Remove Compliance Remove Government Remove Weak Development Team
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For startups, trustworthy security means going above and beyond compliance standards

TechCrunch

Oren Yunger is an investor at GGV Capital , where he leads the cybersecurity vertical and drives investments in enterprise IT, data infrastructure, and developer tools. Winning enterprise sales teams know how to persuade the Chief Objection Officer. In reality, compliance means that a company meets a minimum set of controls.

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IT pros: One-third of AI projects just for show

CIO

Their teams miss out on crucial learning experiences, leaving them ill-equipped to handle genuine AI deployments down the road.” Nagaswamy has witnessed several organizations launching AI projects simply to impress board members or investors. This trend is concerning,” he says. “AI

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The future of data: A 5-pillar approach to modern data management

CIO

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. Poor-quality data is as detrimental as a pipeline outage, and perhaps more, as it can lead to bad decisions and provide harmful information to customers.

Data 167
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Overcoming the 6 barriers to IT modernization

CIO

Solution: Invest in continuous learning and development programs to upskill the existing workforce. Security and compliance concerns Barrier: Modernizing IT systems often involves handling sensitive data and integrating with external platforms, raising security and compliance concerns. A: Expensive, bad processes.

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IT leaders brace for the AI agent management challenge

CIO

What is needed is a single view of all of my AI agents I am building that will give me an alert when performance is poor or there is a security concern. Agentic AI systems require more sophisticated monitoring, security, and governance mechanisms due to their autonomous nature and complex decision-making processes.

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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] Without the necessary guardrails and governance, AI can be harmful.

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When is data too clean to be useful for enterprise AI?

CIO

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211