Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Over two-thirds of enterprises are already running agentic AI in production, according to a 2025 industry survey on the state of agentic AI security. Fewer than one in four have the visibility to know what those agents are actually doing. That gap is live right now, in systems handling customer data, financial records, and protected health information.

In enterprise software, winning a deal is not just about product fit. The buying process matters just as much. Even when a customer is committed to moving forward, procurement friction can slow or stall a deal. New vendor setup, contract reviews, billing workflows, approval chains, and budget constraints all add complexity that extends timelines and increases the risk of deals falling apart. That is why procurement flexibility is not a back-end operational concern. It is a customer experience issue.

Generative AI is no longer emerging. It is already embedded in how businesses work. From content creation and research to customer support and internal productivity, AI tools are rapidly becoming part of everyday workflows across SMBs and the MSPs that serve them. But this shift comes with a hard reality: As GenAI adoption accelerates, so do the risks.

Threat actors are abusing agentic AI automation platforms to deliver malware and send phishing emails, according to researchers at Cisco Talos. The researchers observed attackers using n8n, a legitimate platform that automates workflows in web apps and services like Slack, GitHub, Google Sheets, and others.

Complexity has become a defining security challenge as organizations expand across hybrid and multi-cloud environments. In fact, 52% of surveyed organizations ranked multi/hybrid cloud complexity among their top three infrastructure concerns.1 This complexity creates fragmented visibility across cloud providers, workloads, and Kubernetes environments — gaps that adversaries increasingly exploit to move undetected.

Many modern cloud security challenges come down to visibility. The complexity of modern environments, the deluge of alerts, and the lack of relevant context make it difficult for security teams to identify and prioritize threats — and easy for adversaries to slip through undetected.

Amazon SageMaker accelerates the process of training and deploying machine learning models. However, as AI adoption scales from individual experiments to enterprise-wide production, the focus of leading Fortune 500 software development operations and security teams must shift from pure velocity to governance.