Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

‍Kovrr's cyber risk quantification (CRQ) models are built on a continuously updated database of real-world cyber events, drawing on regulatory disclosures, company filings, legal reports, and proprietary insurance claim intelligence to produce financial exposure estimates grounded in how incidents actually unfold.

Most organisations know they need disaster recovery. Far fewer know what a well-designed DR architecture actually looks like. The gap between “we have backups” and “we can recover our business in under an hour” is architectural. It’s the difference between storing copies of your data somewhere offsite and building a recovery environment that’s been pre-configured, tested, and ready to take over when your production systems fail.

May 19, 2026 What the 2026 Verizon DBIR Reveals About the State of Application Security Read More Natalie Tischler May 14, 2026 How to Manage Risks Within Your Applications Read More Natalie Tischler May 12, 2026 AI Coding Tools Are Creating a Security Gap We Must Close Immediately Read More Natalie Tischler.

In April 2026, Cato CTRL identified and blocked an attempted intrusion against a global manufacturing customer involving TencShell, a previously undocumented, Go-based implant derived from the open-source Rshell C2 framework. The activity appeared in traffic associated with a third-party user connected to the customer environment.

The emergence of the CRINK axis—a coordinated cyber-threat nexus comprised of China, Russia, Iran, and North Korea—has dramatically impacted the 2026 global risk landscape. As these nation-states utilize AI-driven scale and living-off-the-land (LOTL) tactics to target critical infrastructure, SafeBreach’s new content series provides essential intelligence on their evolving motivations and methods.

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.