Building SecOps that improve with every frontier AI release

LimaCharlie CEO Maxime Lamothe-Brassard made an observation after the RSA conference that security vendors don't typically say out loud: "The frontier models are just better than anything people roll their own. There's no secret sauce these vendors are offering that is better than the latest frontier model release."

That's a pointed claim that carries a significant implication buyers may not have fully considered. 

No security vendor has the resources to outpace the frontier labs on raw model capability, and most aren't trying to. What they're selling instead is the wrapper: proprietary orchestration, closed integrations, and narrow workflows built around specific use cases. The problem is that wrapper adds cost and latency without adding intelligence and it puts a vendor's release cycle between your operations and every model improvement that ships.

Security teams using vendor-locked platforms don't automatically benefit when a frontier model gets smarter. They benefit when the vendor decides to update their wrapper, if they decide to update it at all.

Innovative gains compound quickly on open architecture

LimaCharlie created an API-first open integration platform called the Agentic SecOps Workspace (ASW). Rather than building a proprietary harness around frontier models, our platform is designed to get out of the way. 

Our documentation is open-source and written in Markdown, a format that LLMs process well. Every action available to a human operator is available to an AI agent through the same CLI and API surface. There is no separate AI execution layer. Agents operate on the platform the same way a human does.

Our approach to AI will look intuitive to MSSPs, SOCs, and SecOps teams who are familiar with DevOps workflows. LimaCharlie offers infrastructure-as-code, and AI is just another integrated component in our model.  

Since we have no harness or proprietary wrapper around AI, your agentic operators improve in step with the models behind them. If you're running Claude Code in the ASW and Anthropic ships a more capable model next quarter, your SOC simply improves. No tooling changes. No additional platform spend. Anthropic did the work. You received the benefit.

Multiply that effect across three or four frontier labs competing to ship better models on an accelerating schedule, and the compounding benefit becomes substantial. Every major model release from any of the frontier providers is, in effect, a silent upgrade to your security operations.

Why AI adoption strategy matters now

MSSPs have always operated on thin margins, and that's not changing. Hiring is expensive and often slow. Scaling coverage across dozens or hundreds of tenants without adding headcount at the same rate is the challenge every provider is trying to solve.

Vendor-locked AI SOC platforms attempt to address this, but the pricing structure often creates a different problem. Every frontier AI provider is currently operating at a loss on inference, pricing below cost to drive adoption. When the market corrects, inference costs will rise across the board. Operators on closed platforms have no ability to respond. They can't swap to a cheaper model, negotiate directly, or optimize token usage. They absorb whatever increase their vendor passes through.

On an open-API platform like LimaCharlie's Agentic SecOps Workspace, you bring your own model credentials and pay raw vendor rates. If one frontier provider raises prices, you can shift workloads to a more cost-effective model without changing your platform or workflows. LimaCharlie doesn't mark up inference costs, and because agent definitions are infrastructure-as-code, a workflow built for one client can be deployed across thousands with a single command.

Getting started doesn't require a clean-slate migration

One practical concern MSSPs raise about agentic AI is how to begin when they are already running complex security stacks. We recently launched Grid, which is designed to integrate with existing tooling rather than replace it wholesale. 

Because the platform is API-first and unopinionated about integrations, AI agents can operate alongside the tools already in your stack. You don't need to rip out your existing EDR or SIEM to start running agentic workflows. You extend AI operations to what you have, run agents where they add value, and build from there.

The entry point is low. The ceiling, as frontier AI models continue to improve, has no visible limit.

Try the Agentic SecOps Workspace free: https://app.limacharlie.io/signupLearn more about Grid: https://limacharlie.io/grid

Check out the LimaCharlie AI GitHub repo: https://github.com/refractionPOINT/lc-ai