Articles about Artificial Intelligence (AI)

Scammers are already using AI-powered automation to manipulate marketing channels. We don't want to hand out a playbook for bad actors, but it's important to recognize the growing number of scams that have been enhanced by AI and automation tools. Most scams are still about money, either directly or by collecting personal data that can be used to access financial information. If there is no money, there is typically little motive.

In a space like eCommerce, for example, we've seen scammers clone legitimate websites to trick customers into handing over their payment information. These spoof sites look identical to the real thing. 

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According to recent projections from Gartner, by 2028 90 percent of enterprise software engineers will use AI code assistants, up from less than 14 percent in early 2024. But relying on AI in development roles also introduces risks.

Snyk is launching a new AI-native agentic platform specifically built to secure and govern software development in the AI Era.

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Healthcare hinges on diagnostics, which is why doctors must accurately identify the illnesses they aim to treat to provide proper care for their patients. An incorrect diagnosis introduces the risk that treatment may be ineffective or harmful.

To diagnose properly, doctors rely on data by measuring key metrics and evaluating their implications based on experience, education, and best practices. The more data available to doctors, the more accurate their diagnoses can be.

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The promise of GenAI is undeniable, it offers transformative potential to streamline workflows, boost efficiencies, and deliver competitive advantage. Yet, for many organizations, the journey to implement AI is far from straightforward.

Obstacles typically fall into three categories: strategic, technological, and operational. We spoke with Dorian Selz, CEO and co-founder of Squirro, to explore these obstacles in more detail, as well as looking at some of the biggest misconceptions enterprises have when starting their GenAI journey.

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We all know that businesses are facing a raft of more sophisticated cyberthreats, partly driven by AI. We also know that there can be an impact beyond the financial in terms of damage to reputation and loss of customers.

A new report from cyber insurance specialist Hiscox reveals that 67 percent of organizations report increase in attacks and 34 percent of firms have compromised cybersecurity measures due to lack of expertise in managing emerging tech risks.

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New research shows that 30 percent of large enterprises have already made the strategic commitment to a sovereign AI and data platform, and 95 percent say it will be mission critical for them within the next three years.

The research by EDB interviewed more than 2,000 senior executives across 13 countries about how they are planning for the agentic AI world. The initial findings show that 30 enterprises a day are making strategic commitments to becoming sovereign AI and data platforms.

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Hybrid cloud infrastructure is under mounting strain from the growing influence of artificial intelligence, according to a new report.

The study, from observability specialist Gigamon, of over 1,000 global security and IT leaders, shows breach rates have surged to 55 percent during the past year, representing a 17 percent year-on-year rise, with AI-generated attacks emerging as a key driver of this growth.

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Use of generative AI is becoming more common, but this comes with a multitude of inherent risks, security and data privacy being the most immediate. Managing these risks may seem daunting, however, there is a path to navigate through them, but first you have to identify what they are.

We talked to Robert W. Taylor, Of Counsel with Carstens, Allen & Gourley, LLP to discuss how a failure to identify all the relevant risks can leave businesses open to to unexpected legal liabilities.

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While 92 percent of respondents to a new survey say collaboration and information sharing are either 'absolutely crucial' or 'very important' in the fight against cyber threats, the results tell a different story when it comes to the adoption of this practice.

The study from Cyware, conducted among cybersecurity professionals at the RSA Conference 2025, finds only 13 percent say their current automation between cyber threat intelligence (CTI) and SecOps tools is working well. Nearly 40 percent day they struggle to coordinate data across critical security tools like Threat Intelligence Platforms (TIPs), SIEMs, and vulnerability management platforms.

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With new threats such as AI-powered attacks, enterprises must be fully prepared and confident about protecting themselves and their customers and build a unified security operations center (SOC) that combines human expertise with AI advancements.

A new report from Splunk looks at the mounting challenges faced by SOCs. It uncovers the pain points that hamper organizations and open their doors to threats -- 46 percent of respondents say they spend more time maintaining tools than defending the organization, while only 11 percent trust AI completely for mission-critical tasks. Furthermore, 66 percent experienced a data breach in the past year, making it the most common security incident.

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New research shows that 97 percent of CIOs have edge AI either already deployed or on their
roadmap, with only three percent reporting no current plans to implement these technologies.

The report from ZEDEDA, based on a survey by Censuswide of over 300 US CIOs, finds 80 percent of CIOs with deployed edge AI solutions use them for customer experience improvements, while nearly as many (77 percent) focus on risk management applications, including predictive maintenance.

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The use of AI in software development can save valuable time completing routine tasks. But what if it could autonomously respond to events, implement changes, and submit code through standard pull requests?

This is what Zencoder is doing with the launch today of Autonomous Zen Agents for CI/CD, bringing groundbreaking AI automation directly into the software development infrastructure.

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Updates to the Starburst data platform for apps and AI are designed to accelerate enterprise AI initiatives and support the transition to a future-ready data architecture built on a data lakehouse.

At the heart of these changes are Starburst AI Workflows, a purpose-built suite of capabilities that speed AI experimentation to production for enterprises. AI Workflows provides a link between vector-native search, metadata-driven context, and robust governance, all on an open data lakehouse architecture.

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New research shows that one in 10 prompt injection atempts against GenAI systems manage to bypass basic guardrails. Their non-deterministic nature also means failed attempts can suddenly succeed, even with identical content.

AI security company Pangea ran a Prompt Injection Challenge in March this year. The month-long initiative attracted more than 800 participants from 85 countries who attempted to bypass AI security guardrails across three virtual rooms with increasing levels of difficulty.

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The Python programming language has become the foundation of modern AI and machine learning applications. Of course that makes it a prime target for supply chain attacks.

Public registries do minimal vetting of hosted artifacts, and they don't provide assurance that the distributed library matches its source code, exposing enterprises to supply chain attacks. Python libraries are also susceptible to supply chain attacks because many projects include more than just pure Python code -- for example project maintainers often rebundle shared system libraries into their Python libraries to ensure stable behavior.

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