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Coles AI Risk Management: How to Scale AI Securely at Bayshore Shopping Centre in Ottawa, ON
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Every organization is keen to implement AI into their workflows, process and operations. However, most lack an effective framework for managing the security risks of AI. This book is an actionable guide to building effective controls and managing the risk of AI.
Written by security expert Rafah Knight, AI Security shows governance, risk and compliance professionals how to build an AI security operating model. It explains how AI security can become operational, measurable and embedded into existing business process. It helps answer the key questions surrounding adopting, scaling and using AI securely: Who owns AI risk? What governance structures need to be implemented? How should third-party AI vendor exposure be managed? How should AI policies and controls be designed?
The book features real-world examples from industries including financial services, retail, the public sector and healthcare. It explores the emergence of AI governance as a specific discipline and provides practical guidance on managing AI risk in real-world organizations. It shows readers how to govern AI usage, manage AI operation risks, measure AI security risks, handle third-party AI vendor exposure, implement accountability structures and operationalize AI assurance at scale.
Every organization is keen to implement AI into their workflows, process and operations. However, most lack an effective framework for managing the security risks of AI. This book is an actionable guide to building effective controls and managing the risk of AI.
Written by security expert Rafah Knight, AI Security shows governance, risk and compliance professionals how to build an AI security operating model. It explains how AI security can become operational, measurable and embedded into existing business process. It helps answer the key questions surrounding adopting, scaling and using AI securely: Who owns AI risk? What governance structures need to be implemented? How should third-party AI vendor exposure be managed? How should AI policies and controls be designed?
The book features real-world examples from industries including financial services, retail, the public sector and healthcare. It explores the emergence of AI governance as a specific discipline and provides practical guidance on managing AI risk in real-world organizations. It shows readers how to govern AI usage, manage AI operation risks, measure AI security risks, handle third-party AI vendor exposure, implement accountability structures and operationalize AI assurance at scale.
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