AI Trust Registry
Transform discovered tools and agents into risk-prioritized, confidence-scored entities with auditable identity posture.
Last updated Mar 6, 2026
Track: now
Frameworks: Provenance, Mandate, Meridian
Ethira workflow steps: 1 (tool inventory), 2 (agent discovery)
Product Description
AI Trust Registry is the identity and governance intake layer for autonomous systems. It converts inventory and discovery records into actionable trust posture, so security, risk, and platform teams can prioritize onboarding, approvals, and remediation.
The product answers three implementation-critical questions:
- Do we know what this asset or agent actually is? (
Provenance) - Can humans or policy controls intervene when needed? (
Mandate) - Is supporting evidence complete and reliable enough for decisions? (
Meridian)
Problem Narrative: Why This Exists
Most organizations discover AI usage in reverse order. Agents and tools appear in production first, while identity, ownership, and control evidence appear later or not at all.
Typical failure sequence:
- A business team connects a new agent to customer or financial workflows.
- Security teams cannot verify who owns it, what it can access, or how intervention works.
- Compliance teams cannot prove governance posture at audit or incident time.
- Risk decisions become subjective because confidence in the evidence is unknown.
AI Trust Registry solves this by turning discovery into scored governance posture before broad production trust is granted.
Conceptual Scoring Approach
AI Trust Registry operationalizes identity posture as a conceptual composite model driven by three signals:
- Identity integrity and verification posture (
Provenance) - Human intervention readiness (
Mandate) - Evidence quality confidence (
Meridian)
These signals are combined with business exposure context to produce:
- A trust-priority score for remediation ordering
- A certification state for onboarding decisions
- A confidence state for operator review
Interpretation:
- Identity risk state estimates trust uncertainty from identity, intervention readiness, and evidence quality.
- Exposure context represents business impact scope (data sensitivity, access breadth, blast radius).
- Priority score ranks what must be remediated first.
Public note: exact formulas, weights, and threshold constants are intentionally withheld.
Why This Gap Exists In The Market
This problem is usually split across disconnected tooling categories:
- Asset inventories tell you what exists, not whether it is governable.
- IAM and access tools enforce permissions, not trust posture scoring.
- GRC systems track controls, but often depend on manual evidence assembly.
AI Trust Registry combines identity verification, intervention readiness, and evidence confidence into one quantitative decision layer. That integrated scoring approach is still uncommon in mainstream security and governance products.
Compliance Mapping (EU and US)
This product is a control-enablement layer, not legal advice. It supports auditability and policy enforcement by producing traceable scores and decision evidence.
| Region | Framework / Regulation | How AI Trust Registry Helps |
|---|---|---|
| EU | EU AI Act (risk management, governance, oversight expectations for high-risk systems) | Maintains scored inventory and control-readiness state per agent/tool. |
| EU | NIS2 (asset visibility, security governance) | Provides structured inventory + risk-prioritized remediation queues. |
| EU | DORA (ICT third-party and operational resilience controls) | Improves vendor/agent onboarding governance with scored trust posture. |
| US | NIST AI RMF (Govern, Map, Measure, Manage) | Supplies measurable identity/control confidence signals for lifecycle governance. |
| US | FTC Section 5 risk posture (deceptive or unfair AI operations concerns) | Produces documented governance decisions and intervention pathways. |
| US | SOC 2 style control evidence programs | Provides continuous evidence-linked score history for audits. |
Competitor Overlap Analysis
Potential overlap exists with multiple categories, but coverage is partial in each:
| Category | Where Overlap Exists | What AI Trust Registry Adds |
|---|---|---|
| CMDB / asset inventory tools | Discovery and asset records | Quantitative trust scoring + readiness thresholds for AI operations. |
| IAM / PAM platforms | Access control and credential policy | Framework-driven confidence and intervention-readiness scoring for agents/tools. |
| Model registries / MLOps catalogs | Model metadata and lineage | Cross-tool and agent governance posture tied to operational risk decisions. |
| GRC platforms | Control libraries and workflows | Real-time, score-based prioritization from live technical evidence. |
Primary Users
- Platform security teams deciding which agents can move from sandbox to production.
- Risk and compliance operators creating exposure views by business unit.
- Engineering leads triaging low-confidence integrations before launch.
How It Works
Emits
Detailed Example Use Cases
Use Case 1: New Vendor Agent Approval
A procurement team introduces an external vendor agent with access to customer communication channels.
- Discovery connector registers
agent_id. - AI Trust Registry scores identity, control readiness, and evidence quality.
- Policy requires minimum identity, oversight, and confidence criteria.
- Agent fails on Mandate because intervention hooks are incomplete.
- Approval is blocked until control points are implemented and rescored.
Outcome: high-risk onboarding prevented before production access.
Use Case 2: Internal Tool Estate Rationalization
A large enterprise has thousands of discovered tools, but no trust prioritization.
- Batch scoring processes all assets.
- Registry groups entities by risk band + confidence state.
- Teams focus first on "high risk + high confidence" and "medium risk + low confidence" clusters.
- Low-confidence entities get evidence enrichment tasks.
Outcome: remediation order becomes objective and scalable.
Integration Surfaces
POST /v1/assets/scorePOST /v1/agents/scoreGET /v1/score/{framework}/{entity_id}score.updatedwebhook
Minimum Data Contract
tenant_idasset_idoragent_idevent_idscore_versionevidence_events(quality,completeness,weight)- optional confidence inputs (
discovery_confidence,identity_confidence,oversight_readiness)
KPI Examples
- Scoring coverage of discovered assets.
- Low-confidence inventory ratio.
- Mean time to risk classification.
- Approval cycle time for production onboarding.
Supporting Documentation
Use Cases
Use the explorer below to filter potential customer scenarios for AI Trust Registry deployments.
Third-Party Agent Onboarding for Retail Banking
Score newly discovered AI vendors and internal agents before privileged access is granted to customer and payment workflows.
Buying trigger: Rising AI vendor inventory with unclear ownership and control readiness.
Potential customers
Hospital Network AI Asset Governance
Convert fragmented AI tool discovery into confidence-scored registry records before clinical and operational deployment expansion.
Buying trigger: Multiple AI copilots deployed with inconsistent attestation and intervention evidence.
Potential customers
Insurance Distribution and Claims Agent Registry
Create a trust-prioritized inventory of external and internal AI agents used in underwriting, servicing, and claims workflows.
Buying trigger: Audit pressure to show who owns each model-agent path and how intervention works.
Critical Infrastructure Supplier AI Register
Map and score AI-enabled vendor systems in grid, operations, and resilience functions to reduce unknown exposure.
Buying trigger: Dependency on third-party AI systems without consistent trust posture scoring.
Potential customers
Public Sector AI Tool Certification Intake
Support pre-deployment governance for agency AI tools with score-based certification states and remediation workflows.
Buying trigger: Need for defensible procurement and deployment gates across agencies and suppliers.
Telecom AI Operations Inventory Control
Prioritize AI tools and orchestration agents by trust posture before they can touch customer-support and network workflows.
Buying trigger: Rapid rollout of AI assistants with limited governance evidence at time of launch.
Potential customers
Federal Supplier AI Intake for Mission Systems
Evaluate AI-enabled contractors and subcontractor agents before they are authorized in federal mission and operations workflows.
Buying trigger: Contractors are introducing AI components faster than governance teams can certify identity and intervention readiness.
Potential customers
Pharma R&D Agent Registry Governance
Catalog and score research copilots and lab-analysis agents before they are trusted in regulated development workflows.
Buying trigger: R&D programs adopt multiple AI assistants without a single trust-prioritized governance inventory.
Potential customers
European Banking Group Subsidiary AI Registry
Standardize AI asset identity and trust posture scoring across multi-country banking subsidiaries and shared services.
Buying trigger: Regional entities run separate AI pilots with inconsistent ownership and intervention evidence.
Potential customers
EU Payments Agent Admission Controls
Score third-party payment orchestration and support agents before onboarding them into cross-border transaction operations.
Buying trigger: Payment platforms need a defensible gate before giving AI agents access to sensitive customer and settlement flows.