AI

The AI Revolution: Getting Culture Right for AI Success

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The article discusses the critical role of fostering a balanced AI culture in enterprises to unlock AI's transformative potential. It emphasizes empowering employees through training and hands-on experience while ensuring governance to manage AI risks, addressing fears and skepticism about AI adoption, and tailoring AI education to different career levels. Leaders highlight that widespread, guided AI experimentation combined with effective governance and measuring ROI will drive innovation and competitive advantage as AI rapidly evolves and becomes integral to business operations.

https://www.cio.com/article/4146677/the-ai-revolution-getting-culture-right-for-ai-success.html

Teleport Report Finds Over-Privileged AI Systems Linked to Fourfold Rise in Security Incidents

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A report by Teleport found that enterprises granting excessive access permissions to AI systems experience 4.5 times more security incidents than those restricting AI access, highlighting identity management's lag behind AI adoption. Based on interviews with 205 security leaders, the study shows that broad AI access correlates with higher incident rates, often due to static credentials and lack of automated governance controls, emphasizing the need for unified, machine-speed identity management to mitigate risks.

https://www.infoq.com/news/2026/03/teleport-ai-report/

Why Cybersecurity’s Uncertainty Problem Is Getting Worse

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Cybersecurity faces increasing uncertainty, with leading cryptographers unable to agree on the greatest threats. Paul Kocher, a cryptography researcher, warns that AI will accelerate the discovery of vulnerabilities in protocols and implementations, posing a significant threat to cybersecurity.

https://www.govinfosecurity.com/cybersecuritys-uncertainty-problem-getting-worse-a-31232

Ransomware and Phishing Still Drive Data-Security Incidents, But AI’s Shadow Looms

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The 12th annual Data Security Incident Response Report by law firm BakerHostetler reveals that ransomware demands averaged $4.24 million last year, rising 70%, while phishing caused 30% of data-security incidents. The report highlights AI's growing role in cyberattacks, evolving beyond phishing enhancement to sophisticated social engineering and automated hacking, signaling a significant shift in the cybersecurity landscape.

https://www.digitaltransactions.net/ransomware-and-phishing-still-drive-data-security-incidents-but-ais-shadow-looms/

The CISO’s Guide to Responding to Shadow AI

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The article provides a guide for Chief Information Security Officers (CISOs) on responding to shadow AI, emphasizing four key steps: assessing the associated risks, understanding the motivations behind unapproved AI use, deciding whether to shut down or integrate shadow AI tools, and reviewing AI governance policies. It highlights that shadow AI usage often arises from the rapid adoption of AI tools without proper oversight, posing risks such as data breaches and operational disruptions, and stresses the importance of balanced governance to manage these risks while fostering responsible AI use within organizations.

https://www.csoonline.com/article/4143302/the-cisos-guide-to-responding-to-shadow-ai.html

AI Sovereignty Risk: a Five-Step Agenda for CIOs

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The article discusses the growing importance of AI sovereignty, where nations control AI ecosystems within their borders, posing challenges for global CIOs. It outlines a five-step agenda for CIOs to manage AI sovereignty risks, including educating executives, consulting legal experts, balancing AI providers, securing data, and anticipating architectural shifts toward hybrid AI models. This approach helps organizations navigate complex regulatory environments and align AI strategies with jurisdictional compliance and enterprise goals.

https://www.idc.com/resource-center/blog/ai-sovereignty-risk-a-five-step-agenda-for-cios/

Before You Scale: a Risk Management Framework for AI Systems

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As AI systems transition from pilot phases to full-scale production, organizations often face hidden risks in governance, data management, operations, and change management that can hinder sustainable growth. EisnerAmper outlines a six-pillar risk management framework—covering governance, business strategy, cybersecurity and data privacy, technology and cloud infrastructure, people and change, and data practices—that helps organizations identify and address potential friction points early, ensuring responsible and scalable AI adoption aligned with established standards like NIST and ISO. Early assessment under this framework is critical for sustaining effective AI systems as usage expands.

https://www.eisneramper.com/insights/artificial-intelligence-insights/ai-risk-management-framework-for-scaling-0326/

Microsoft Backtracks on Copilot Chat Access in M365 Apps

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Microsoft will remove free access to its AI assistant, Copilot Chat, from Office apps like Word, Excel, and PowerPoint for large Microsoft 365 enterprise customers (those with over 2,000 users) starting April 15, 2026, requiring a paid Microsoft 365 Copilot license instead. For smaller customers, Microsoft will impose usage restrictions and reduced performance on Copilot Chat, reflecting a shift to prioritize paid subscriptions despite limited adoption of the full-featured paid version.

https://www.computerworld.com/article/4150022/microsoft-backtracks-on-copilot-chat-access-in-m365-apps.html

Ransomware’s New Era: Moving at AI Speed

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Ransomware attacks are accelerating in speed and sophistication, with threat actors increasingly using artificial intelligence to quickly exploit valid credentials and bypass traditional security tools like endpoint detection and response (EDR). Reports from Halcyon and Arctic Wolf highlight that ransomware tactics have evolved from encrypting data to multi-extortion schemes and direct victim targeting, while AI enables automated, high-fidelity social engineering, making defense more challenging and emphasizing the need for improved access management and transparency in cybersecurity efforts.

https://www.darkreading.com/endpoint-security/ransomware-new-era-moving-ai-speed

Why AI Scaling Is so Hard – and What CIOs Say Works

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The article explains that many organizations struggle to scale AI beyond pilot projects due to high costs, poor data quality, unclear business value, and difficulty integrating it into everyday workflows. CIOs say successful scaling starts with solving real operational problems, involving end users early, improving data foundations, and measuring outcomes instead of experimenting without goals. The article concludes that AI delivers results only when treated as a business transformation effort with governance, user adoption, and clear return on investment, rather than as a standalone technology project.

https://www.informationweek.com/machine-learning-ai/why-ai-scaling-is-so-hard-and-what-cios-say-works

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