gate-house/ArchitectureBlueprint.md

Gate House — Architecture Blueprint

Repository: gate-house
Project family: NetKingdom
Artifact: ArchitectureBlueprint.md
Status: Initial architecture blueprint
Version: 0.1
Date: 2026-08-24

---

1. Purpose

This document translates the Gate House intent into a practical architecture for a first reference implementation.

Gate House is the deterministic authority plane for human, workload, and agent-active NetKingdom environments. It is the reference implementation of the Active Secrets Management (ASM) Plane.

Its central responsibility is:

Decide whether a requested action is allowed, under explicit and reproducible rules, and represent that decision as bounded authority that downstream systems can enforce.

Gate House does not replace identity systems, secrets backends, policy engines, runtime execution systems, or adaptive security systems. It coordinates them around a canonical authority model.

The architectural maxim is:

Cognition proposes. Authority disposes. Infrastructure executes.

---

2. Architectural Goals

Gate House should optimize for the following properties.

2.1 Deterministic final authorization

The final decision to grant or deny protected authority must be made by deterministic mechanisms.

LLMs, agents, probabilistic classifiers, or adaptive systems may contribute context or request actions, but they must not be able to manufacture authority by themselves.

2.2 Explicit identity chain

The system should distinguish:

• the principal on whose behalf authority originates;
• the actor directly requesting or performing the action;
• the runtime identity of the executing workload;
• the tenant and organizational context;
• the mandate or delegated purpose under which the action is attempted.

2.3 Least authority

Authority should be:

• task-scoped;
• action-scoped;
• resource-scoped;
• tenant-scoped;
• environment-scoped;
• time-bounded;
• quota-bounded where appropriate.

2.4 Short-lived authority

Standing credentials and standing privilege should be minimized.

Gate House should prefer issuance of short-lived authorization grants and rely on the Secrets Engine/OpenBao or cloud-native STS mechanisms to materialize them as short-lived credentials where necessary.

2.5 Explicit agentic operating modes

The system must support and distinguish:

• Agent Assistant Coding — human-supervised;
• Autonomous Agent Coding — independently operating under explicit mandate.

The transition from Assistant to Autonomous mode is an explicit governance event.

2.6 Safe autonomy

Autonomous agents must be independently stoppable.

Gate House should support deterministic:

• expiry;
• revocation;
• concurrency limits;
• action quotas;
• environment restrictions;
• circuit breakers;
• step-up authorization.

2.7 Explainability and evidence

Every material authorization decision should be reconstructable:

Who acted? On whose behalf? Under which mandate? Against which resource? For which action? Under which policy and constraints? For how long? And why was it allowed or denied?

2.8 Composability

Gate House should be a small control plane with explicit integration contracts.

It should avoid becoming:

• an IAM suite;
• another secret manager;
• a SIEM;
• an incident platform;
• a workflow engine;
• an agent runtime.

---

3. Non-Goals

The first Gate House implementation will not attempt to:

• replace OpenBao;
• replace Key Cape;
• replace User Engine;
• replace Tenant Engine;
• implement King's Guard;
• implement a general secrets scanner;
• implement a general policy language;
• implement a general-purpose workflow engine;
• implement full cloud IAM abstraction;
• implement autonomous remediation;
• become the system of record for every security policy in NetKingdom.

Its job is to provide the deterministic authority contract connecting these systems.

---

4. System Context

flowchart TB
    H[Human Developer / Operator]
    AA[Assistant Agent]
    AU[Autonomous Agent]
    KG[King's Guard]
    KC[Key Cape]
    UE[User Engine]
    TE[Tenant Engine]

    GH[Gate House]

    SE[Secrets Engine]
    OB[OpenBao]
    OW[Ops Warden]
    OM[Ops Mason]

    K8S[Kubernetes]
    CLOUD[Cloud APIs]
    DB[Databases]
    GIT[Git / CI/CD]
    SAAS[SaaS / External APIs]

    WH[Whitehat Security]
    AUD[Audit / Evidence Store]

    H --> KC
    KC --> GH
    UE --> GH
    TE --> GH

    H --> AA
    AA --> GH
    AU --> GH

    KG --> GH

    GH --> SE
    SE --> OB

    GH --> OW
    GH --> OM

    OW --> K8S
    OW --> CLOUD
    OW --> DB
    OM --> K8S
    OM --> CLOUD

    GH --> GIT
    GH --> SAAS

    GH --> AUD
    KG --> AUD
    OW --> AUD
    OM --> AUD

    WH --> GH
    WH --> KG

---

5. Three-Plane Model

Gate House is easiest to reason about as the center of three planes.

5.1 Cognitive Plane

The Cognitive Plane determines what might usefully happen next.

Examples:

• humans;
• LLMs;
• coding agents;
• operations agents;
• planners;
• MCP clients;
• A2A agents;
• King's Guard risk analysis.

The Cognitive Plane can request authority.

It cannot grant authority.

5.2 Authority Plane

Gate House belongs to the Authority Plane.

The Authority Plane evaluates:

• authenticated identity;
• principal;
• actor;
• runtime identity;
• tenant;
• environment;
• task;
• mandate;
• delegation;
• requested action;
• requested resource;
• policy;
• approval;
• posture;
• TTL;
• quotas;
• budgets;
• authority ceiling.

It produces:

• GRANT;
• DENY;
• REQUIRE_APPROVAL;
• optionally DEFER for non-security-critical upstream enrichment.

Security-critical ambiguity should normally fail closed.

5.3 Execution Plane

The Execution Plane changes system state.

Examples:

• Git;
• CI/CD;
• Kubernetes;
• cloud APIs;
• databases;
• SaaS;
• production services;
• Ops Warden;
• Ops Mason.

Gate House should not perform most execution itself.

Instead it should issue or represent authority that execution systems can enforce.

---

6. Core Domain Model

The domain model is intentionally small.

6.1 Principal

The human, organization, workload, or system on whose behalf authority originates.

Examples:

user:alice@example
service:release-controller
org:acme
tenant:acme

6.2 Actor

The entity directly requesting or performing the action.

Examples:

agent:deploy-agent-22
workload:ci/github/actions/release
human:alice@example

6.3 Runtime Identity

The concrete workload instance performing execution.

Examples:

spiffe://netkingdom/prod/deployer/22
k8s://cluster-a/ns-release/sa-deployer/pod-7f91
aws://sts/role-session/...

6.4 Tenant

The security and organizational domain in which the action occurs.

Examples:

tenant:acme
tenant:internal
tenant:platform

6.5 Environment

Operational environment.

Examples:

dev
test
staging
production

6.6 Mandate

A standing organizational authorization defining what an autonomous actor exists to do.

Examples:

mandate:release-automation
mandate:vulnerability-remediation
mandate:production-diagnostics

A mandate is not a credential.

It is a policy-recognized declaration of intended authority.

6.7 Delegation

A scoped transfer of authority from a principal to an actor.

Delegation should normally attenuate:

child_authority ⊆ parent_authority

6.8 Authority Ceiling

The maximum authority an actor can ever obtain under deterministic policy.

An agent may use less authority than its ceiling.

It may not reason itself into more.

6.9 Action

A canonical operation.

Examples:

kubernetes.pod.read
kubernetes.deployment.restart
database.select
database.schema.modify
cloud.iam.role.modify
git.repository.write
release.deploy

6.10 Resource

A canonical target.

Examples:

k8s://cluster-a/ns/payments/deployment/api
db://orders-prod/schema/public/table/orders
aws://123456789012/eks/cluster-a
git://github/acme/payments

6.11 Grant

A positive authorization result.

A grant should be:

• immutable after issuance;
• scoped;
• versioned;
• time-bounded;
• independently revocable where practical.

6.12 Denial

A negative authorization decision.

A denial should include:

• machine-readable reason;
• policy reference;
• evaluated constraints;
• optional remediation/approval hints.

6.13 Approval

An independent authorization artifact required for higher-risk actions.

Approvals should be:

• actor-bound;
• resource-bound;
• action-bound;
• parameter-bound;
• time-bound;
• replay-resistant.

6.14 Posture

A bounded security-state input.

Typical source:

• King's Guard.

Examples:

HEALTHY
DEGRADED
SUSPICIOUS
CONTAIN
BLOCKED

Gate House should interpret posture through deterministic policy.

6.15 Credential Binding

The mapping from an authority grant to the technical credential mechanism used by a target system.

Examples:

aws-sts-role
openbao-database-lease
openbao-pki-certificate
oauth-token-exchange
kubernetes-service-account-token

6.16 Decision Evidence

The authoritative record of:

• request;
• identities;
• evaluated policy;
• policy version;
• result;
• constraints;
• grant ID;
• approval ID;
• posture input;
• timestamps.

---

7. Canonical Authorization Request

A canonical request should be expressive enough for human, workload, and agent contexts without requiring every field in every scenario.

Example:

request_id: req-01J6A8A1FJ3P

principal:
  id: user:alice@example
  type: human

actor:
  id: agent:deploy-agent-22
  type: agent

runtime:
  id: spiffe://netkingdom/prod/deployer/22

tenant:
  id: tenant:acme

environment: production

task:
  id: release-7.2.1
  type: release

mandate:
  id: release-automation

delegation:
  mode: assistant
  parent_session: session-01J6A7

request:
  action: production.deploy
  resource: k8s://cluster-a/ns/payments/deployment/api

parameters:
  image: registry.example/payments:7.2.1
  replicas: 6

approval:
  id: approval-9432

posture:
  actor: HEALTHY
  resource: HEALTHY

constraints:
  requested_ttl: 20m
  max_mutations: 1

context:
  trace_id: 4bf92f3577b34da6a3ce929d0e0e4736
  source: mcp://ops-warden

---

8. Canonical Decision

Example:

decision_id: dec-01J6A8AJ4YQW
request_id: req-01J6A8A1FJ3P

decision: GRANT

policy:
  bundle: production-release
  version: 2026-08-24.3

authority:
  grant_id: grant-01J6A8AXK0N2
  action: production.deploy
  resource: k8s://cluster-a/ns/payments/deployment/api
  expires_at: 2026-08-24T20:22:00+02:00
  mutation_limit: 1

credential_binding:
  type: kubernetes-execution-token
  provider: secrets-engine
  audience: ops-warden

obligations:
  - immutable_audit
  - emit_deployment_evidence
  - revoke_after_first_success

explanation:
  code: RELEASE_MANDATE_APPROVED
  message: >
    Actor is operating under the release-automation mandate,
    approval matches resource and parameters, and current posture
    permits one production deployment.

evidence:
  policy_hash: sha256:...
  input_hash: sha256:...

---

9. Decision Types

The first implementation should support at least four outcomes.

9.1 GRANT

The request is authorized under explicit constraints.

9.2 DENY

The request is not authorized.

9.3 REQUIRE_APPROVAL

The request may proceed only after acquisition of a separately governed approval artifact.

This is preferable to returning a soft "maybe".

9.4 DEFER

Optional.

Used only for non-security-critical enrichment, such as waiting for authoritative tenant metadata.

A request must not be allowed to cross a privileged boundary while unresolved.

---

10. Internal Component Architecture

flowchart LR
    API[Authorization API]
    NORM[Request Normalizer]
    CTX[Context Resolver]
    POLICY[Policy Decision Service]
    CEIL[Authority Ceiling Evaluator]
    APPR[Approval Validator]
    POST[Posture Interpreter]
    LIMIT[Limits / Change Dynamics]
    GRANT[Grant Service]
    CRED[Credential Binding Adapter]
    REVOKE[Revocation Service]
    AUDIT[Audit / Evidence Service]
    CACHE[Policy & Context Cache]

    API --> NORM
    NORM --> CTX
    CTX --> POLICY

    POLICY --> CEIL
    POLICY --> APPR
    POLICY --> POST
    POLICY --> LIMIT

    CEIL --> GRANT
    APPR --> GRANT
    POST --> GRANT
    LIMIT --> GRANT

    GRANT --> CRED
    GRANT --> AUDIT
    REVOKE --> AUDIT

    CACHE --> CTX
    CACHE --> POLICY

---

11. Component Responsibilities

11.1 Authorization API

Responsibilities:

• receive authorization requests;
• validate schema;
• enforce authentication of calling systems;
• attach correlation metadata;
• return normalized decisions.

The API should not embed business policy.

11.2 Request Normalizer

Responsibilities:

• canonicalize resource identifiers;
• canonicalize actions;
• normalize parameters;
• remove ambiguity before policy evaluation;
• ensure parameter-bound approvals compare normalized values.

Example:

kubectl rollout restart deployment api -n payments

may normalize to:

action: kubernetes.deployment.restart
resource: k8s://cluster-a/ns/payments/deployment/api

11.3 Context Resolver

Fetches authoritative context from trusted systems.

Potential sources:

• User Engine;
• Tenant Engine;
• Key Cape;
• Mandate registry;
• King's Guard;
• environment/resource registry.

The resolver should distinguish:

• authoritative context;
• advisory context;
• stale context.

Security-critical missing context should fail closed.

11.4 Policy Decision Service

Evaluates deterministic authorization logic.

The first implementation should make the policy backend pluggable.

Potential engines:

• OPA/Rego;
• Cedar;
• native evaluator.

The domain model must not depend on one policy language.

11.5 Authority Ceiling Evaluator

Ensures that requested authority does not exceed:

• actor ceiling;
• mandate ceiling;
• delegation ceiling;
• tenant ceiling;
• environment ceiling;
• parent-agent ceiling.

This should be a mandatory layer even if policy already encodes similar logic.

Defense in depth is appropriate at the authority boundary.

11.6 Approval Validator

Validates step-up authorization artifacts.

Should verify:

• issuer;
• actor;
• principal;
• action;
• resource;
• normalized parameters;
• issuance time;
• expiry;
• nonce/replay properties;
• approval class.

11.7 Posture Interpreter

Converts trusted adaptive-security posture into deterministic restrictions.

Example:

posture:
  actor: DEGRADED

may resolve into:

restrictions:
  deny:
    - production.write
  require_approval:
    - staging.write

The Posture Interpreter must not call an LLM to determine whether authority exists.

11.8 Limits / Change Dynamics Controller

Evaluates operational limits:

• concurrent grants;
• concurrent mutating actors;
• per-task mutation count;
• runtime;
• deployment frequency;
• destructive action quota;
• token/compute budget;
• agent-chain depth.

This is not the primary authorization layer.

It is a secondary blast-radius layer.

11.9 Grant Service

Issues immutable grant records.

Responsibilities:

• assign grant IDs;
• enforce TTL;
• encode authority scope;
• attach obligations;
• register revocation state;
• optionally mint signed grant artifacts.

11.10 Credential Binding Adapter

Maps grants to credential mechanisms.

It should not contain the actual secret backend logic.

Instead it should delegate to Secrets Engine.

Example:

grant
  ↓
credential binding request
  ↓
Secrets Engine
  ↓
OpenBao / AWS STS / OAuth / PKI

11.11 Revocation Service

Supports:

• grant revocation;
• actor suspension;
• mandate suspension;
• tenant freeze;
• emergency kill switch;
• propagation to credential providers.

Revocation should not depend on the affected agent cooperating.

11.12 Audit / Evidence Service

Records:

• request;
• normalized request;
• identity chain;
• context versions;
• policy version;
• decision;
• grant;
• revocation;
• credential binding metadata;
• execution references where available.

Audit storage should be independently controlled.

---

12. Policy Architecture

Gate House should separate policy into layers.

12.1 Baseline Invariants

Hard global rules.

Examples:

deny if tenant is unresolved
deny if production action has no authenticated actor
deny if autonomous actor has no mandate
deny if child authority exceeds parent authority
deny if actor attempts to modify its own authority ceiling
deny if audit subsystem is unavailable for critical actions

These should be difficult to override.

12.2 Environment Policy

Examples:

production
staging
development

Production should require stronger controls.

12.3 Tenant Policy

Per-tenant restrictions.

Important for multi-tenancy.

12.4 Mandate Policy

Defines what autonomous actor classes are intended to do.

12.5 Resource Policy

Specific restrictions for:

• Kubernetes clusters;
• databases;
• cloud accounts;
• repositories;
• SaaS APIs.

12.6 Posture Policy

Maps King's Guard state to authority consequences.

12.7 Approval Policy

Defines which action classes require:

• human approval;
• dual approval;
• break-glass;
• step-up MFA;
• parameter-bound authorization.

12.8 Change Dynamics Policy

Constrains:

• concurrency;
• rate;
• budget;
• runtime;
• action quotas.

---

13. Policy Evaluation Order

A simple initial evaluation order is desirable.

1. authenticate caller
2. normalize request
3. resolve principal / actor / tenant / runtime
4. verify operating mode
5. verify mandate / delegation
6. enforce authority ceiling
7. evaluate hard invariants
8. evaluate environment + tenant + resource policy
9. apply posture restrictions
10. validate approval
11. apply change-dynamics limits
12. derive obligations
13. GRANT or DENY
14. emit evidence

Where possible, evaluation should be monotonic toward restriction:

initial possible authority
    ↓
identity constraints
    ↓
mandate constraints
    ↓
tenant constraints
    ↓
resource constraints
    ↓
posture constraints
    ↓
approval constraints
    ↓
limits
    ↓
final authority

No later stage should silently expand beyond an earlier hard ceiling.

---

14. Assistant Mode Architecture

14.1 Intent

Assistant mode keeps the human as primary principal while preserving the agent as actor.

sequenceDiagram
    participant H as Human
    participant A as Assistant Agent
    participant K as Key Cape
    participant G as Gate House
    participant O as Ops Warden
    participant R as Resource

    H->>K: authenticate
    K-->>H: human session
    H->>A: request task
    A->>G: request action(principal=H, actor=A)
    G->>G: deterministic policy evaluation
    G-->>A: scoped grant
    A->>O: execute with grant
    O->>R: perform authorized action
    R-->>O: result
    O-->>G: execution evidence

14.2 Assistant-Mode Requirements

• human session must be active;
• human principal must remain identifiable;
• agent should not inherit every human credential;
• privileged grants should be short-lived;
• human session termination should invalidate or shorten associated authority;
• irreversible actions may require explicit confirmation;
• agent must not silently continue as autonomous actor.

---

15. Autonomous Mode Architecture

sequenceDiagram
    participant T as Trigger/Scheduler
    participant A as Autonomous Agent
    participant K as Workload Identity
    participant G as Gate House
    participant S as Secrets Engine
    participant O as Ops Warden
    participant R as Resource

    T->>A: start task
    A->>K: attest workload identity
    K-->>A: short-lived identity
    A->>G: request action(actor=A, mandate=M)
    G->>G: evaluate mandate + ceiling + policy
    G-->>A: grant
    A->>S: request credential binding(grant)
    S-->>A: short-lived execution credential
    A->>O: execute
    O->>R: protected action
    O-->>G: evidence

15.1 Autonomous-Mode Requirements

Every autonomous agent must have:

• unique identity;
• named governance owner;
• explicit mandate;
• authority ceiling;
• lifecycle;
• runtime limit;
• task budget;
• concurrency limit;
• revocation path;
• audit trace;
• no borrowed human credential.

---

16. King's Guard Integration

King's Guard is an adaptive security system.

Gate House should consume King's Guard output as bounded posture input.

16.1 Accepted Pattern

King's Guard:
  actor = agent-934
  posture = DEGRADED
  confidence = 0.72
  reason = anomalous-tool-usage

Gate House policy:

IF actor.posture == DEGRADED
THEN
  deny production.write
  allow production.read
  require approval for staging.write

16.2 Forbidden Pattern

King's Guard:
  "This looks safe enough.
   Give agent-934 admin."

Adaptive systems may restrict or request authority changes.

They must not bypass Gate House.

16.3 Containment Flow

sequenceDiagram
    participant KG as King's Guard
    participant GH as Gate House
    participant OW as Ops Warden
    participant R as Resource

    KG->>GH: posture update: CONTAIN actor-X
    GH->>GH: deterministic containment policy
    GH-->>OW: authorized containment grant
    OW->>R: isolate / suspend / revoke
    OW-->>KG: containment result
    KG->>KG: validate outcome

---

17. Secrets Engine and OpenBao Integration

Gate House should not directly become a secrets backend.

17.1 Separation of Responsibilities

Gate House:
  "This authority is allowed."

Secrets Engine:
  "This is how the target system expects authority to be represented."

OpenBao:
  "This is how the required credential is securely issued and leased."

17.2 Example

Request:

database.select
db://orders-prod
TTL 15m

Gate House:

GRANT

Secrets Engine:

binding = openbao.database.dynamic
role = orders-readonly
ttl = 15m

OpenBao:

username = v-gh-...
password = ...
lease = 15m

The credential should ideally be consumed by a trusted execution layer and not placed into LLM context.

---

18. Execution Gateway Pattern

Where possible, Gate House should encourage a brokered execution model.

Preferred:

Agent
  ↓ request
Gate House
  ↓ grant
Execution Gateway / Ops Warden
  ↓ obtains credential
Target System

Less preferred:

Agent
  ↓
raw reusable credential
  ↓
Target System

Benefits:

• fewer secrets in model context;
• easier audit;
• easier revocation;
• target-specific enforcement;
• reduced exfiltration risk.

---

19. MCP Integration

MCP is treated as a capability interface, not an authorization system.

19.1 MCP Tool Registration

Tool metadata may define:

• action identifier;
• resource schema;
• parameter schema;
• risk class;
• reversibility;
• required grant class.

Example:

tool: restart_service
action: kubernetes.deployment.restart
risk: privileged-write
reversible: true
authorization:
  gate_house: required

19.2 Invocation Flow

LLM selects tool
  ↓
MCP server constructs normalized authority request
  ↓
Gate House
  ↓
GRANT / DENY
  ↓
MCP server executes only if granted

19.3 Token Handling

Inbound tokens should not be blindly forwarded to downstream resources.

Downstream credentials should be separately issued or exchanged and audience-bound.

---

20. Change Dynamics Envelope

Gate House should model agentic operational velocity as a security concern.

20.1 Envelope Dimensions

Possible controls:

change_dynamics:
  max_concurrent_mutations: 2
  max_actions_per_grant: 1
  max_resources_per_task: 5
  max_runtime: 30m
  max_agent_chain_depth: 3
  max_destructive_actions: 0
  max_deployments_per_hour: 4
  token_budget_eur: 10
  compute_budget_cpu_minutes: 120

20.2 Hard vs Soft Limits

Hard:

• destructive action quota;
• production concurrency;
• authority ceiling;
• environment boundary.

Soft/advisory:

• cost warning;
• anomaly score;
• human attention recommendation.

Security-critical limits should be deterministic.

---

21. Circuit Breaker Architecture

Gate House should support independent suspension primitives.

Examples:

suspend actor
suspend mandate
suspend tenant writes
suspend production writes
revoke grant
revoke all grants for runtime
switch environment to read-only
require approval for all mutations

21.1 Circuit Breaker Sources

Potential sources:

• King's Guard;
• human operator;
• incident automation;
• budget threshold;
• audit failure;
• policy subsystem failure;
• anomaly threshold mapped by deterministic policy.

21.2 Safe Degraded Mode

Preferred sequence:

NORMAL
  ↓
RESTRICTED
  ↓
READ_ONLY
  ↓
BLOCKED

Observability should usually remain available longer than mutation authority.

---

22. Multi-Tenant Model

Gate House is intended for multi-tenant environments.

Tenant context must be explicit.

22.1 Rules

• every protected request should resolve to a tenant or explicitly be platform-scoped;
• cross-tenant authority must be exceptional;
• tenant isolation policy must be evaluated before resource policy;
• ambiguous tenant context should fail closed;
• audit evidence must retain tenant context;
• King's Guard signals must not accidentally leak tenant-private information across boundaries.

22.2 Platform Operations

Platform-level actors should use explicit platform mandates rather than implicit global authority.

Example:

mandate: platform-maintenance
scope:
  tenants: "*"
constraints:
  action_class: infrastructure-only
  customer_data_access: denied

---

23. Policy Self-Modification Protection

A major agentic failure mode is self-escalation.

Gate House must treat modifications to:

• authority ceilings;
• policy bundles;
• identity trust;
• tenant mappings;
• approval rules;
• audit configuration;
• credential backends;
• circuit breakers;

as control-plane changes requiring stronger authorization than ordinary workload changes.

Example invariant:

IF actor == subject_of_policy_change
AND change would expand actor authority
THEN require independent governance principal

---

24. Audit Architecture

24.1 Event Types

At minimum:

AUTH_REQUEST_RECEIVED
AUTH_CONTEXT_RESOLVED
AUTH_DECISION
GRANT_ISSUED
GRANT_REVOKED
APPROVAL_VALIDATED
POSTURE_APPLIED
CREDENTIAL_BINDING_REQUESTED
CREDENTIAL_BINDING_ISSUED
EXECUTION_STARTED
EXECUTION_COMPLETED
CIRCUIT_BREAKER_TRIGGERED
POLICY_CHANGED

24.2 Evidence Requirements

Privileged action evidence should support reconstruction of:

• principal;
• actor;
• runtime;
• tenant;
• task;
• mandate;
• resource;
• action;
• parameters hash;
• approval;
• posture;
• policy version;
• grant;
• credential binding;
• execution result.

24.3 Tamper Resistance

The actor being audited should not have authority to erase the authoritative audit record.

Gate House may write to:

• append-only event store;
• external SIEM;
• immutable object storage;
• signed evidence log.

The first implementation may use ordinary storage but should preserve the interface for stronger backends.

---

25. Data Storage

Gate House should minimize durable sensitive state.

25.1 Durable State

Likely:

• mandates;
• grant metadata;
• revocation state;
• policy references;
• approval references;
• audit metadata;
• limits/counters.

25.2 Avoid Storing

Prefer not to store:

• raw secret values;
• cloud access keys;
• database passwords;
• OAuth refresh tokens;
• full sensitive payloads unless required.

Credential material belongs in Secrets Engine/OpenBao or equivalent backends.

---

26. API Surface

Initial APIs should remain small.

26.1 Authorization

POST /v1/authorize

Returns:

GRANT
DENY
REQUIRE_APPROVAL

26.2 Grant Introspection

GET /v1/grants/{grant_id}

Used by execution gateways.

26.3 Revocation

POST /v1/grants/{grant_id}/revoke
POST /v1/actors/{actor_id}/suspend
POST /v1/mandates/{mandate_id}/suspend

26.4 Posture

POST /v1/posture

Trusted integrations only.

26.5 Mandates

POST /v1/mandates
GET  /v1/mandates/{id}

Initial implementation may keep mandates static/config-driven.

26.6 Decision Explain

GET /v1/decisions/{decision_id}

Returns machine- and human-readable explanation.

---

27. Trust Boundaries

flowchart TB
    subgraph Untrusted["Potentially Probabilistic / Untrusted"]
        LLM[LLM / Agent]
        MCP[MCP Client]
    end

    subgraph TrustedIdentity["Trusted Identity Sources"]
        KC[Key Cape]
        UE[User Engine]
        TE[Tenant Engine]
    end

    subgraph GateHouse["Gate House Trust Boundary"]
        API[API]
        POL[Policy]
        GRT[Grant Service]
        AUD[Audit]
    end

    subgraph Credential["Credential Boundary"]
        SE[Secrets Engine]
        OB[OpenBao]
    end

    subgraph Execution["Protected Execution"]
        OW[Ops Warden]
        RES[Resources]
    end

    LLM --> API
    MCP --> API
    KC --> API
    UE --> API
    TE --> API
    API --> POL
    POL --> GRT
    GRT --> SE
    SE --> OB
    GRT --> OW
    OW --> RES
    GRT --> AUD

Gate House must not blindly trust identity fields supplied by the agent request.

Identity and tenant claims should come from authenticated or independently resolved sources.

---

28. Failure Modes

28.1 Policy Engine Unavailable

Production writes:

DENY

Read-only diagnostics may optionally use cached policy if policy explicitly permits.

28.2 King's Guard Unavailable

Do not grant additional authority.

Use last-known-good restrictive posture or configured fallback.

28.3 Audit Unavailable

For privileged production mutation:

DENY

or use redundant audit path.

28.4 Secrets Engine Unavailable

Authorization may still return a grant, but execution requiring credentials cannot proceed.

Gate House should distinguish:

authority granted
credential unavailable

28.5 User Engine / Tenant Engine Unavailable

If identity or tenant context is security-critical and cannot be resolved:

DENY

28.6 Stale Context

Every externally resolved context item should have:

• source;
• version;
• fetched time;
• expiry/staleness policy.

---

29. Caching

Caching is useful but dangerous at authorization boundaries.

29.1 Cacheable

Potentially:

• static resource metadata;
• policy bundles;
• tenant membership with short TTL;
• mandate definitions;
• public key sets.

29.2 Avoid Long-Lived Caching

Avoid long-lived caching of:

• revocation state;
• incident posture;
• emergency suspension;
• approvals;
• human session presence.

29.3 Cache Rule

A cache may improve availability.

It must not silently expand authority after source-of-truth changes.

---

30. Deployment Model

The first reference implementation should target Kubernetes.

30.1 Suggested Services

Minimal deployment:

gate-house-api
gate-house-policy
gate-house-grants
gate-house-audit

The first prototype may combine these into one process with clear internal module boundaries.

30.2 Production Direction

Later:

• multiple replicas;
• stateless decision path;
• durable grant/revocation store;
• dedicated audit sink;
• independent policy bundle distribution;
• mTLS or workload identity;
• namespace/network isolation;
• protected admin/control API.

30.3 Workload Identity

Gate House itself should authenticate downstream services using workload identity rather than static credentials wherever possible.

---

31. Technology Direction

The architecture should remain implementation-neutral initially.

Potential choices:

31.1 API

• HTTP/JSON for initial interoperability;
• gRPC later if useful for high-volume decision calls.

31.2 Policy

• OPA/Rego is a strong initial candidate;
• Cedar is worth benchmarking for typed authorization semantics;
• native policy layer may be appropriate for hard invariants.

A hybrid model may eventually be useful:

hard invariants: native
organizational policy: OPA/Cedar

31.3 Storage

Potential:

• PostgreSQL for grants, mandates, revocation, audit metadata;
• append-only event store later.

31.4 Identity

• OIDC;
• SPIFFE/SPIRE;
• Kubernetes projected service-account tokens;
• cloud workload identity.

31.5 Secrets

• Secrets Engine abstraction;
• OpenBao backend;
• cloud-native STS backends.

---

32. Reference Repository Structure

gate-house/
├── README.md
├── INTENT.md
├── ArchitectureBlueprint.md
├── docs/
│   ├── adr/
│   ├── concepts/
│   ├── flows/
│   └── threat-model/
├── api/
│   ├── openapi/
│   └── schemas/
├── domain/
│   ├── identity/
│   ├── authority/
│   ├── delegation/
│   ├── mandate/
│   ├── posture/
│   └── grant/
├── policy/
│   ├── invariants/
│   ├── bundles/
│   ├── engine/
│   └── tests/
├── grants/
│   ├── issuance/
│   ├── revocation/
│   └── introspection/
├── approvals/
├── limits/
│   ├── quota/
│   ├── budget/
│   └── concurrency/
├── integrations/
│   ├── user-engine/
│   ├── tenant-engine/
│   ├── key-cape/
│   ├── secrets-engine/
│   ├── openbao/
│   ├── kings-guard/
│   ├── ops-warden/
│   └── mcp/
├── audit/
├── cmd/
├── internal/
├── test/
│   ├── unit/
│   ├── integration/
│   ├── adversarial/
│   └── scenarios/
└── deploy/
    ├── kubernetes/
    └── helm/

This is a reference layout, not yet a constraint.

---

33. Initial End-to-End Scenarios

Scenario 1 — Human-Assisted Kubernetes Read

Goal:

• prove principal/actor separation;
• prove assistant-mode session binding;
• prove read-only grant.

Flow:

human SSO
→ assistant agent
→ Gate House
→ read-only grant
→ Ops Warden
→ Kubernetes logs

Success criteria:

• no production write;
• grant expires with session;
• audit reconstructs human + agent.

Scenario 2 — Autonomous Release

Goal:

• prove workload identity;
• prove mandate;
• prove production deployment scope.

Flow:

scheduler
→ release agent
→ workload identity
→ Gate House
→ production.deploy grant
→ Ops Warden
→ Kubernetes

Success criteria:

• no IAM modification;
• one deployment only;
• short TTL;
• independent revocation.

Scenario 3 — Dynamic DB Credential

Goal:

• prove authority-to-credential separation.

Flow:

agent
→ Gate House
→ GRANT database.select
→ Secrets Engine
→ OpenBao
→ dynamic DB credential
→ execution gateway

Success criteria:

• credential not exposed to model;
• credential TTL <= grant TTL;
• credential revoked with grant.

Scenario 4 — King's Guard Restriction

Goal:

• prove adaptive input cannot grant privilege.

Flow:

King's Guard posture = DEGRADED
→ Gate House
→ deny production.write
→ allow read

Success criteria:

• deterministic restriction;
• no model reasoning in authorization path.

Scenario 5 — Self-Escalation Attempt

Goal:

• prove control-plane protection.

Flow:

agent
→ request policy change
→ attempt to expand own authority
→ Gate House
→ DENY / independent governance required

---

34. Security Test Program

The reference implementation should include adversarial tests from the beginning.

34.1 Malicious Model Test

Assume agent intentionally requests maximum destructive access.

Expected:

authority ceiling holds

34.2 Prompt Injection Test

Assume tool description tells agent to dump credentials.

Expected:

no reusable credentials available in model context

34.3 Human Disconnect Test

Terminate Assistant session.

Expected:

associated privileged grants expire or are revoked

34.4 Policy Self-Modification Test

Agent attempts own privilege expansion.

Expected:

DENY

34.5 Approval Replay Test

Reuse approval for different parameters.

Expected:

DENY

34.6 Cross-Tenant Test

Attempt resource access in another tenant.

Expected:

DENY

34.7 MCP Token Passthrough Test

Attempt reuse of inbound token against downstream resource.

Expected:

DENY / separate credential required

34.8 Audit Loss Test

Disable audit sink.

Expected:

privileged mutation fails closed

34.9 Circuit Breaker Test

Suspend actor while task is active.

Expected:

new actions denied
active grants revoked where possible

---

35. Performance Targets

Security correctness is primary, but authorization cannot become an operational bottleneck.

Initial qualitative targets:

• local/cached authorization decision: low tens of milliseconds;
• external context lookups should be minimized;
• grants should be cacheable by execution gateways until expiry/revocation;
• revocation propagation should be near-real-time for privileged actors;
• audit should be asynchronous where safe but durable before returning success for critical actions.

Exact SLOs should be established after the first benchmark prototype.

---

36. Availability Model

Gate House is a security-critical dependency.

Production architecture should eventually target:

• multiple replicas;
• redundant policy engine;
• redundant audit path;
• durable revocation store;
• clear fail-closed semantics.

Availability must not be improved by silently bypassing authorization.

A preferred degradation model is:

full authority
  ↓
cached read-only
  ↓
diagnostic-only
  ↓
deny privileged action

---

37. Observability

Gate House should expose:

Metrics

• authorization requests;
• grants;
• denials;
• approval-required decisions;
• decision latency;
• context lookup latency;
• policy errors;
• active grants;
• revoked grants;
• actor suspensions;
• mandate suspensions;
• circuit-breaker activations;
• cache hit rate;
• audit failures;
• cross-tenant denial count.

Traces

Trace:

request
→ normalization
→ context resolution
→ policy
→ grant
→ credential binding
→ execution

Logs

Logs must avoid:

• raw credentials;
• sensitive approval payloads;
• unnecessary customer data.

---

38. Administrative Interfaces

The admin/control path is itself a privileged security surface.

Initial administrative functions:

• register/update mandate;
• suspend actor;
• suspend mandate;
• revoke grant;
• set emergency environment restriction;
• inspect decision;
• inspect policy version.

Administrative access should:

• require strong authentication;
• be separately authorized;
• be audited;
• never be available to ordinary agent workload identities.

---

39. Governance Integration

Gate House should make governance machine-readable where practical.

A mandate record may include:

id: mandate:release-automation
owner: team:platform-release
purpose: deploy approved releases
operating_mode: autonomous

authority_ceiling:
  actions:
    - production.deploy
  resources:
    - k8s://cluster-a/ns/payments/*
  denied_actions:
    - cloud.iam.*
    - kubernetes.rbac.modify

constraints:
  max_runtime: 30m
  max_concurrency: 1
  max_mutations_per_task: 1

This becomes a bridge between organizational governance and runtime enforcement.

---

40. Architecture Decision Records to Create Early

Recommended ADRs:

ADR-001 Canonical Authority Request Schema
ADR-002 Principal / Actor / Runtime Identity Model
ADR-003 Policy Engine Selection
ADR-004 Grant Representation and Signing
ADR-005 Revocation Semantics
ADR-006 Posture Input Contract with King's Guard
ADR-007 Credential Binding Contract with Secrets Engine
ADR-008 Audit Evidence Model
ADR-009 Assistant Session Binding
ADR-010 Autonomous Mandate Model
ADR-011 Multi-Tenant Isolation Rules
ADR-012 Failure / Fail-Closed Semantics
ADR-013 MCP Authorization Integration
ADR-014 Policy Self-Modification Protection

---

41. Prototype Milestones

M0 — Executable Skeleton

Deliver:

• service skeleton;
• /authorize;
• canonical request schema;
• canonical decision schema;
• static policy;
• structured audit.

Success:

request → deterministic GRANT/DENY

M1 — Identity and Tenant Context

Deliver:

• principal;
• actor;
• runtime identity;
• tenant;
• assistant/autonomous mode.

Success:

same actor receives different decisions depending on principal / tenant / mode

M2 — Mandates and Authority Ceilings

Deliver:

• autonomous mandate model;
• authority ceiling;
• delegation attenuation.

Success:

agent cannot exceed mandate

M3 — Grant Lifecycle

Deliver:

• grant issuance;
• TTL;
• introspection;
• revocation;
• actor suspension.

Success:

granted authority can be independently revoked

M4 — Secrets Binding

Deliver:

• Secrets Engine integration;
• OpenBao dynamic database credential scenario.

Success:

authority → ephemeral credential → expiry/revocation

M5 — Agent Assistant Scenario

Deliver:

• human principal;
• agent actor;
• session binding;
• disconnect revocation.

Success:

human-supervised authority stops with supervision

M6 — Autonomous Release Scenario

Deliver:

• workload identity;
• autonomous mandate;
• deployment grant;
• quota;
• runtime budget.

Success:

scheduled agent deploys but cannot alter IAM/policy

M7 — King's Guard Posture

Deliver:

• posture API;
• deterministic posture restrictions;
• circuit breaker.

Success:

adaptive risk signal reduces authority without granting privilege

M8 — Adversarial Security Suite

Deliver:

• self-escalation;
• cross-tenant;
• token misuse;
• approval replay;
• audit outage;
• circuit breaker tests.

Success:

incorrect/malicious agent behavior does not cross configured authority boundaries

---

42. Longer-Term Evolution

Possible later capabilities:

• signed portable grants;
• token exchange;
• cross-domain delegation;
• policy simulation;
• policy impact analysis;
• temporal authorization;
• graph-based delegation chains;
• distributed revocation;
• tenant-local policy extensions;
• confidential-computing attestation;
• agent reputation/posture inputs;
• capability-based authorization;
• formal verification of hard invariants;
• high-assurance decision replay;
• multi-region authority plane.

These should only be introduced if they preserve the small and explicit responsibility boundary.

---

43. Architectural Invariants

Gate House should preserve the following invariants throughout implementation.

A-01

No LLM or statistical component is the final source of privilege.

A-02

Every privileged action resolves to an explicit actor.

A-03

Autonomous actions resolve to an explicit mandate.

A-04

Delegated authority cannot silently exceed parent authority.

A-05

Tenant context is explicit for protected actions.

A-06

Production mutations require deterministic policy evaluation.

A-07

A grant cannot outlive its configured TTL.

A-08

Revocation does not depend on the affected agent cooperating.

A-09

Adaptive posture may restrict authority but cannot probabilistically create new authority.

A-10

Credentials are not treated as the source of authority.

A-11

Agents cannot normally change the controls that define their own authority ceiling.

A-12

Audit evidence is independent of the actor being audited.

A-13

Failure of security-critical dependencies does not silently expand authority.

A-14

Assistant sessions do not silently become autonomous sessions.

A-15

If every agent behaves incorrectly, deterministic authority boundaries still hold.

---

44. Blueprint Summary

Gate House should be implemented as a small, composable, deterministic authority control plane.

Its core path is:

authenticated context
  ↓
principal + actor + runtime
  ↓
tenant + environment
  ↓
mandate / delegation
  ↓
action + resource + parameters
  ↓
authority ceiling
  ↓
deterministic policy
  ↓
posture restrictions
  ↓
approval
  ↓
change-dynamics limits
  ↓
GRANT / DENY
  ↓
credential binding
  ↓
execution
  ↓
audit / evidence

The architecture should make one property easy to demonstrate:

An intelligent system may request anything. It can only cause the protected system to do what deterministic authority policy permits.

That is the architectural essence of Gate House.