Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.