Use Cases
Explore the deployment scenarios where AI risk appears — from customer agents and copilots to cloud integrations, decision support, mission workflows, AI vehicles, and future-facing R&D.
Illustrative scenarios. Practical review points. Evidence-first deployment decisions.
Why Use Cases Matter
Every AI deployment has a different risk surface. A support agent can expose customer records. A RAG assistant can retrieve the wrong documents. A tool-using agent can exceed permissions. A cloud-connected AI system can create new data paths. Mythos helps teams examine the specific workflow before trust is expanded.
Understand what the AI can access, where requests go, which systems are connected, and what data becomes reachable.
Validate how the AI responds under normal, edge-case, adversarial, and permission-boundary conditions.
Receive findings, proof, remediation guidance, and retest material that supports deployment decisions.
Use Case Library
Filter by deployment type to see concrete scenarios. What the system does, where it can go wrong, which Mythos project applies, and what an assessment produces. Open any scenario to review the detail.
Illustrative scenarios, evidence-first review
These use cases are illustrative examples of AI deployment patterns Mythos is designed to help review. They are not claims of completed customer work, guaranteed outcomes, compliance certification, or automatic approval. Each assessment should be authorized, scoped, non-destructive, and controlled by the customer.
Showing 12 of 12 scenarios
A customer support AI agent answers questions, drafts replies, summarizes tickets, and guides customers through common issues.
Risk: Can expose customer data, hallucinate policy, mishandle escalation, or follow malicious ticket content.
An internal assistant searches company documents, policies, wikis, tickets, and knowledge bases.
Risk: Can retrieve restricted documents, trust hidden instructions, cite stale policy, or leak sensitive information.
A tool-using AI agent calls APIs, updates records, sends messages, creates tickets, or triggers workflows.
Risk: Can exceed permissions, bypass approval, misuse tools, chain actions, or create changes the business cannot explain.
An AI assistant supports claims, finance, healthcare-adjacent, legal, compliance, procurement, or audit workflows.
Risk: Can influence decisions, mishandle sensitive records, use stale policy, or create weak audit trails.
A company connects a third-party AI product, SaaS feature, chatbot, model API, or partner workflow.
Risk: Can create new data-sharing paths, overbroad permissions, unclear retention, weak audit logs, or vendor evidence gaps.
A copilot helps employees and executives summarize meetings, draft emails, search files, and prepare work.
Risk: Can expose overshared files, sensitive meetings, internal drafts, stale policies, or confidential executive context.
A developer AI assistant writes code, generates tests, explains repositories, reviews pull requests, and supports engineering work.
Risk: Can introduce vulnerabilities, weak tests, unsafe dependencies, misleading PR summaries, or agentic code changes.
An AI system scores, classifies, prioritizes, recommends, or routes cases before a human decision.
Risk: Can become the practical decision-maker, hide uncertainty, treat similar cases inconsistently, or trigger unsupported actions.
AI supports public-sector, civic, defense-adjacent, emergency, or mission-sensitive workflows.
Risk: Can expose restricted records, trust public-input attacks, weaken review, or create official-looking unsupported outputs.
AI connects to cloud systems, warehouses, lakehouses, catalogs, dashboards, logs, vector indexes, and model gateways.
Risk: Can bypass permissions, expose sensitive rows or columns, trust poisoned metadata, route data through unapproved models, or export restricted information.
AI-driven vehicles, autonomous fleets, telematics, OTA / model updates, sensors, perception, and cyber-physical mobility systems.
Risk: Can involve sensor disagreement, unsafe planning, model regressions, OTA risk, fleet portal abuse, or weak incident evidence.
AI supports post-quantum readiness, cryptographic inventory review, vendor quantum-safe evidence review, advanced simulation, or future specialized compute workflows.
Risk: Can overstate quantum-safe claims, mishandle cryptographic inventory, confuse algorithms, trust vendor hype, or recommend unsafe migration steps.
Assessment Outputs
Mythos assessments are built to produce decision material, not vague AI opinions. Each engagement is designed to help security, product, engineering, compliance, leadership, and operations teams understand what was tested, what was found, what needs to change, and what evidence supports the next deployment decision.
A leadership-ready summary of the reviewed AI system, readiness posture, major blockers, business impact, and recommended deployment path.
Useful for Leadership, product owners, security leaders, legal/compliance, and risk owners.
A detailed technical record of what was observed, why it matters, how severe it is, and what needs to be fixed.
Useful for Engineering, security, AI platform teams, data teams, and technical owners.
A structured collection of proof material showing what was tested, what happened, and what supports each finding.
Useful for Security review, audit, compliance, incident review, procurement, and internal approval.
A visual and structured map of what the AI can access, where requests go, what tools it can use, and which controls apply.
Useful for Security architecture, AI governance, data governance, compliance, and platform teams.
A prioritized list of fixes tied directly to findings, owners, acceptance criteria, and retest expectations.
Useful for Engineering managers, security teams, product owners, and implementation teams.
Evidence that remediation actually changed the system behavior before deployment expands.
Useful for Release gates, security signoff, compliance review, product leadership, and operational approval.
Mythos connects technical evidence to deployment decisions. A finding is not the end of the process. Each issue connects to a remediation path, a retest requirement, and a clear recommendation about whether the AI system should remain in sandbox, enter pilot, expand access, or stay blocked.
Finding → Impact → Remediation → Retest → Decision
The assessment identifies a specific behavior, exposure, control gap, or evidence weakness.
The finding is translated into business, security, operational, compliance, or deployment risk.
The customer receives practical guidance and acceptance criteria for fixing the issue.
The corrected system is tested again to confirm whether the issue is closed.
The updated evidence supports a release, pilot, restriction, delay, or block recommendation.
Want to see what a finished deliverable looks like? Walk through an illustrative 12-page assurance evidence pack: fictional data, for demonstration only.
View Example ReportExample Output
A Mythos report should make the next decision easier. The customer should be able to see what was tested, what failed, what changed, and what still requires approval.
Assessment Snapshot
Engagement Path
A Mythos assessment begins with a defined AI system, a controlled scope, and a clear deployment question. The goal is to understand what is being trusted before trust expands.
Select the AI workflow, assistant, agent, model, copilot, integration, or vehicle/strategic-horizon scenario that needs review.
Customer provides
Confirm what systems, data sources, users, tools, environments, and limits are included before testing begins.
Scope includes
Mythos maps exposure, tests behavior, reviews controls, identifies weaknesses, and gathers evidence under the approved scope.
Assessment may include
The customer receives findings, remediation guidance, retest requirements, and a deployment recommendation.
Possible outcomes
Mythos testing should be authorized, scoped, non-destructive, and controlled by the customer. Mythos produces evidence and recommendations — the customer keeps every decision.
For teams preparing to launch an AI assistant, agent, copilot, integration, or workflow.
Best for Before pilot, before production, before expanding access.
For teams that already have an AI system in staging or limited use and need evidence before broader rollout.
Best for Controlled pilot, limited beta, department rollout.
For teams connecting third-party AI products, SaaS AI features, model APIs, or partner workflows.
Best for Procurement, SaaS AI enablement, model provider review, vendor risk.
For teams that have already fixed findings and need proof before deployment expands.
Best for After remediation, before release, before executive signoff.
Start the Assessment
Tell Mythos what you are building, connecting, or preparing to release. We will help identify the right assessment path.
