The organizations that stand to benefit most from AI are often the ones with the highest security requirements, the most complex compliance landscapes, and the least tolerance for failure. A hospital cannot afford an AI system that can be manipulated into giving wrong medical guidance. A bank cannot deploy fraud detection that is itself vulnerable to adversarial attacks. A government agency cannot run citizen services on infrastructure it does not fully control.
These are the organizations we are building for. Not because they are the easiest customers, but because the constraints they impose produce better technology — systems that are safer, more reliable, and more trustworthy for everyone.
Enterprise
Large organizations adopting AI face a paradox: the technology that promises to transform their operations also introduces a new category of risk that their existing security infrastructure was not designed to handle. Prompt injection, model manipulation, and adversarial attacks do not appear in traditional threat models. SIEM tools do not detect them. SOC teams are not trained to respond to them.
We are designing every product with enterprise deployment requirements built in from the start — not retrofitted after the fact. That means isolated environments where no customer's data touches another's. Single sign-on that integrates with existing identity providers. Audit logging that captures every action, every query, every decision. Granular access controls that map to the roles and responsibilities organizations already have. And data processing agreements that reflect the reality of how AI handles information, not boilerplate written for a pre-AI world.
When our products reach enterprise availability, they will not need a "hardening phase." They will be ready because security and compliance were the first requirements, not the last.
Financial services
Financial institutions are among the most sophisticated adopters of AI — and among the most exposed to its risks. Models that assess creditworthiness, detect fraud, price assets, and interact with customers are increasingly central to operations. Every one of those models is a target. An adversarial input that manipulates a fraud detection system can authorize transactions that should be blocked. A jailbreak against a customer-facing AI can expose confidential account information or provide misleading financial guidance.
The regulatory landscape compounds the challenge. Basel III, PCI DSS, SOX, MiFID II, and an expanding body of AI-specific regulation demand not just that systems work, but that organizations can demonstrate how they work, why they made specific decisions, and what safeguards are in place against manipulation. Our approach — auditable defense at the architecture level, not opaque filters — is designed to produce systems that regulators can inspect and institutions can stand behind.
Healthcare
When AI assists in clinical decisions, the consequences of adversarial manipulation are not measured in dollars — they are measured in patient outcomes. A model that helps interpret diagnostic imaging, recommend treatment protocols, or triage patient inquiries must be protected with the same rigor applied to the medical devices and pharmaceutical products that undergo years of validation before reaching patients.
Healthcare also presents unique data challenges. HIPAA in the United States, GDPR in Europe, and comparable frameworks globally impose strict requirements on how patient data is collected, processed, and stored. AI systems that process this data must do so within environments that maintain compliance at every layer — from the infrastructure they run on to the models they use to the logs they generate. Our platform is designed to meet these requirements as a fundamental property of the architecture, not as a compliance overlay.
Government
Government agencies operate at a scale and with a responsibility that few private organizations match. The AI systems they deploy touch citizen services, national security, infrastructure management, and public safety. The infrastructure supporting those systems must meet standards for data sovereignty that commercial cloud configurations do not satisfy by default — zero public IP addresses, sovereign data residency, air-gapped environments where required, and access controls that enforce the principle of least privilege at every layer.
We build with these requirements in mind because we believe public sector AI should set the standard for safety and accountability, not follow it. The same zero-trust architecture, isolated project environments, and comprehensive audit logging that we build for enterprise customers will be available for government deployment — with additional controls for classified environments and FedRAMP-equivalent compliance frameworks as our platform matures.
Education
Educational institutions are integrating AI into teaching, administration, research, and student services at a pace that exceeds their ability to evaluate the security implications. AI tutoring systems interact with minors. Administrative AI processes sensitive student records. Research AI handles proprietary data. Each of these use cases carries distinct privacy obligations — FERPA, COPPA, state student privacy laws — and each represents an attack surface that traditional IT security tools were not designed to protect.
We are particularly focused on ensuring that our platform supports age-appropriate use, strong data minimization, and transparent processing. When AI interacts with students, there must be no ambiguity about what data is collected, how it is used, and who can access it. These are not features we plan to add — they are constraints we build around.
Security
Organizations in the security industry — managed security service providers, SOC operators, penetration testing firms, threat intelligence companies, defense contractors — have requirements that differ fundamentally from other sectors. They do not just need secure tools. They need tools built by people who think adversarially, who understand that every system will be tested by people whose job is to break things, and who design accordingly.
Our team builds products for this audience because we come from this audience. The same adversarial research that informs Prion's defense layer informs how we think about the security of our own infrastructure. Every API endpoint, every data pipeline, every authentication flow is designed with the assumption that someone is trying to exploit it. We build tools for people who hold their vendors to the same standard they hold themselves.
Startups
Early-stage companies building on AI face an impossible choice: move fast and accept security debt, or invest in security infrastructure and slow down product development. Neither option is acceptable. Security debt compounds. But so does competitive pressure.
As our platform matures, we intend to offer programs that give startups access to the same AI infrastructure that enterprise customers use — at a scale and price appropriate to their stage. The goal is to make enterprise-grade security a default, not a luxury. A three-person startup deploying its first AI feature should have the same protection against adversarial manipulation as a Fortune 500 company. The technology to make that possible is what we are building.
Working with us
We are in the early stages of building our platform and are not yet accepting general customers in any sector. If your organization operates in one of these industries and you are interested in working with us as we develop our products — whether as an early design partner, an advisor on industry-specific requirements, or a future customer — we would like to hear from you.