Reflex is an information security platform. It was conceived by some of the founders of the information security profession. The Reflex platform was developed using technology from CISOware’s parent company, GRAYBELT innovations. CISOware Corporation owns and manages the Reflex platform.

Reflex was designed for situation response. It solves a critical problem: organizations often have a plan as a paper document—but that paper isn’t at hand or on the server when it’s needed. Reflex converts that static, paper-formatted plan into a custom mobile application, delivered instantly to every responder’s mobile device. The mobile application is not just a formatted piece of paper. It takes advantage of the advanced computing abilities of a mobile device

In cybersecurity, this is called incident response. Reflex goes further: it monitors progress, flags irregularities, coordinates across all devices running the plan, and records each responder’s actions, skills, and other relevant data. Afterward, that data is packaged and stored for use in training the AIs popular today. It can teach them to understand situation response—a bridge between human action and AI.

The technology used to build Reflex, is my life’s work distilled into a massive software system. After I launched the first commercial AI product, and was stomped into the ground by Microsoft, I did not quit. I spent 20 years thinking about intelligence-related systems before designing Reflex, and more than 10 years building it. Hal (the name of my AI) is not all-knowing like Claude and ChatGPT. But it knows something that they don’t know. It knows how to collect data about real human actions and converted into a format it can understand and analyze. How can also work with you while you are handling an incident and understand the events that are occurring. And how doesn’t require billions of dollars to learn.

Hal doesn’t have models. Hal is hardcoded knowledge with of some founders of the information security profession. But the actions that Hal takes are guided by the customer who configures Reflex. His goal is not to push you around ( and you’ll know what I mean if you use ChatGPT 5). his goal is to mimic the way the orchestrator thinks. We did not just jump on the AI bandwagon. We built the wheels and the rest of the bandwagon, Many years before large language models were introduced.

It was never my intent to market Reflex as an AI product. Reflex is a massive leap forward in how we handle information security and incident response.

In every major cybersecurity standard, the final step of incident response is called “Lessons Learned.” Reflex was built with this in mind from day one. Every decision, every action, every timestamp is recorded. And this archiving feeds a follow-up product I developed, called PainPoint. And as it happens, it is exactly what the current large language model AI needs to generate new data. It will likely be years before anyone starting out today could accomplish this task.

PainPoint uses Reflex’s internal Real Intelligence (RI) engine to compare past responses and spot where things broke down. If something went faster, or slower, or failed outright — it can suggest why. It’s not abstract. It’s grounded in real, timestamped, forensic data.

Reflex doesn’t require external AI. It stands on its own as a fully functional, stand-alone platform. However, when an incident is in progress, the orchestrator can send observations to a popular AI. The results are less than stellar — right out of a textbook, which is where all the AIs get their information. It is scraped from someplace. If Reflex data were incorporated into an LLM, it would introduce all kinds of possibilities.

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