risk mitigation

The December 2025 Palo Alto Networks State of Cloud Security Report reveals that cloud security is lagging behind the rapid growth and complexity of modern IT, with a critical emphasis on identity risks and the urgent need for integration of security operations to protect increasingly vulnerable cloud systems.

In a competitive AI market, the key to success lies not in the technology itself but in crafting a solid business case that emphasizes clear problem definition, coalition-building, scalable solutions, and pre-deployment governance to ensure measurable ROI and executive buy-in.

The Model Context Protocol (MCP) donation to the Agentic AI Foundation, under the Linux Foundation, aims to ensure its neutral governance and collaborative development within a multi-vendor environment.

Amazon Web Services (AWS) is launching on-premises AI factories that integrate advanced hardware and managed service offerings into customer data centers, enabling organizations to develop and deploy AI applications while addressing data sovereignty concerns and enhancing their existing ERP systems.

IBM is modernizing Toyota South Africa Motors' core systems through two SAP S/4HANA upgrades that enhance data visibility and streamline operations, marking a critical step in Toyota's transition from legacy systems ahead of the 2027 support deadline.

As companies rapidly adopt generative and autonomous AI technologies, effective governance has become essential to mitigate risks such as accuracy, bias, and compliance, necessitating executive oversight and coordinated efforts across various departments to ensure trust and accountability in AI deployments.

The shift from monolithic ERP systems to composable architectures enables businesses to integrate specialized applications via APIs, enhancing agility and functionality while mitigating risks associated with digital transformations.

With Oracle sunsetting its GRC solutions by May 2025, organizations must swiftly transition.