AI Risk by Environment
Different AI deployments.
Evolving risks.
Continuous need for proof.
Mythos examines AI where it actually runs: customer facing surfaces, regulated records, and approval paths each buyer environment puts at stake.
Find Your Starting Point- Customer-facing AI
- Copilots and agents your customers and prospects interact with directly.
- Regulated records
- AI inside finance, healthcare, and audited records.
- Mission-sensitive systems
- Sensitive environments where scope and approval come first.
AI risk changes with the environment around it.
The same AI feature can create different risks depending on the data it touches, the users it serves, the tools it can call, and the decisions it supports.
Hidden access
AI may retrieve documents, metadata, logs, permissions, or user context teams did not intend to expose.
Action paths
Agents and copilots may call tools, move data, trigger actions, or bypass expected approval steps.
Review gaps
Security, compliance, and leadership teams need findings they can evaluate, not vague scanner output.
Change drift
Prompts, models, tools, permissions, and routes can change after launch.
By Buyer Environment
Find the lens that matches your environment.
Each environment puts different data, users, tools, and approval paths at stake. Find the one closest to yours to see what Mythos examines and what your teams get back.
AI-heavy SaaS
- Environment risk
- Customer security teams want confidence that customer-facing agents and copilots will not cross tenant boundaries, misuse tools, or behave unpredictably.
- What Mythos examines
- Tenant boundaries, RAG leakage, agent tool use, prompt injection exposure, and the actions a customer security review will ask about.
- What your team receives
- Boundary test results, reproduction context, finding summaries, retest notes, and a customer-review packet.
Finance and Insurance
- Environment risk
- AI assistants can touch customer records, policy documents, claims systems, and the approval paths behind regulated decisions.
- What Mythos examines
- Data exposure, retrieval paths, permission boundaries, approval bypass risk, and logging exposure across sensitive routes.
- What your team receives
- Data exposure findings, route review, permission test results, remediation notes, and control mapping.
Healthcare and Life Sciences
- Environment risk
- AI may touch protected health information, research data, clinical notes, lab systems, or sensitive operational records.
- What Mythos examines
- Context exposure, retrieval leakage, role boundaries, data handling, and the AI-assisted record paths privacy teams review.
- What your team receives
- Exposure findings, data-flow notes, test results, remediation guidance, and review-ready reporting.
Regulated Enterprise
- Environment risk
- Large organizations need confidence that AI controls hold across cloud, identity, data, applications, and regulated records.
- What Mythos examines
- System exposure, identity paths, cloud configuration, AI behavior, tool boundaries, and the findings behind each one.
- What your team receives
- A compliance review pack with finding summaries, control mapping, remediation tracking, and retest status.
Defense-Adjacent and Mission-Sensitive Teams
- Environment risk
- Sensitive AI workflows require defined scope, authorization-first testing, human review, and careful handling of findings.
- What Mythos examines
- Access boundaries, route control, agent behavior, data handling, and the approval paths a mission-sensitive review depends on.
- What your team receives
- Custom finding packages, control mapping, retest notes, executive summaries, and reporting for mission-sensitive review.
Systems Integrators and Consultancies
- Environment risk
- Partners delivering AI for clients need repeatable checks before client handoff, procurement review, or production launch.
- What Mythos examines
- Client AI systems, RAG pipelines, agent tool use, route boundaries, and what a client review will expect.
- What your team receives
- Partner-ready findings, assessment outputs, remediation tracking, client handoff documentation, and repeatable test artifacts.
Start with a scoped assessment.
Assessments give teams a focused way to examine one AI surface, agent, data path, or buyer environment before expanding into an ongoing engagement.
Assessment
AI Readiness Assessment
For teams preparing to launch or expand customer-facing AI that want exposure, behavior, and finding gaps surfaced first.
Assessment
Agent Safety Assessment
For copilots, agents, and tool-using systems; focused on action boundaries and tool behavior under realistic conditions.
Assessment
AI Data Exposure Assessment
For teams concerned about sensitive context, retrieval, and the access paths an AI system can reach.
Assessment
AI Compliance Evidence Package
For organizations that need reviewable findings, control mapping, and retest records the compliance team can use.
Assessment
Custom High-Risk AI Assessment
For sensitive, regulated, or mission-critical environments with unique constraints and custom reporting needs.
One model, adapted to each environment.
Every engagement starts with scope. Mythos maps the environment, tests the paths that matter, records findings, supports remediation review, and retests when changes occur.
Scope
Define the environment, data paths, and boundaries to test.
Test
Check the paths, agents, and access that matter in your environment.
Report
Deliver organized findings your teams can review and act on.
Retest
Re-check after fixes and when the system changes.
Why teams choose Mythos.
Environment-specific testing
Checks are scoped to the systems, users, tools, and data paths that matter to each buyer environment.
Reviewable output
Findings are organized so security, AI, compliance, and leadership teams can act on them.
Built for AI execution paths
Mythos focuses on what AI can access, where requests move, and what actions can occur.
Human-accountable process
Customer teams remain in control of approvals, fixes, and deployment decisions.