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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. Before you give up on your dreams of releasing an AI chatbot, remember: no risk, no reward. That doesn’t sound so bad, right? So, what do you do?

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PODCAST: Making AI Real – Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower

bridgei2i

Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. Today the Chief Risk Officers(CROs) struggle with the critical task of monitoring and assessing key risks in real time and firefight to mitigate any critical issues that arise.

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

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns. CIOs should consider placing these five AI bets in 2025.

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Sweat the small stuff: Data protection in the age of AI

CIO Business Intelligence

As concerns about AI security, risk, and compliance continue to escalate, practical solutions remain elusive. as AI adoption and risk increases, its time to understand why sweating the small and not-so-small stuff matters and where we go from here. AI usage may bring the risk of sensitive data exfiltration through AI interactions.

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What Business Analysts Can Learn From Swiss Cheese

BA Learnings

Latent conditions can be highlighted and corrected via effective risk management before problems manifest in the system. Applying the Swiss cheese model is a form of proactive risk management that should be practised to prevent disaster. How does this affect business analysts?

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AI as a growing child: How we can shape its future responsibly

CIO Business Intelligence

Systems of influence At the most immediate level is the microsystem the developers, engineers, and users directly interacting with AI. Bronfenbrenners theory reveals the interconnected layers of influence that guide its growth and underscores the urgent need for responsible governance of AI.

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