Investors
Building the assurance layer for enterprise AI deployment.
AI is moving from passive assistance into systems that retrieve data, use tools, trigger workflows, support decisions, and connect to critical infrastructure. Mythos AI Security is building an evidence-first assurance layer to help organizations review these systems before trust expands.
Investor materials, roadmap context, and forward-looking information are available by request and reviewed before sharing.
Core Thesis
AI deployment risk is becoming an operating problem.
Enterprises are moving beyond simple chat interfaces. AI systems are being connected to company documents, customer records, engineering repositories, cloud platforms, data warehouses, APIs, tools, vendor systems, and decision workflows.
The question is no longer only whether the model can answer. The question is what the AI can access, what it can do, where requests go, whether it can be manipulated, and what evidence supports the next deployment decision.
Mythos is focused on AI deployment assurance: mapping access, testing behavior, producing evidence, guiding remediation, and supporting retest before deployment expands.
AI is becoming operational
AI is moving into workflows where it can retrieve, summarize, recommend, draft, route, and act.
Trust requires evidence
Buyers need proof of what was tested, what failed, what changed, and what remains blocked.
Assurance becomes recurring
AI systems change as models, prompts, data, tools, vendors, and permissions change. Review should not be a one-time event.
Why Now
The AI adoption curve is outpacing deployment assurance.
Organizations are adopting copilots, RAG assistants, agents, AI coding tools, decision-support systems, and cloud/data integrations quickly. Traditional security review remains important, but AI introduces additional deployment questions around retrieval, prompt injection, tool use, model routing, human approval, and evidence.
Traditional review asks
- Are known vulnerabilities present?
- Are secrets exposed?
- Are permissions configured correctly?
- Are logs available?
- Are controls mapped?
AI deployment assurance also asks
- What can the AI retrieve?
- What can the AI do?
- Can retrieved content manipulate the system?
- Does the AI respect user permissions?
- Where do prompts and outputs go?
- Which model/provider handles the request?
- Can humans verify the output?
- What evidence supports rollout?
Mythos is designed for this gap between AI adoption and deployment evidence.
Current Wedge
Enterprise AI deployment assurance.
Mythos’ current wedge is enterprise AI assurance for organizations deploying AI assistants, agents, copilots, RAG systems, vendor AI integrations, developer AI tools, regulated workflow assistants, decision-support systems, and cloud/data AI integrations.
This wedge is practical because enterprise teams already need to answer deployment questions before expanding AI access, autonomy, or production use.
Customer-facing AI
Support agents, customer workflows, and external-facing AI behavior.
Internal AI
RAG assistants, copilots, productivity tools, and employee knowledge systems.
Agentic AI
Tool-using systems that can call APIs, update records, send messages, or trigger workflows.
Regulated workflows
AI used around formal review, sensitive records, policy, compliance, or audit-heavy processes.
Developer and data platforms
AI connected to code, repositories, CI/CD, cloud systems, data platforms, and model routes.
Vendor AI integrations
Third-party AI products, SaaS AI features, model providers, and partner workflows.
Platform
Athena maps the system. Achilles tests the behavior.
Mythos is built around a product structure that separates exposure mapping from behavior validation.
Athena
Enterprise assessment and evidence engine
Athena helps map systems, access, data flows, model/provider routes, permissions, control gaps, findings, remediation guidance, and evidence.
Achilles
AI behavior and deployment validation engine
Achilles helps test prompts, RAG behavior, tool use, model routes, permissions, escalation, decision boundaries, adversarial inputs, release readiness, and retest behavior.
Minotaur (internal only): internal-only adversarial validation support. It is not a customer-facing product or a purchasable offering.
Strategic Horizon
A broader assurance category beyond the first wedge.
The immediate Mythos focus is enterprise AI deployment assurance. Over time, the same evidence-first category may expand into more complex assurance problems where AI touches physical systems, advanced computing, and high-assurance environments.
Project Hermes
AI Vehicle Assurance
Project Hermes is the strategic horizon direction for AI-driven vehicle, fleet, telematics, OTA/model update, sensor, perception, autonomy, and cyber-physical mobility assurance.
Read Hermes Use CaseQuantum-Adjacent AI
Quantum-Adjacent AI Integration
Quantum-adjacent AI is a strategic R&D scenario involving future-facing assurance considerations around post-quantum readiness, cryptographic inventory, vendor evidence, advanced simulation, optimization, research, and specialized compute workflows.
Read Quantum ScenarioBusiness Model
Assessment-led entry, recurring assurance potential.
Mythos can be positioned around scoped assessments that produce evidence, findings, remediation guidance, and retest requirements. Over time, recurring assurance may become important as AI systems change through new models, prompts, data sources, tools, vendors, and permissions.
Scoped assessments
Defined AI system, defined scope, controlled testing, findings, evidence, remediation, and executive readout.
Retest and release gates
After remediation, Mythos can help validate whether the issue changed before deployment expands.
Recurring assurance
As AI systems evolve, recurring reviews may support ongoing confidence across model updates, tool changes, data-source changes, and workflow expansion.
Investor Materials
Request the investor packet.
Investors can request materials covering the Mythos thesis, current product wedge, roadmap, use cases, strategic horizon, and planned commercialization path.
Request Investor Materials- Investor packet
- Product overview
- Use case library
- Roadmap overview
- Founder conversation
- Strategic partnership discussion
Disclosure
Planning-stage and forward-looking information.
Some investor-facing materials may include planning-stage, roadmap, or forward-looking information. Product direction, strategic horizon scenarios, partnerships, pricing, funding plans, and commercialization paths may change. Mythos should not be understood as claiming guaranteed outcomes, current public availability of strategic horizon products, or completed customer traction unless explicitly stated.
Interested in the Mythos thesis?
Request investor materials or start a conversation about the AI deployment assurance category, product roadmap, and strategic direction.