Computers always involved some kind of credential from the time they were first available. But when they became connected through networking, each improvement brought greater threats. The field of information security today can be traced back to the first companies that made personal information accessible through online communications. The World Wide Web specifically made technology available to the world. Some of the first companies to use the web for employee resources were Fidelity Investments and Wells Fargo. These companies offered human resources-related applications. It was at these companies that many of the best practices used today were first created. These companies created policies and procedures that were used as templates by the majority of companies operating on the web.
These were paper-based documents. Policies and procedures are typically reviewed every year for updates. These were distributed to staff and frequently printed. At that time, it was common to have a printer on each desk or at least in the vicinity of a person’s desk. Documents were formatted for paper.
Since that time, the web has evolved into what it is today. At the same time, information security tools have been created to deal with modern threats. Many of the original policies and procedures remained the same. Employees may receive an email about a change and an online location where they can read the document. But there is one critical document for which this model continues to create a weakness.
An information security group has two primary purposes. It makes sure the company is following best practices in regard to protecting company assets from threats. When a threat has penetrated this protection, it is called an incident. The document that outlines what events should occur during an incident is called an incident response plan (IRP). The IRP is basically a to-do list executed from top to bottom. Companies are audited to verify that they have a working incident response plan. This document has become more of an asset designed to pass an audit than a practical document to follow.
Where the IRP differs from other documents is that it is updated frequently. The location of that document is on a network server. Studies have shown that the availability of the incident response plan is a factor that determines the success of the incident being handled.
In reality, when most incidents are declared, staff members do not have a printed copy of that document. Infrequently, if they do, it is not the current version. It’s also possible that the incident has affected the server the document is stored on. Due to the wastefulness of printing documents, printers are not readily accessible to all staff members. It’s very common for incident response teams to begin handling the incident without a plan in front of them.
A solution to this has never been successfully implemented. Even today, it’s safe to assume all staff members have a mobile device. Companies have investigated using mobile devices as a way for staff to always carry their incident response plans. This does not work for multiple reasons. One reason is that an incident response plan is a classified corporate document that, in theory, should not be on a personally owned device. But most importantly, the format used by corporations is the PDF file. A PDF file creates its pages to fit a letter-size piece of paper. When viewed from a mobile device as an IRP, it is unusable.
The application that solves this problem is called Reflex. The Reflex project started out to solve the problem described above. But its implementation required the invention of several technologies. Then, once implemented, the solution presented an opportunity to take Reflex far beyond its ability to deliver paper documents to mobile devices.
Conventional client-server software architectures, which rely on direct connections to discoverable endpoints and fixed IP addresses, present unacceptable security risks in this context. Such systems are inherently vulnerable to denial-of-service (DoS) attacks and sophisticated network surveillance, which could disrupt or compromise the entire response effort. A truly robust solution must therefore be architected to function securely and reliably even in a hostile network environment where the communication channels themselves may be under active attack by determined adversaries.
In view of these multifaceted challenges, a review among the assembled subject-matter experts confirmed a notable absence in the state of the art of any existing technology or integrated system capable of solving this complex coordination problem. Despite the clear and present need articulated within this expert forum, the field lacked a comprehensive solution that was mobile-native, secure against advanced threats, and resilient to infrastructure failure.
A second, foundational aspect of Reflex addresses a distinct and equally critical problem, not in operational response, but in the field of artificial intelligence itself. The principles behind the present Reflex’s intelligence engine are rooted in a unique paradigm first developed by the inventor decades prior. This earlier system, one of the first commercially available AIs, operated on a different principle from modern, large-model-based systems. Instead of being trained on vast, impersonal datasets, it functioned as a “real intelligence engine” by creating a high-fidelity model based on the explicitly defined preferences, logic, and priorities of a single human user. It did not attempt to be a generalized intelligence; rather, its sole purpose was to emulate the decision-making process of its designated orchestrator.
This proven, user-centric AI paradigm was adapted for the present Reflex to solve an immediate need: to automate and guide an incident response according to the specific, pre-configured intent of the organization’s security leader. The system’s “Reflex” engine, therefore, does not pretend to be an independent intelligence. It is designed to act as a perfect digital proxy for the person who configured it, executing their plan with speed and precision.
Concurrent with solving this operational challenge, the Reflex was conceived with a forward-looking objective: to create a system that would serve as the indispensable bridge between real-world human action and the advanced, conversational AIs that were anticipated to one day exist. It was recognized that modern AI, for all its power, suffers from a fundamental limitation: it has no access to structured, ground-truth data about human performance, especially in high-stakes, confidential environments like cybersecurity. Its knowledge is derived from public texts, not from observing real people solving real problems.
Therefore, the system’s architecture was intentionally designed from its inception to function as a data-generation engine. It was built to meticulously capture and structure human performance data, correlating standardized, quantified skills with objective performance metrics. This was done in anticipation of a future where this unique and priceless dataset could be fed to an advanced AI, allowing it, for the first time, to make meaningful analyses and judgments about human operational capability. This anticipatory design, creating a purpose-built data connection to a future technology, is a unique aspect of Reflex.
Furthermore, the operational environment of modern enterprises involves a complex ecosystem of third-party monitoring tools, security products, and managed service providers. A significant challenge in this environment is the “last mile” of incident response. While these third-party systems are adept at detecting anomalies and generating alerts, they traditionally lack a secure and efficient mechanism to directly initiate a structured response within their customer’s organization. The conventional methods, requiring manual intervention by a human operator or complex, high-maintenance API integrations, introduce critical delays and create security vulnerabilities. This highlights a need for a universal, low-friction method for trusted third-party systems to securely and automatically trigger a pre-defined operational response plan.
An additional challenge arises when an incident involves specialized, proprietary equipment or software. The standard support model requires a designated employee to act as a single point of contact, relaying information between the internal response team and the external vendor’s technical support. This linear, indirect communication is inherently inefficient, prone to misinterpretation, and slow. Often, the vendor’s expert lacks the real-time, multi-faceted context of the live incident, and the internal team lacks the deep product-specific expertise. This creates a need for a new paradigm of collaborative diagnostics, where an external expert can be virtually and securely embedded into the live response effort, able to communicate directly with the hands-on team and view real-time data without needing physical access or system credentials.
Finally, with the advent of large language models (LLMs) and advanced AI, a new possibility emerges, along with a new problem. While these AIs possess immense analytical capability, they are disconnected from the live, unstructured human observations that occur during an ongoing incident. There is no existing mechanism to feed the real-time, on-the-ground “color commentary” from human responders to an AI for immediate analysis. Concurrently, there is no system capable of detecting the faint, cross-organizational signals of a nascent, widespread incident before it is publicly known. The siloed nature of incident data prevents the identification of coordinated, multi-victim attack campaigns in their earliest stages, leaving each organization to face the threat alone. This reveals a need for a system that can both connect live human observation to AI for analysis and aggregate anonymized data to serve as a global early-warning system.