Mythos helps teams understand system exposure, evaluate AI behavior, and make release decisions with evidence before deployment.
Why Now
Enterprise AI now touches internal data, routes requests through outside providers, retrieves context, calls tools, and triggers workflows. A traditional security review looks at parts in isolation, it rarely shows how those pieces behave together at the moment of deployment.
What the AI can see, retrieve, infer, or expose across connected data and systems.
Where prompts, data, model calls, logs, tools, and fallback paths actually travel.
Which files, APIs, tools, approvals, or workflows the AI is able to trigger on its own.
Deployment Assurance
AI systems introduce a different review problem. A secure application can still have an AI workflow that retrieves the wrong data, follows hidden instructions, misuses tools, routes sensitive prompts to the wrong provider, or produces output that humans trust too easily. Mythos looks at the full deployment surface: access, behavior, evidence, remediation, and retest.
Traditional security review may ask
AI deployment assurance also asks
Mythos does not replace security review. It adds an AI deployment assurance layer around the systems, data, tools, and decisions that AI touches.
The Assurance Model
Deployment risk comes down to six questions. Athena and Achilles are built to answer each one with tested results, not assumptions.
Data, documents, metadata, permissions, and sensitive context available to the workflow.
Apps, gateways, model providers, logs, tools, regions, and fallback routes a request touches.
Tools, APIs, files, approvals, business actions, and workflow steps the AI can trigger.
Prompt injection, poisoned retrieval, goal hijacking, memory manipulation, or approval bypass.
Evidence for security teams, compliance reviewers, executives, auditors, and trust reviews.
Revalidation when models, prompts, tools, permissions, routes, or data sources change.
Data, documents, metadata, permissions, and sensitive context available to the workflow.
Apps, gateways, model providers, logs, tools, regions, and fallback routes a request touches.
Tools, APIs, files, approvals, business actions, and workflow steps the AI can trigger.
Prompt injection, poisoned retrieval, goal hijacking, memory manipulation, or approval bypass.
Evidence for security teams, compliance reviewers, executives, auditors, and trust reviews.
Revalidation when models, prompts, tools, permissions, routes, or data sources change.
The Forge
Athena validates the system foundation. Achilles tests the AI execution layer. Mythos connects both into one repeatable process that turns testing into structured evidence.
Offensive security and evidence engine
System risk & proof
Athena maps exposure, routes, permissions, findings, and proof so teams can understand the system risk beneath an AI workflow.
AI assurance and validation engine
AI behavior & boundaries
Achilles tests prompts, retrieval, agents, tools, approvals, permissions, routes, and lifecycle gates to determine whether AI behaves within defined boundaries.
The Assurance Path
Mythos starts from a defined, customer-controlled scope, tests the AI workflow and the system around it, records evidence, supports remediation review, and retests before any release decision is made.
Scope
Defined
Map
Exposure
Test
Behavior
Evidence
Recorded
Remediate
Review
Retest
Proof
Release Review
Readiness
Revalidate
Ongoing
Customer teams keep approval authority at every step. Mythos produces the evidence — it does not approve production or remediate on its own.
Evidence Pack
Mythos turns testing into reviewable outputs for security, compliance, remediation, and executive decisions.
An Evidence Pack supports customer review. It is not legal certification, compliance approval, or a guarantee.
A pack may include
Release Gate
Before an AI workflow moves forward, Mythos helps teams see what passed, what failed, what changed, and what still needs review.
Ready to proceed
Key controls passed within the defined scope.
Needs remediation
Findings require fixes before the workflow moves forward.
Requires revalidation
Changes in data, tools, routes, prompts, permissions, or models call for another review.
Mythos calls this a release-readiness review. It informs the customer's decision — it does not certify a system as safe.
Continuous Revalidation
Models, prompts, data sources, permissions, tools, and routes shift over time. Mythos is designed to support recurring validation so new risk does not stay hidden after launch.
Change-triggered review
Re-test when something that affects risk moves.
Recurring testing
Validation that runs on a defined cadence, not once.
Updated evidence
Fresh proof that reflects the system as it stands today.
Retest proof
A clear record of what was fixed and re-verified.
The platform roadmap includes continuous monitoring and change-risk alerting so recurring validation can run with less manual effort over time.
Deployment
Mythos is designed for scoped, authorization-first work with organizations that need validation, evidence, and clear control over sensitive AI systems.
Defined scope
Work is bounded to systems the customer names.
Customer authorization
Testing proceeds only with explicit approval.
Controlled test environments
Designed for isolated, auditable contexts.
Sensitive data handling
Built for organizations with strict data requirements.
Enterprise review process
Findings flow into your existing review and sign-off.
Private deployment paths
On-prem and isolated paths are part of the roadmap.
