Security

TramAI Security provides the control surface behind governed AI deployments: deny-by-default policy enforcement, data loss prevention, human-in-the-loop approval gates, build-time artifact verification, audit-grade evidence generation, and fully air-gapped runtime support.

For the broader product and platform framing, start with Sovereignty. That section explains why these controls matter, who they are for, and what adopting sovereign AI actually changes for a team.

Why This Exists

Production AI systems do more than call a model. They move sensitive context across provider boundaries, invoke tools, resume long-running workflows, and create decisions that may need to be justified to security, legal, compliance, or customer teams.

Sovereign mode exists to make those controls explicit instead of application-specific:

restrict where classified data can be routed
allow only approved models, providers, tools, and permissions
require human approval for high-risk tool execution
verify local model artifacts before the runtime starts
produce tamper-evident audit and evidence artifacts
enforce offline or air-gapped deployment rules at build time

The practical goal is simple: make governed AI behavior repeatable, testable, and reviewable before it reaches production.

The security surface spans two packages:

tramai-security — SPI definitions and implementations for DLP, audit, approval coordination, and artifact verification
tramai-sovereign — an aggregator module that composes tramai-security + tramai-standalone into a secure-by-default embedded runtime profile

For the detailed release-history view, see 0.4.0 — Sovereign Runtime and Governed AI Operations. It explains what changed between 0.3.1 and 0.4.0: the sovereign runtime, deny-by-default policy layer, trust-zone routing, approval gateway, hash-chained audit, evidence packs, artifact verification, offline deployment checks, and Spring Boot sovereign operations.

Feature Overview

Feature	Module	Complexity	Status	Use When
Sovereign Mode	`tramai-sovereign`	Medium	Stable	You need a secure-by-default runtime with deny-all policy, model allowlists, and trust zones
DLP	`tramai-security` + `tramai-core`	Low	Stable	You need to redact sensitive text (PII, secrets) from model outputs or tool results
Approval Workflows	`tramai-security` + `tramai-engine`	High	Experimental	You need human-in-the-loop approval before tool execution or workflow step resume
Artifact Verification	`tramai-security` + `tramai-core`	Medium	Stable	You need to verify model artifact integrity at build time (SHA-256, manifest enforcement)
Evidence Packs	`tramai-sovereign`	Low	Experimental	You need auditable, deterministic JSON evidence of deployment security posture
Offline Deployment	`tramai-sovereign`	Medium	Experimental	You need a fully air-gapped runtime with zero egress

Module Dependencies

tramai-sovereign
  ├── tramai-standalone
  ├── tramai-security
  │     ├── tramai-core (DLP SPI, ModelArtifactVerifier)
      └── tramai-engine (approval resume, SuspendedInvocationStore)

Quick Comparison

Concern	Sovereign Mode	DLP	Approval	Artifact Verification	Evidence Packs	Offline
Policy enforcement	Deny-by-default	—	—	—	—	—
Sensitive data redaction	—	Regex-based	—	—	—	—
Human-in-the-loop	Requires approval coordinator	—	Suspend/resume lifecycle	—	—	—
Build-time model integrity	—	—	—	SHA-256 streaming	—	—
Audit trail	Hash-chained audit engine	Redaction audit bridge	Lifecycle audit events	Verification receipts	Evidence packs	Zero-egress probes
Air-gap validation	Provider trust zones	—	—	Local-only verification	Zero-egress subsection	Full offline profile

Getting Started

The quickest path to a secure TramAI deployment:

// 1. Define your sovereign profile
val profile = SovereignProfileConfiguration(
    allowedModels = setOf("llama3.2"),
    allowedProviders = setOf("ollama"),
    providerZones = mapOf("ollama" to ProviderTrustZone.LOCAL),
)

// 2. Build the sovereign runtime
val tramai = SovereignTramai.builder()
    .profile(profile)
    .modelRegistry(registry)
    .auditStore(auditStore)
    .provider(ollamaProvider, name = "ollama", default = true)
    .model("llama3.2", "ollama")
    .build()

See the individual guides for each feature.

Next Steps

Sovereign Mode — secure-by-default embedded runtime
DLP — data loss prevention configuration
Approval Workflows — human-in-the-loop gates
Artifact Verification — model integrity checks
Evidence Packs — deployment attestation
Offline Deployment — air-gapped operations