We did not set out to build a security company.

Two things happened at once.

The first was inside our own work. Building AI-based operating systems and automation platforms for clients meant putting generative models, autonomous agents and AI-driven workflows into production environments that mattered. Every time we asked how the security of one of those systems would be evaluated, the answer came back thin. Policies existed. Governance frameworks existed. Nobody could show us evidence.

The second was in the market. Between August 2024 and June 2026, the independent AI-security firms were bought one after another by the large platform vendors. Cisco, Palo Alto Networks, Check Point, CrowdStrike, SentinelOne, F5, Zscaler. Good outcomes for those founders. But the practical effect for a buyer running three clouds and five models was that AI security increasingly arrived attached to a reason to consolidate everything else.

What started as an internal capability to protect our own clients turned out to be worth more than the consulting work it came from.

Then we looked at how everyone was doing it.

Almost every product in the category used a language model to judge whether a request was an attack. The flaw is visible from a long way off. If a clever prompt can jailbreak a frontier model, it can jailbreak the smaller model you placed in front of it. You have not removed the vulnerability. You have duplicated it and added latency.

That intuition is now well documented. Researchers at Lancaster University and Mindgard reported evasion success of up to 100% against six production protection systems, including Microsoft Azure Prompt Shield and Meta Prompt Guard. A 2026 study from TU Munich and Mila found LLM-judge performance degrading to near random chance under red-teaming distribution shift, across 6,642 human-verified labels. A review of 78 studies found attack success rates above 85% against agentic coding assistants when adaptive strategies were used, with most defenses achieving under 50% mitigation.

Three doctrines.

• Deterministic security comes before probabilistic intelligence. You cannot jailbreak a regular expression. Math does not have bad days. Every decision the system makes traces back to a rule a human can read, in a log, with an identifier.
• Security without continuous adversarial validation is faith. An annual penetration test describes the week it ran. Attack techniques change monthly, so validation happens monthly, against the live deployment.
• AI security is a new discipline, not an extension of cybersecurity. Your existing stack is not deficient. It is aimed at a different problem. Nothing in a WAF was ever designed to read a paragraph and decide whether it is trying to manipulate a model.

What we will not do.

• ✕Put a language model in the enforcement path and call it AI-securityNot available yet
• ✕Write real-time security in Python, where interpreter overhead and garbage collection cost you the latency budget you needed for adoptionNot available yet
• ✕Lock you into one cloud, one firewall or one model provider to get AI securityNot available yet
• ✕Claim 100% protection, or unhackable, or any of the words that tell a technical buyer to stop readingNot available yet
• ✕Ask for six figures before you are allowed to see what the product does to your own systemNot available yet
• ✕Let an AI component author or enforce a live security policy, everNot available yet

On the Rust decision, since engineers ask.

We are careful about this argument because a version of it circulating in our industry is wrong. Python is a memory-safe language. The memory-safety research from CISA, NSA, Microsoft and Google compares C and C++ against memory-safe languages, and Python is on the safe side of that line. Anyone telling you to pick Rust over Python for memory safety has not read the source material.

The real argument is latency and predictability. A security control on the request path pays its cost on every single request, forever. Rust gives us compiled performance, no garbage collector pausing mid-request and no global interpreter lock, which is what makes a sub-20 millisecond tail-latency target achievable rather than aspirational. That is a narrower claim than the one you will hear elsewhere, and it is the one we can support.

Where we actually are.

True as of August 2026

• v6.0 released May 2026, after five development waves of progressive tightening
• 16 Rust crates, roughly 205,000 lines of code
• 1,237 automated tests, 160 mapped to specific MITRE ATLAS technique IDs
• Paying customers. Multiple live pilots - some with global brands.
• Engineered and operated in the United States, no foreign cloud dependency

What we are working on

• Converting a pilot into the first paid enterprise contract
• Completing ISO/IEC 42001 certification
• Kubernetes and edge deployment modes
• Closing a pre-seed round to fund that roadmap
• Publishing named customer results, with permission, as soon as there are any

If that last list reads as less polished than a vendor page usually does, that is deliberate. The entire argument of this company is that you should demand evidence instead of assurances. It would be strange to make that argument and then ask you to take our own status on trust.

Do not take our word for it. Run it against your own traffic.

Monitor mode goes in non-blocking, in minutes, with zero risk to live workflows. Within days you get a written audit of the prompt injections, extraction attempts and data leaks your current stack is not catching. Then you decide.

The audit is free. The blind spot is not.