Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Relying on legacy hardware for modern business is like trying to run a high-speed bullet train on old wooden tracks. The train has immense potential, but the underlying foundation simply can’t support the speed, capacity, or safety required for the journey. As companies migrate to the cloud and adopt artificial intelligence, their networks must evolve. Outdated systems create bottlenecks that drain resources and expose sensitive data to modern threats.

Because IT security no longer moves at human speed, endpoint management can no longer be seen as a background IT function. It’s now the front line and critical as ever. Attackers move fast, and AI makes them faster. Organizations can’t keep validating exposure and fixing issues the old way. Endpoint management has to run at machine speed because every manual handoff and every delayed exception adds more risk.

The Mini Shai-Hulud supply chain attack compromised more than 170 packages across npm and PyPI, including packages from TanStack, Mistral AI, and Guardrails AI, by hijacking legitimate CI/CD publishing workflows to distribute malicious versions that still carried apparently valid provenance signals.

Runbooks are supposed to be the safety net under operations. Unfortunately, most aren't because they live in wikis that decay as tools change, get linked from alerts but never consulted, and fail the responder the moment pressure arrives. The gap is between what the runbook says and what the responder can actually execute. Teams reach for AI to close the gap.

Imagine a customer service representative at your organization uploads sensitive customer data into an AI tool to draft emails more quickly. When an employee uses an AI tool without IT approval, it is known as shadow AI, and such scenarios are becoming increasingly common. Among employees who use AI at work, 78% report using tools that have not been formally approved by their organization, according to Microsoft’s 2024 Work Trend Index.

Data exfiltration is the unauthorized transfer of sensitive data out of an organization's control. It happens across endpoints, cloud applications, removable storage, email, and, increasingly, AI tools. Understanding the most common forms of data exfiltration is the first step toward stopping it.

In conversations with CISOs about their agentic environments, the question I ask first is not whether they have agents deployed. Most do. It is not whether those agents are creating value. Most are. The question I ask is whether they have mapped their Agentic Security Graph. Almost none of them have. And that gap, between the agentic infrastructure that exists inside their organizations and the visibility they have into it, is where the most serious AI security risk in the enterprise lives right now.

Article by Luis Monzon originally published on MyBroadband, May 9, 2026. Anthropic’s AI model Mythos, part of its Claude software, represented a clear threat to banks and financial institutions in South Africa. This is according to Bijan Sanii, CEO of Canadian fraud detection provider INETCO. Anthropic, one of the world’s most important generative AI makers, positioned Mythos as an extremely capable AI model designed to identify vulnerabilities in critical software beyond human capabilities.