Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The endpoint detection and response (EDR) market has become crowded with solutions claiming comprehensive threat protection. Yet many organizations struggle with EDR platforms that force difficult tradeoffs: prevention-focused tools with limited forensic depth, investigation-heavy solutions that overwhelm lean security teams, or vendor-locked architectures requiring wholesale replacement of existing security infrastructure.

As 2025 comes to a close, it’s the perfect time to look back at the last year to see what LevelBlue as a company accomplished. We can point to the thousands of clients that we helped keep secure, more than a few acquisitions that have resulted in the creation of LevelBlue as the world’s largest pure-play MSSP company and pulling it all together are the accolades from industry analyst firms and the media showing the extent and depth of our expertise.

Ultimately, my prediction for cybersecurity in 2026 is that we will continue to see more of the same, just a lot faster. Threat actors will keep using the methods that work because there is no incentive to change what already delivers results.

Insider threats have become one of the most difficult and damaging challenges in cybersecurity. Unlike external attackers, insiders already have access to sensitive data and systems. Their actions often appear legitimate until it’s too late. Whether it’s a malicious employee stealing intellectual property or a well-meaning one accidentally leaking customer information, insider incidents are complex, nuanced, and often invisible to traditional security tools.

Is an AI-to-AI attack scenario a science fiction possibility only for blockbusters like the Terminator series of movies? Well, maybe not!

The rapid adoption of AI agents for development is creating a critical security gap. We are moving from predictable logic, deterministic code paths, and human-driven workflows to non-deterministic agents that reason, plan, and act autonomously using large language models across the broader software development lifecycle. As enterprises adopt these autonomous AI agents, the core challenge isn’t just the new risks and attack vectors; it’s a loss of runtime control.

Large language models (LLMs) have revolutionized artificial intelligence and are rapidly transforming the cybersecurity landscape. As these powerful models become commonly used among both attackers and defenders, developing specialized cybersecurity LLMs has become a strategic imperative. The CrowdStrike 2025 Global Threat Report highlights a concerning trend: Threat actors are increasingly enhancing social engineering and computer network operations campaigns with LLM capabilities.

Imagine this. Your phone rings on January 2nd, and it’s your DevSecOps and AppSec groups. A major security vulnerability is exposing your business, and your teams are trying desperately to find and fix it to protect your data. You probably have scars as far back as Log4j, as well as threats from more recent incidents like npm attacks, Glassworm and others ringing in your ears. With CVEs expected to rise by tens of thousands a year, you can envision that the situation will only worsen.

Energy. Water. Finance. Healthcare. Transportation. Each of these sectors underpins the daily functioning of modern society—and each is now a potential attack vector for cyber attacks.

Following our successful MITRE ATT&CK Evaluation results earlier this month, WatchGuard has now achieved an AAA rating in the SE Labs Endpoint Protection Suite (EPS) evaluation, the highest possible score in this independent test. SE Labs evaluates prevention and response in real-world scenarios, validating the ability to stop attacks while allowing legitimate business activity to continue uninterrupted.