AI SOC,
Done Right

SOCs still optimize for reactive speed—but reactive is capped by human attention. The shift is preemptive: investigations done before..

Recognised across three categories: Threat Detection Leader • SIEM Disruptor • User Reliability Leader

Whether you’re looking at emerging vendors, evolving threats, or shifting architectures, our timely, opinionated insights help modern security leaders make smarter, faster decisions.

The week of 18 – 24 May 2026 was defined by ecosystem-level compromise. Three independent supply-chain attacks landed in a single week — across npm, CI/CD workflows, and IIS web servers. A critical authentication-bypass vulnerability in SD-WAN edge appliances (CVE-2026-20182) is under active mass scanning, with confirmed post-compromise webshell deployment. Mobile malware reached a cross-platform peak across macOS, Android, and infostealer ecosystems.

Security teams don’t have an alert problem. They have a detection and operationalization problem. Here’s how this week’s adversaries prove it.

Where SOC workflows fail this week, and what to do about it

If you have sat in a room recently where the agenda item was 'AI security strategy,' you know how the conversation tends to go. There is broad agreement that AI will reshape security operations. There is real urgency to move. And then someone asks the question that tends to slow everything down: where do we actually start?

The Single-SIEM SOC Is Over. Here’s What Comes Next.

Over the last year working with SOC teams, one thing has become clear to me: we don’t just have an alert volume problem, we have a detection quality problem.

For years, security operations have been optimized around one core function: responding to alerts. SIEMs generate alerts. SOCs triage them. MDR providers investigate and close tickets. Success is measured in mean time to respond, tickets resolved, and alerts handled per analyst. But this model is reaching its limits.

Most SOCs worry about false positives — the noisy alerts that eat away analyst time and slow down response. But what if the real killer isn’t the noise you hear, but the silence you don’t?

How AiStrike transforms ticketing data into continuous cybersecurity improvement.

Transforming to an AI-Powered Self-Improving SOC

Global Software Design Company Leverages AiStrike to Investigate Cloud Alerts

AI-native platform improves detection quality to cut alert noise, eliminates detection blind spots, and maximizes SIEM ROI through continuous optimization

SAN FRANCISCO, CA – [02-04-2026] – AiStrike, an AI-native cyber defense platform built for modern security operations, today announced the launch of AiStrike MDR, an AI-powered Managed Detection and Response (MDR) service designed to replace traditional, human-heavy MDR with an AI-led, expert-guided operating model built for scale, speed, and measurable outcomes.

The era of purely reactive security operations is over. AiStrike, a cybersecurity company pioneering AI-native, preemptive cyber defense, today announced it has raised $7 million in Seed funding to scale its agentic AI platform for security operations. The round was led by Blumberg Capital, with participation from Runtime Ventures, Oregon Venture Fund, and strategic angel investors.

Reuniting a Proven Leadership Team to Build the Future of Autonomous Security Operations.

San Francisco, CA – April 14, 2025 – AiStrike, the AI SOC automation platform transforming cybersecurity operations, today announced the launch of its AI Agents for Detection Optimization—a first-of-its-kind capability that helps security teams improve detection quality, eliminate blind spots, and reduce alert noise by automatically identifying coverage gaps and tuning detections in real time.

AiStrike leverages advanced AI and machine learning to automate the triage, investigation, and remediation of cloud-native threats, empowering organizations to rapidly respond to threats across all their AWS environments.

Guidelines for selecting the most suitable CMS for your project.

MSSPs are under constant pressure to support more customers and increasingly complex environments while maintaining consistent response, coverage, and service quality. Traditional MDR models rely heavily on manual investigation, detection tuning, and analyst-driven workflows, making it difficult to scale operations and deliver proactive outcomes across tenants.

Security teams are overwhelmed by alert volume while real threats still slip through. Traditional SIEM and XDR platforms generate high-noise signals, and many AI SOC tools focus on faster triage without addressing detection gaps or true risk exposure.

From Reactive SOC to Preemptive Security Operatins

Cloud infrastructure today is the primary target for malicious actors. The risk of exposure of cloud assets continues to grow as organizations expand their cloud footprint and new cyberattacks targeting cloud infrastructure emerge.

This guide provides CISOs with a comprehensive understanding of how AI-driven automation can revolutionize cloud security operations, enhancing both efficiency and effectiveness.