Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The backup finished. The dashboard shows a green tick. You close the laptop and go home. But what if that green tick is lying to you? It sounds dramatic. It is not. Across organisations of every size, backups complete successfully every night while quietly storing corrupted data, broken recovery chains, or in the worst cases, malware that hitched a ride before the snapshot was taken. Nobody knows. Nothing alerts. The tick stays green. The problem only surfaces the moment you need the backup most.

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 Outdated systems create bottlenecks that drain resources and expose sensitive data to modern threats.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.