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Risk Management for AI Chatbots

O'Reilly on Data

Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. ” ) With a chatbot, the web form passes an end-user’s freeform text input—a “prompt,” or a request to act—to a generative AI model.

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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

As gen AI heads to Gartners trough of disillusionment , CIOs should consider how to realign their 2025 strategies and roadmaps. Even simple use cases had exceptions requiring business process outsourcing (BPO) or internal data processing teams to manage.

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New regulation intensifies focus on IT risk management and operational resilience

CIO Business Intelligence

A comprehensive regulatory reach DORA addresses a broad range of ICT risks, including incident response, resilience testing, third-party risk management, and information sharing. One notable tool, BMC HelixGPT , uses a large language model (LLM) that drives a suite of AI-powered software agents.

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Successful Change Management with Enterprise Risk Management

Speaker: William Hord, Vice President of ERM Services

Leveraging the data that your ERM program already contains is an effective way to help create and manage the overall change management process within your organization. It is the tangents of this data that are vital to a successful change management process. Organize ERM strategy, operations, and data.

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AI brings complexity to cybersecurity and fraud

CIO Business Intelligence

The 2024 Security Priorities study shows that for 72% of IT and security decision makers, their roles have expanded to accommodate new challenges, with Risk management, Securing AI-enabled technology and emerging technologies being added to their plate. Ensuring diversity in data sources helps models make impartial decisions.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

A look at how guidelines from regulated industries can help shape your ML strategy. In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring.