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Generative AI: Eight fundamental security risks and considerations

CIO

As new technologies emerge, security measures often trail behind, requiring time to catch up. This is particularly true for Generative AI, which presents several inherent security challenges. Here are some of the key risks related to AI that organizations need to bear in mind. However, this shift introduces challenges.

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CIOs face mounting pressure as AI costs and complexities threaten enterprise value

CIO

CIOs are under increasing pressure to deliver meaningful returns from generative AI initiatives, yet spiraling costs and complex governance challenges are undermining their efforts, according to Gartner. However, unlocking the full value of AI remains elusive, with four critical challenges standing in their way.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.

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Gartner: 13 AI insights for enterprise IT

CIO

Artificial intelligence is an early stage technology and the hype around it is palpable, but IT leaders need to take many challenges into consideration before making major commitments for their enterprises. Analysts at this week’s Gartner IT Symposium/Xpo spent tons of time talking about the impact of AI on IT systems and teams.

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Addressing Top Enterprise Challenges in Generative AI with DataRobot

The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments.

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Reimagine application modernisation with the power of generative AI

CIO

In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. The foundation of the solution is also important.

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When it comes to generative AI in the enterprise, CIOs are taking it slow

TechCrunch

To hear the hype from vendors, you would think that enterprise buyers are all in when it comes to generative AI. But like any newer technology, large companies tend to move cautiously. Throughout this year, as vendors feverishly announced new generative AI-fueled products, CIOs took note.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for the future.