Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Detection engineering is fundamentally a translation problem: rules need to be converted between formats, IOCs need to be converted into detection logic, and noisy alerts need to be converted into precise suppressions. That translation work is what consumes analyst time, and it's what Claude Code handles well.

A credential access event fired. An AI agent investigated it, correlated it against running processes, assessed the risk, and closed the ticket. No analyst touched it. The entire loop ran in minutes. This is what security operations look like when AI can actually operate in the environment rather than advise from outside it. Security operations have always required a special kind of person.

Every new customer shouldn't cost you headcount. But with stack diversity, it usually does.

Revisiting a conversation between LimaCharlie co-founder Christopher Luft and Chris Cochran, Field CISO & Vice President of AI Security at SANS Institute, on The Cybersecurity Defenders Podcast. For most of cybersecurity’s history, defenders could operate under a safe assumption: somewhere on the other end of an attack, a human was making decisions. Scripts might automate parts of the kill chain, tools might accelerate execution, but a person was in the loop.

Every MSSP is fielding the same question from clients right now:"Are we safe with AI?" Most are answering with some version of"yes, we're logging everything." In a recent Defender Fridays episode, Saurabh Shintre, Founder and CEO of Realm Labs drew a hard line between these two concepts."You can log prompt and response and this bare minimum you have to do.

Security teams enter an asymmetric battle when adversaries freely use AI to wage attacks. The aggressors are armed with top-tier capabilities. Defenders hesitate to adopt AI they can't see, trust, or control. SecOps teams are drowning in alerts and outpaced by adversaries who are unafraid to automate everything. The solution isn't another dashboard or another AI chatbot offering recommendations.

Most of the work covered in our agentic security operations content assumes one thing: Claude Code is already connected to your LimaCharlie environment. If you haven't done that yet, this is your starting point.

When nation states target cloud infrastructure, MSSPs are at risk. Many security teams have quietly accepted this as someone else's problem. It isn't, and ignoring the problem only increases their risk exposure. A recent episode of the Cybersecurity Defenders Podcast featured a conversation on cloud infrastructure vulnerability between LimaCharlie Co-Founder Christopher Luft and Prophet Security R&D Guru, Matt Bromiley.

A CVE surfaces in the morning. By the time you are talking to that customer, you can tell them: we saw it, we checked your environment, you were not affected, and we deployed a rule that will catch it if it ever shows up. For MSSPs and MDR providers, detection engineering is among the most valuable services you can offer. It is also among the most expensive to deliver consistently and at scale.

Picture a SOC that investigates its own alerts, hunts threats across customer tenants, isolates compromised endpoints, and writes its own detection rules. Envision the same SOC attacking itself every morning to find the gaps it missed, all before your analysts arrive for the day. This is not a roadmap item, but an operational reality on LimaCharlie. It’s what agentic AI security looks like on a platform built to support it.