Remove IT Remove Risk Management Remove Testing
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Risk Management for AI Chatbots

O'Reilly on Data

Welcome to your company’s new AI risk management nightmare. Before you give up on your dreams of releasing an AI chatbot, remember: no risk, no reward. The core idea of risk management is that you don’t win by saying “no” to everything. So, what do you do? That code was too trusting, though.

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New regulation intensifies focus on IT risk management and operational resilience

CIO Business Intelligence

As IT landscapes and software delivery processes evolve, the risk of inadvertently creating new vulnerabilities increases. A comprehensive regulatory reach DORA addresses a broad range of ICT risks, including incident response, resilience testing, third-party risk management, and information sharing.

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How resilient CIOs future-proof to mitigate risks

CIO Business Intelligence

Over the past year, the focus on risk management has evolved significantly, says Meerah Rajavel, CIO of Palo Alto Networks. With the increasing sophistication of cyber threats and the accelerated pace of digital transformation, organizations must be more proactive in identifying and mitigating risks.

Risk 105
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5 top business use cases for AI agents

CIO Business Intelligence

Then in November, the company revealed its Azure AI Agent Service, a fully-managed service that lets enterprises build, deploy and scale agents quickly. Before that, though, ServiceNow announced its AI Agents offering in September, with the first use cases for customer service management and IT service management, available in November.

Software 143
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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.

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Why you should care about debugging machine learning models

O'Reilly on Data

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. Not least is the broadening realization that ML models can fail.

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

O'Reilly on Data

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. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.