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AI market evolution: Data and infrastructure transformation through AI

CIO

However, many face challenges finding the right IT environment and AI applications for their business due to a lack of established frameworks. While early adopters lead, most enterprises understand the need for infrastructure modernization to support AI. AI applications rely heavily on secure data, models, and infrastructure.

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Cloud infrastructure spending more than doubles in the third quarter of 2024

CIO

Spending on compute and storage infrastructure for cloud deployments has surged to unprecedented heights, with 115.3% billion, highlighting the dominance of cloud infrastructure over non-cloud systems as enterprises accelerate their investments in AI and high-performance computing (HPC) projects, IDC said in a report.

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Chinese cyber espionage growing across all industry sectors

CIO

China-linked actors also displayed a growing focus on cloud environments for data collection and an improved resilience to disruptive actions against their operations by researchers, law enforcement, and government agencies. They complicate attribution due to the often short-lived nature of the IP addresses of the nodes being used.

Industry 198
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Implementing a Version Control System for AWS QuickSight

Xebia

Implementing a version control system for AWS QuickSight can significantly enhance collaboration, streamline development processes, and improve the overall governance of BI projects. The Azure CLI (az command line tool) then creates the pull request and provides a link to the user for review.

AWS 130
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IT leaders brace for the AI agent management challenge

CIO

Agentic AI systems require more sophisticated monitoring, security, and governance mechanisms due to their autonomous nature and complex decision-making processes. Durvasula also notes that the real-time workloads of agentic AI might also suffer from delays due to cloud network latency.

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Security is dead: Long live risk management

CIO

Unfortunately, many organizations still approach information security this way waiting until development is nearly complete before conducting security reviews, penetration tests, and compliance checks. This means creating environments that enable secure development while ensuring system integrity and regulatory compliance.

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The key to operational AI: Modern data architecture

CIO

Technology: The workloads a system supports when training models differ from those in the implementation phase. However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture.