AI systems increasingly sit at the center of decision-making, automation, and risk. Model access, inference permissions, and usage constraints are no longer experimental concerns — they are governance obligations.
Hexarch is designed for environments where who is allowed to run which model, for what purpose, under which conditions, and for how long must be enforced continuously and auditable over time.
The Problem
Most organizations deploy AI systems faster than they can govern them.
Common breakdowns include:
Model access granted informally
API keys and endpoints proliferate without durable approval or expiry.
Usage constraints exist only in policy documents
Intended limits (purpose, scope, data sensitivity) are not enforced at runtime.
No authoritative record of model decisions
When incidents occur, teams cannot explain who approved usage, under which rules, or why access was still active.
Governance lives outside the system
Controls are tracked in spreadsheets, wikis, or GRC tools disconnected from enforcement.
These are not maturity issues — they are architectural gaps.
How Hexarch Helps
Hexarch introduces a control plane that treats AI access and usage as a governed lifecycle, not a static API credential.
Model Access as a License Lifecycle
Model usage is granted through explicit licenses that define:
- which model or capability
- permitted scope and purpose
- duration and expiry
- conditions for renewal or revocation
Access exists because authority exists — and disappears when it does not.
Continuous Enforcement at Runtime
Licenses and policies are synchronized directly into enforcement layers so:
- expired approvals fail closed
- revoked access is immediate
- runtime behavior always reflects current authority, not stale configuration
Governance is enforced continuously, not reviewed retroactively.
Auditability Without Narrative Reconstruction
Every proposal, approval, issuance, renewal, and revocation emits immutable audit events.
This creates a durable record of:
- who approved model usage
- when and why access was granted
- what changed over time
- when access expired or was revoked
Intent, authority, and enforcement remain linked.
AI-Assisted Governance (Advisory, Not Autonomous)
AI assists humans by:
- summarizing approval context
- explaining why access exists or was revoked
- highlighting drift between policy intent and runtime usage
- generating review narratives for audits
AI never overrides enforcement or grants access.
Typical Use Cases
Hexarch is well suited for AI governance environments including:
Internal AI platforms
Governing access to shared models across teams and business units.
Model marketplaces
Enforcing entitlement, expiry, and usage limits for customers or partners.
Regulated inference systems
Ensuring model usage aligns with approved purposes and regulatory constraints.
AI-enabled decision systems
Maintaining traceability for model-driven outcomes in audits or investigations.
Why This Matters
AI governance failures are rarely about model quality. They are about authority drift.
Hexarch replaces informal controls and post-hoc explanations with explicit, enforceable governance, allowing organizations to scale AI usage without scaling compliance risk.
Hexarch is built for AI systems where model access and usage must be enforced, explained, and defended — not merely documented.