Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

An employee leaves the company, but their SaaS account remains active. A contractor still has access to a project workspace months after the contract ends. Someone changes departments and keeps permissions from their previous role. Meanwhile, IT is managing identities, human and non-human across multiple directories, cloud platforms, SaaS applications, and third-party tools. Individually, these accounts may seem insignificant.

Android devices are designed for flexibility. Employees can check notifications, adjust connectivity, open settings, and move between apps with a few taps. That flexibility works well for personal use, but it can introduce distractions, security gaps, and usability issues when a device is dedicated to business tasks.

Security teams comparing unified data security platforms in 2026 are no longer just choosing between DLP vendors. They're deciding how much of their data security architecture, discovery, classification, enforcement, insider risk, and AI governance should live in one system versus remain assembled and operated from separate tools. That decision has gotten harder. DSPM vendors are adding DLP. DLP vendors are adding posture management. Everyone claims AI coverage.

Privileged accounts are standing invitations for attackers, with credentials to steal and permissions to misuse. When administrative rights are persistently active, whether or not they’re being used, privileged accounts significantly expand the attack surface. Zero Standing Privilege (ZSP) shrinks that risk by ensuring no user holds permanent elevated access.

Modern threats increasingly unfold at runtime, where attackers exploit live workloads, spawn malicious processes, and move laterally across your environment. Detecting that activity is essential, but a signal only matters if you can stop it. When a threat appears, every step before a response gives an attacker more time to act. Datadog Workload Protection can now directly remediate threats with both automated and manual response.

The volume of published incident research involving agentic AI is increasing, and the analysis that follows each report tends to concentrate on the same attribute: speed. The recent investigation from Unit 42, the threat intelligence and incident response group at Palo Alto Networks, is a representative case.

Your coding agents run overnight on cloud dev boxes, with auto-approve on and an organization token in the environment, and the only thing standing between a public repository and that token is whatever the repository’s README tells the agent to do.

Fraud doesn’t wait for your investigation queue. Every minute an alert sits unresolved gives fraudsters more time to move the money. Yet at most financial institutions, a single case still takes an analyst 10 to 30 minutes to investigate — pulling transaction history, checking device and behavioral data, weighing risk signals and documenting a decision. Multiply that by hundreds or thousands of alerts a day, and slow investigation isn’t just an inconvenience.

Cyber threats rarely start in hidden corners of the internet. Most begin in plain sight, on social media, before moving into Telegram channels and dark web forums. This blog looks at why organisations need visibility across the open and underground web, and how CYJAX connects the two into one picture of risk.