Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In technology, the proof of a lasting relationship is in the infrastructure — the pipelines, security services, and log plumbing have to work seamlessly together long before anyone sees the outcome. That’s precisely what Sumo Logic and AWS have built. Aligned around open standards like OCSF (Open Cybersecurity Schema Framework), integrated with services like Security Hub and GuardDuty, and connected through shared telemetry, it makes cloud security and observability possible at scale.

Back before live security video feeds in homes, people would walk around at night checking to make sure they locked every window and door. They took these precautions because they knew that a single open lock gave burglars an opportunity to steal from them. For organizations, vulnerability management programs are a way to lock the doors against cybercriminals.

Compliance is not, nor has it ever been, security. Compliance is the spellcheck of the security world. Security is the work that people do every day to implement, enforce, and monitor the controls that protect systems, networks, applications, devices, users, and data. Compliance is the process of reviewing security work to ensure that it functions as intended. Compliance is an important component of an organization’s security posture.

For too long, security has been defined by reaction, responding to every alert, chasing every anomaly, burning time and energy without clarity. But the strongest fighters don’t swing at every feint. They train, prepare, and conserve their energy for the moments that matter. That’s not just strength; that’s resilience. Now, this philosophy has entered the SOC. And it has a name: Sumo Logic Dojo AI.

The cybersecurity perimeter is gone. In its place, a far more resilient framework has emerged: Zero Trust Networking (ZTN). This security model assumes no transaction is automatically permitted. Every connection, request, and data flow must be verified continuously. Learn how Elastic and Gigamon Application Metadata Intelligence (AMI) enable a security posture built on constant verification, rapid response, and adaptive trust.

Advanced AI-supported threat detection for faster MTTD and MTTR The Ministry of Defence (MoD)’s own assessments describe an unacceptable cyber risk position amid an escalating wave of malicious cyber activity1 — a sobering reality for defence security operations centre (SOC) managers With the risk of cyber attack among the highest managed by the Defence Board2, the pressure on security teams is growing. SOC teams must detect sophisticated cyber threats before they cause damage.

Elastic Defend delivers endpoint protection for macOS Tahoe 26 from the moment it’s released, ensuring your macOS fleet stays secure without delay. Apple macOS Tahoe 26 raises the bar on performance, usability, and security. Elastic Security is ready on day one, giving organizations the confidence to roll out new machines, upgrade existing devices, or evaluate macOS Tahoe 26 for future deployments without delay.

Industries, governments, and enterprises of all kinds have adopted large language models (LLMs) and generative AI (GenAI) into their operations and workflows, unlocking new possibilities for everything from customer interaction to complex data analysis. But with this innovation comes new challenges for security, observability, and data science teams.

In today’s data-driven world, CISOs and senior leadership need to prove that their security programs mitigate risk. Just like grades theoretically quantify how well students understand material their teachers present, cybersecurity metrics quantify your security controls’ effectiveness. As the threat landscape becomes more complex, security teams struggle to identify the metrics that best showcase their value.

Phishing threats remain one of the most common and effective attack methods. Research shows it contributes to over 34% of confirmed breaches. The financial impact is significant as well, with credential-related breaches averaging $4.76 million per incident. And despite years of security awareness training, nearly a third of employees still click on simulated phishing emails. Why does phishing work so well? Attackers exploit gaps in visibility, speed, and user behavior.

AI agents (utilizing LLMs and RAG) are being used within SOCs and SIEMS to both help identify attacks and assist analysts with working more efficiently; however, I’ve done a little bit of research one sunny British afternoon and found that these agents can be abused by attackers and made to go rogue. They can be made to modify the details of an attack, hide attacks altogether, or create fictitious events to cause a distraction while the real target is attacked instead.

When we talk about emerging technologies and digitization, we often forget that while innovators work to bring the best security tools to market, malicious actors are concurrently working to identify loopholes and vulnerabilities in these new systems. Gone are the days when cyber attacks were a rare occasion; now, they happen almost daily.

Remember when we thought the application layer was where all the fun happened? Firewalls, WAFs, EDR, dashboards galore — the entire security industrial complex built around watching what apps do. Well, with “agentic AI” running the show, that middle ground is turning into a bypass lane. Instead of clicking through UIs or APIs, your AI buddy is making direct system calls, automating workflows at the OS and hardware level.

As IT environments become more complex, organizations face rising threat volumes, persistent cybersecurity talent shortages, and adversaries capable of dwelling undetected for days and moving laterally within hours. In this context, choosing between SIEM and XDR is no longer a technical preference; it’s a strategic decision that shapes how your organization defends itself.

Imagine security data and analytics like a carnival’s hall of mirrors. From convex mirrors that show you a shorter, squatter version of something to the concave mirrors that show a highly magnified image, you see the same object in multiple ways. Every view gives you a different insight and provides a unique vantage point. Online Analytical Processing (OLAP) systems are different mirrors that allow security teams to create focused analytics models for different insights about your security posture.

In today’s modern SOC, more data doesn’t always mean better outcomes. Security teams are overwhelmed by irrelevant telemetry, storage bills are out of control, and compliance regulations demand long-term retention—even for data with little threat value. Legacy SIEMs treat all logs the same, flooding analysts with noise and slowing down response times. The result? Bloated infrastructure, delayed investigations, and boardroom frustration over unproven ROI.