Components
Five layers, versioned schemas, no shared fate
Gateways stay stateless and horizontally scalable, secrets live in a dedicated layer, and telemetry moves over queues so an analytics outage never blocks a safe runtime decision.
Control plane
Manage inventory, policy and analytics.
- Tenants & users
- Agent registry
- Policy authoring
- Risk analytics
- Reporting
- Billing
- Configuration
Runtime gateway
Enforce close to the workload.
- Request interception
- Identity verification
- Policy evaluation
- Redaction
- Approval orchestration
- Forwarding
Telemetry pipeline
Ingest without ever blocking a decision.
- Append-only ingestion
- Normalization
- Enrichment
- Searchable storage
Connector fabric
Coexist with the existing stack.
- AI platforms
- Clouds
- Identity providers
- SaaS
- SIEM
- MCP
- APIs
Evidence store
Searchable records for audit and investigation.
- Structured records
- Policy versions
- Approvals
- Incident links
Reference flow
Agent
Agent Trust Gateway
Identity + policy + risk decision
Protected tool / API
Event / evidence pipeline
Agent
Agent Trust Gateway
Identity + policy + risk decision
Protected tool / API
Event / evidence pipeline
Implementation decisions
- Use stateless horizontally scalable gateways where possible; keep secrets in a dedicated secrets layer.
- Define versioned schemas for agents, identities, tools, actions, resources, policies, decisions and evidence.
- Use queues/streams for telemetry so analytics outages never block safe runtime decisions.
- Design regional isolation and customer-managed deployment options early enough to avoid architectural rework.
Definition of done
- A request can be evaluated locally when cloud analytics is degraded.
- All components emit correlated trace IDs.
- A tenant can export inventory, policies and events through APIs.
REFERENCE FLOW
Agent → Agent Trust Gateway → identity + policy + risk decision → protected tool/API → event/evidence pipeline.
Zero trust
Security Architecture & Zero Trust
Agent Trust Cloud should implement zero-trust principles internally and expose them as customer controls. NIST SP 800-207 emphasizes resource-focused protection and explicit authentication and authorization rather than implicit trust based on network location. That maps naturally to agents because agents are mobile software principals that can operate across clouds, SaaS and tool boundaries.
What we build
- Mutual authentication between gateways, connectors and control-plane services.
- Short-lived credentials and workload identities instead of long-lived static keys.
- Fine-grained service authorization enforced at every sensitive internal API.
- Strict separation of tenant data, administrative operations and production secrets.
- Continuous vulnerability management, dependency scanning, secrets scanning and threat modeling.
Internal security zones
- Create internal security zones: public edge, control plane, telemetry, evidence, secrets and administration.
- Adopt deny-by-default service permissions; authorize service-to-service calls by identity.
- Encrypt data in transit and at rest, with envelope encryption for high-sensitivity stores.
- Document the shared-responsibility model for SaaS vs. customer-managed gateway deployments.
STANDARDS ANCHOR
Use NIST Zero Trust Architecture as a design reference; adapt it to agent principals, delegated authority and tool-level decisions.
Data model
Core Data Model & Event Schema
The data model is a strategic asset. If Agent Trust Cloud normalizes agent identity, delegation, tools, resources and actions across vendors, every downstream module—policy, risk, audit, FinOps and incident response—gets stronger. Avoid vendor-specific event models leaking into the core domain.
Canonical objects
- Principal objects: human, workload, agent, service account, application and external actor.
- Agent objects: owner, purpose, provider, model, framework, environment, trust state, capabilities and lifecycle state.
- Authority graph: who delegated to whom, what scope, when, through which credential and for what purpose.
- Action events: requested tool, operation, target resource, parameters fingerprint, decision, approval, outcome and cost.
- Evidence objects: logs, prompts/hashes, policy versions, approvals, external alerts and incident links.
Schema rules
- Use globally unique immutable IDs; store display names separately.
- Version policy, identity and agent configuration so past decisions can be reconstructed.
- Normalize resource URIs and action verbs into a canonical vocabulary while preserving raw vendor fields.
- Design PII/secret classification metadata into schemas rather than adding it later.
DATA MOAT
The normalized agent-authority-action graph becomes the foundation for cross-vendor security intelligence.
By what you came looking for
The same platform, answered six ways
One page per question a buyer actually types. Every capability on them is read from the module model, so none of them can claim something the product does not ship.
AI agent security platform
Discover AI agents, control identity and access at runtime, contain unsafe actions, and preserve an audit-ready decision trail.
AI governance platform
An AI governance platform that decides whether an agent’s action is allowed, enforces the decision in the request path, and produces the record as a by-product. Inventory, ownership, policy and evidence in one control plane.
AI agent governance platform
Governing an agent means governing authority, not output. Register every agent with an accountable owner, evaluate its authority per action, narrow it on delegation, and keep the record of what it did.
AI security tools
AI security tooling splits into detection that reduces how often something goes wrong and enforcement that limits what a failure costs. What each layer covers, and which risks only the second one reaches.
AI risk management software
Model risk management assesses outputs. Agentic risk is about authority: what an agent can reach, what it can irreversibly change, and whether a control exists that can stop it. Software that measures the second kind.
Non-human identity management
Service accounts, keys and workload identities outnumber people and are governed less. What non-human identity management covers, and what an agent adds that credential hygiene does not reach.
Shadow AI discovery
Network monitoring misses in-house agents calling provider APIs from your own infrastructure. Where shadow AI actually shows up, and what a first-day discovery report can tell you before any policy is switched on.
AI access control
Evaluate every consequential AI-agent action against identity, delegated authority, resource context and policy before it reaches the target system.
MCP security platform
Discover MCP servers and tools, bind them to accountable owners, authorize tool calls at runtime and retain evidence for every consequential decision.
Prompt injection protection
Combine prompt-injection detection with deterministic action policy, data controls and evidence so a missed classification cannot become an authorized breach.
AI compliance software
Turn AI inventory, policy decisions, approvals and audit events into continuous evidence that can be mapped to the EU AI Act, NIST AI RMF, ISO 42001 and internal controls.