Articles about Artificial Intelligence (AI)

Identities are probably the biggest attack surface for organizations in today's world as employees rely more on systems and apps to do their jobs.

Mapping identity and access data from the large, disparate, and often disconnected, mix of on-premise and cloud systems that enterprises use is a major challenge.

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Admit it, you have at some time or other exaggerated your skills when applying for a job. But a new survey finds that this is becoming much more of a problem since the advent of AI.

The survey by Capterra of 3,000 job seekers around the world shows that 58 percent say they are using AI tools as part of their job search.

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As we've already seen today AI systems are becoming increasingly popular targets for attack.

New research from Snyk and Lakera looks at the risks to AI agents and LLMs from prompt injection attacks.

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The UK government has announced the launch of a new scheme that will encourage the building of new generative AI tools to help teachers when they’re planning lessons or marking homework.

This will involve the creation of a 'data store' for education data including the national curriculum, guidance for teachers, lesson plans and more. The £3m ($3.96m) data store will help tech companies build AI tools that teachers can trust to help in their work by making this data machine readable.

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New research from Legit Security shows that widely available GenAI development services risk sensitive information exposure, or leakage of secrets.

Legit's analysis of unprotected vector databases finds that 30 servers investigated contained corporate or private data, including company email conversations, customer PII, product serial numbers, financial records, resumes, and contact information.

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Although enterprises are adopting GenAI in a big way, only five percent of the 1,000 cybersecurity experts responding to a new survey have confidence in the security measures protecting their GenAI applications even as 90 percent are actively using or exploring its use.

The research from Lakera shows attack methods specific to GenAI, or prompt attacks, are easily used by anyone to manipulate the applications, gain unauthorized access, steal confidential data and take unauthorized actions.

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A new report from data lakehouse company Starburst highlights the critical role of real-time hybrid data access and robust security in successful AI implementations.

Based on a survey of 300 IT professionals from diverse industries in the United States and Western Europe, carried out by TheCUBE Research, the report shows 90 percent of respondents believe their data management practices are either somewhat or very aligned with their AI innovation goals, highlighting the critical role of coherent data strategies.

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Document processing specialist Accusoft is releasing new Auto Tagging and Classification modules within its PrizmDoc secure document viewer.

The modules use the IBM watsonx data and AI platform with the IBM Granite foundation model to automate manual tagging and classification providing consistency and ensures documents are meticulously organized, easily searchable, and compliant with regulations.

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After all the headlines we have read about how amazing Artificial Intelligence (AI) is and how businesses would literally stagnate if they didn't have it, it was interesting to read this article in Forbes, who suggest that AI stock is showing 'bubble-like' tendencies and may soon experience a sharp correction as businesses struggle to operationalize AI. So, should we write off AI? Maybe not.

Perhaps the better plan is to accept that AI is at the top of its hype cycle, and, like any new technology, there will be some limitations to ChatGPT-style AI, which in its raw state can be subject to issues like hallucinations. We knew this anyway, as the CEO of the company behind it explained: "ChatGPT is incredibly limited but good enough at some aspects to create a misleading impression of greatness. It's a mistake to be relying on it for anything important right now."

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The majority of teams today are contending with too much data which means they struggle to generate meaningful insights from their information, and can become overwhelmed by the sheer volume.

We spoke to CallMiner CMO Eric Williamson who believes sourcing customer feedback from different sources might help solve the problem.

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The collapse of Baltimore's Francis Scott Key Bridge sent shockwaves both nationally and internationally earlier this year. The city is now faced with the significant economic loss of a critical transport channel – and the challenging task of its reconstruction. Engineering and construction specialists anticipate that the rebuild effort could take up to 15 years. As a once-widely utilized structure, there is a deep sense of urgency to expedite the resurrection of the Key Bridge.

But current engineering and construction processes could wreak havoc on the possibility of an expeditious rebuild. The design and planning processes for large architecture, engineering and construction (AEC) projects, specifically critical infrastructure such as airports, bridges and ports, are historically rigid. Operating with a highly formalized approach derived from years of informed engineering practices, the volume of factors to contemplate around the build of a crucial transport structure is astronomical. Identifying and adhering to rigorous regulatory standards is only the first hurdle. Combine this with navigating other variables such as climate change, traffic implications and sourcing highly specialized materials, and suddenly a decade doesn’t seem like an unreasonable timeline for the construction of a major transport hub.

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Almost one in 10 decision-makers in both the UK (eight percent) and US (seven percent) are planning to spend over $25 million on AI this year.

A survey from Searce, of 300 C-Suite and senior technology executives at organizations with more than $500 million, finds that for US decision-makers, data privacy and security are ranked as the number one hurdle to adopting AI (20 percent), whereas UK decision-makers rank lack of qualified talent as their number one challenge (19 percent).

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The rise of artificial intelligence (AI) has brought immense opportunities and significant challenges. While AI promises to revolutionize various sectors, from healthcare to finance, the erosion of public trust in AI safety is becoming a critical concern.

This growing skepticism is fueled by factors such as a lack of transparency, ethical considerations, and high-profile failures. Addressing these issues cannot be overstated, as public trust is essential for the widespread acceptance and successful integration of AI technologies.

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A new report from Pure Storage finds that the emergence of AI is a key avenue for innovation, but infrastructure issues, talent shortages and energy costs are holding back progress.

Based on a survey of 1,500 global respondents carried out by Vanson Bourne the report finds that although CIOs and other senior IT leaders face significant economic pressures, driving innovation remains imperative for enterprises.

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A new study finds that 70 percent of enterprises are still experimenting with AI and preparing for it is a top priority.

However, the report from Komprise finds that budgets are an even higher priority with only 30 percent saying they will increase their IT budgets to support AI projects.

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