the full blog on AI risk management: https://www.kovrr.com/blog-post/ai-risk-management-defining-measuring-mitigating-the-risks-of-ai

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