Articles about Generative AI

Agentic AI has been in the news quite a bit of late, but how should enterprises expect it to impact their organizations?

We spoke to Mike Finley, CTO of AnswerRocket, to discuss Agentic AI's benefits, use cases and more.

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The latest State of Pentesting report from Cobalt reveals that organizations are fixing less than half of all exploitable vulnerabilities, with just 21 percent of GenAI app flaws being resolved.

It also highlights a degree of over-confidence with 81 percent of security leaders saying they are 'confident' in their firm's security posture, despite 31 percent of the serious findings discovered having not been resolved.

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Despite many organizations reporting significant business gains from using GenAI, data privacy is still a major risk. Notably, 64 percent of respondents to a new survey worry about inadvertently sharing sensitive information publicly or with competitors, yet nearly half admit to inputting personal employee or non-public data into GenAI tools.

The latest Data Privacy Benchmark Study from Cisco, with input from from 2,600 privacy and security professionals across 12 countries, shows an increased focus on investing in AI governance processes, an overwhelming 99 percent of respondents anticipate reallocating resources from privacy budgets to AI initiatives in the future.

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Artificial intelligence has claimed a huge share of the conversation over the past few years -- in the media, around boardroom tables, and even around dinner tables. While AI and its subset of machine learning (ML) have existed for decades, this recent surge in interest can be attributed to exciting advancements in generative AI, the class of AI that can create new text, images, and even videos. In the workplace, employees are turning to this technology to help them brainstorm ideas, research complex topics, kickstart writing projects, and more.

However, this increased adoption also comes with a slew of security challenges. For instance, what happens if an employee uses a generative AI service that hasn’t been vetted or authorized by their IT department? Or uploads sensitive content, like a product roadmap, into a service like ChatGPT or Microsoft Copilot? These are some of the many questions keeping security leaders up at night and prompting a need for more visibility and control over enterprise AI usage.

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Over 70 percent of developers and quality assurance professionals responding to a new survey say their organization is currently developing AI applications and features, with 55 percent stating that chatbots and customer support tools are the main AI-powered solutions being built.

The research from Applause surveyed over 4,400 independent software developers, QA professionals and consumers explored common AI use cases, tools and challenges, as well as user experiences and preferences.

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In recent years generative AI has made its way into many areas of business, helping to transform and streamline processes. However, its potential in the procurement space remains relatively unexplored.

We talked to Kevin Frechette, CEO of Fairmarkit, to find out how enterprises can exploit GenAI to gain agility, efficiency, and smarter decision-making in their sourcing decisions.

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Chinese AI models (like DeepSeek) are outperforming US models like Meta Llama in specific categories such as sensitive information disclosure according to a new AI Trust Score introduced by Tumeryk.

It evaluates AI models across nine key factors, including data leakages, toxic content, truthfulness, and bias. This enables CISO’s to ensure their AI deployments are secure, compliant, and trustworthy, and offers developers solutions for addressing any issues in their AI applications.

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Enterprises are increasingly using GenAI to transform their organization. As they move ahead, they're evaluating their preparedness from a business, safety, skills, and product level. But there's another key factor at the backend that's being overlooked: the network.

Full GenAI adoption introduces significant new challenges and demands on the network, such as bandwidth strain and unique security vulnerabilities. If these demands aren't accommodated, organizations won't realize the benefits of GenAI.

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The latest Enterprise Cloud Index (ECI) survey from Nutanix shows that that while 80 percent of organizations have already implemented a GenAI strategy, implementation targets vary significantly.

Organizations are eager to leverage GenAI for productivity, automation, and innovation, but they also face critical hurdles in the form of data security, compliance, and IT infrastructure modernization. 95 percent of respondents agree that GenAI is changing their organization’s priorities

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Until the late 1990s, manufacturing reigned as the lifeblood of the global economy -- leading in productivity, employment, growth, and investments across all points of the world. However, once we neared the close of the 20th century, manufacturing found its Achilles heel in the compounded complexity accrued from outdated processes, an over-reliance on human labor that simply couldn’t meet its extreme needs, supply chain disruptions, and rising costs.

I fear that today, the information technology industry finds itself at all-too-familiar cross-roads. Why is this?

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For many people AI has been seen as an answer to all their problems. But a recent Gartner report suggests we could be entering a 'trough of disillusionment' as the biggest challenge when adopting GenAI is estimating and proving the business value.

Getting that value needs creative thinking and integration of GenAI with other technologies that can benefit from its ability to analyze, summarize and create content.

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New research from Netskope Threat Labs has found that 94 percent of organizations are now using GenAI apps, but interestingly more than 99 percent have controls in place to mitigate the risks that they pose.

Organizational use of GenAI has grown from 81 percent of companies using the apps in 2023. ChatGPT continues to be the most popular genAI app, being used in 84 percent of organizations.

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Google has introduced Whisk, a new generative AI tool designed to make image creation more interactive and creative. Unlike traditional AI tools that rely heavily on lengthy text prompts, Whisk allows users to start with images. Simply drag and drop images into the interface, and Whisk lets you generate unique creations by combining these visuals in novel ways.

Whisk enables users to input up to three images: one for the subject, one for the scene, and another for the style. This approach gives users the flexibility to create anything from digital plushies to enamel pins or stickers. Some early examples from Whisk include a fantastical fish, a whimsical walrus, and even a glazed doughnut reimagined as an enamel pin.

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AI, and generative AI in particular, is expected to greatly enhance productivity within work processes. Some studies estimate that generative AI could contribute between $2.6 trillion and $4.4 trillion annually to the economy.

However, AI infrastructure is costly because the underlying algorithmic problems are extremely computationally intensive and this means there's a potential gap between demand and the capacity needed to meet it.

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Generative AI (GenAI) holds a lot of promise for software developers: an ability to help them ship code faster, improve productivity, and reduce time spent on menial and repetitive tasks. But how much of that promise are GenAI coding tools actually delivering -- and how much is hype?

We spoke with Matt Hoffman, product manager and data analyst at Uplevel, an engineering intelligence platform that uses data from across developer tools and collaboration platforms to help engineering leaders drive organizational improvements. Uplevel's data science team, Uplevel Data Labs, recently studied the impact of Copilot usage on developer productivity.

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