Trust fit assessment
Choose the proof model before choosing the runtime.
Use this assessment to clarify workload sensitivity, proof audience, timeline, decision stage, cloud/runtime assumptions, data geography and the path to a useful design-partner pilot.
1. Describe the pressure
Tell us what review, customer, compliance or adoption blocker matters so the recommendation starts from the business pressure, not from infrastructure preference.
2. See the fit
Get a recommended runtime/evidence configuration, the value it creates and the trust assumptions that still need to be reviewed.
3. Scope the path
Capture timeline, readiness, cloud context and who needs to be involved next so the first design-partner step is practical.
Shared Veritas-managed runtime
A Veritas-managed confidential runtime gives the fastest path to a verifiable pilot.
Buying signal
Not enough signal yet
Pilot shape
Needs scoping
Why this recommendation appears
• Complete more fields to sharpen the recommendation.
Evidence this path would produce
Workload identity, runtime conditions, policy version, tenant context, execution metadata, and verification outcome.
Honest trust trade-off
Fastest to adopt, but the tenant still trusts Veritas to operate the shared platform, isolate tenants correctly, and protect the control plane.
Implementation readout
Current blocker
UnknownTarget timeline
UnclearCloud context
UnclearReadiness
UnclearWhat Veritas would improve
What still needs review
Suggested next steps
• Use for first proof-of-value.
• Limit initial workloads to low/medium sensitivity.
• Plan migration path to tenant-dedicated runtime for enterprise review.
Supported trust configurations
These profiles help a tenant understand not just the recommendation, but the likely setup complexity, breadth of work and value they get back from the configuration.
Shared Veritas-managed runtime
Design partners, pilots, early enterprise proof, and low-to-medium sensitivity workloads.
What is included
• Shared runtime path
• Basic evidence receipt
• Pilot policy profile
Tenant decision question
Can we prove value quickly without starting with the hardest isolation model?
Fastest to adopt, but the tenant still trusts Veritas to operate the shared platform, isolate tenants correctly, and protect the control plane.
Tenant-dedicated governed session
Regulated SaaS, fintech, sensitive AI workflows, and enterprise design partners.
What is included
• Tenant runtime boundary
• Session evidence
• Policy and storage bindings
Tenant decision question
Do we need a tenant-specific proof path that still feels practical for users?
Strong balance of UX and proof, but individual intra-session actions may need separate evidence if every step must be independently reviewed.
Per-execution ephemeral runtime
High-assurance jobs, data sovereignty proofs, third-party data processing, and regulated one-off workflows.
What is included
• Short-lived runtime
• Job-bound evidence
• Teardown and artifact policy
Tenant decision question
Is every sensitive job important enough to justify startup and orchestration cost?
Strongest execution isolation, but higher cost, slower startup, more orchestration complexity, and more failure modes.
Long-running verified service
APIs, agent services, always-on regulated workloads, and customer-facing inference services.
What is included
• Service identity
• Re-attestation plan
• Runtime drift checks
Tenant decision question
Do we need an always-on API or service with continuing verification?
Better latency and availability, but weaker than per-job isolation because the runtime lives longer and must be rotated or re-attested.
Customer-held key / external data boundary
Data sovereignty, partner-cloud data, clean-room workflows, and customer-controlled datasets.
What is included
• External release condition
• Key/grant boundary
• Data owner evidence path
Tenant decision question
Does the tenant or data owner need to control data release outside Veritas?
Strongest data-owner control, with more integration complexity and dependency on external key or storage policy correctness.
Human-approved execution
High-risk AI actions, regulated operations, financial workflows, data exports, and review-sensitive approvals.
What is included
• Approval checkpoint
• Approver context
• Evidence-linked decision
Tenant decision question
Which sensitive actions need human judgment before they proceed?
Adds accountability and reviewability, but introduces human latency, approval fatigue, and approver judgment risk.