From Infrastructure Data
to Governed Decisions
The mechanism, end to end — and read-only first. AuthorIOM discovers your real environment, builds authority against it, validates every change before it runs, and lets AI act through the same gate. Nothing touches production until the model is proven against your reality. That same gate is what makes AIOps trustworthy — every AI action validated before it runs.
Discover Reality
Connectors ingest live state from cloud providers, IaC systems, identity, CI/CD, and observability. The modeling engine builds a canonical graph of resources, ownership, dependencies, and intent.
Reconciliation runs in read-only mode. Initial drift surfaces — typically 15–30% of resources reveal ownership or dependency gaps.
Build Authority
Policy is authored against the model, working from existing policy-as-code where present. The reconciliation engine compares declared intent against observed state continuously.
Authority is validated against historical change decisions before any production gating. The model either fits your reality — or it does not — and you find out before exposure.
Govern Change
IaC pipelines, change controllers, and agents call the Decision API before executing. The change proceeds, or returns with a structured denial reason.
Every decision is recorded against the model. Audit becomes a query, not a forensic reconstruction across logs and tickets.
Enable Trusted AI
Agentic AI is authorized against the operating model. Approved actions execute at machine speed; denied actions surface with reason.
100% of agent actions governed — without slowing them. AI recommendations are grounded in explainable system context.
Agentless. Read-only first. It sits in front of the tools you already run.
AuthorIOM ingests through provider APIs and your existing pipelines — no agents on hosts, nothing in the data path until you choose to gate. It connects to the systems that already hold pieces of the truth — ServiceNow today, with additional connectors built as your environment requires:
Cloud provider APIs — AWS, Azure, GCP — plus hybrid and on-prem inventory. Resources, configuration, and live state.
Terraform, OpenTofu, Pulumi, CloudFormation, Ansible, and CI/CD — GitHub Actions, GitLab, Jenkins. Declared intent and the change stream before it runs.
Okta, Entra ID, and cloud IAM. Who owns what — and who is permitted to change it.
ServiceNow and the asset systems you already maintain. Intent, approvals, and your system of record.
Datadog, Splunk, Prometheus. Observed behavior, reconciled against declared and permitted state.
OPA/Rego, Sentinel, and existing policy engines. AuthorIOM builds on the rules you have written rather than replacing them.
Read-only first — reconciliation runs against observed state to prove the model before anything writes. Once governance is on, the same connectors run both ways: AuthorIOM reads from each system, acts on it, and keeps the model in sync with changes made outside it.
ServiceNow
AuthorIOM opens tickets directly in ServiceNow. The connection also works in the other direction: a request submitted in ServiceNow can initiate an action in AuthorIOM — provisioning new infrastructure, such as an Azure subnet — which is validated against the model before it executes.
Terraform & CI/CD
The same two-way pattern extends to IaC and pipelines as connectors are built: AuthorIOM updates infrastructure code and pushes it to your repository, and a change made to the repository directly notifies AuthorIOM to update the model — keeping it aligned with what is actually deployed.
Every actor — human, automation, or AI agent — calls the same Decision API. The pipeline is the actor; the Infrastructure Operating Model is the authority.
One gate. Every actor calls it before a change runs.
IaC pipelines, change controllers, and AI agents send a proposed change to the Decision API and get a synchronous answer: allow or deny, the policy that fired, and a structured reason. Approved changes proceed at pipeline speed; denied changes never reach infrastructure. Every call is recorded against the model, so audit is a query — not a forensic reconstruction.
POST /v1/decisions
{
"actor": "ci/terraform-apply",
"action": "modify",
"resource": "prod/payments-db",
"change": { "instance_class": "db.r6g.large → db.t3.medium" }
}
→ 200 OK
{
"decision": "deny",
"policy": "prod-db-no-downsize",
"reason": "Downsizes a production database below the approved tier. Owner: payments-platform. Blast radius: 3 dependent services.",
"model_ts": "2026-06-21T14:02Z"
}Pre-apply hooks
IaC calls the API before apply; the plan proceeds only on an allow.
Change controllers
Pipelines and admission points gate on the same decision, the same way.
Agent middleware
AI agents route proposed actions through the API before they execute.
The Operating Difference
AuthorIOM is not a dashboard layer. It is a decision-quality layer for execution across teams and tools — the integration layer above CMDBs, observability, automation, and policy engines.
Compliance Positioning
AuthorIOM supports continuous compliance readiness by making infrastructure authority current, explainable, and auditable. Audit response moves from weeks to hours.
Questions engineers ask.
Is AuthorIOM agentless?
Yes. It connects through cloud provider APIs and your existing pipelines — no agents installed on hosts. Reconciliation runs read-only first, so nothing writes to infrastructure until you choose to turn governance on.
What does AuthorIOM integrate with?
The systems that already hold pieces of the truth: cloud and on-prem infrastructure, infrastructure-as-code and CI/CD, identity and IAM, ITSM and CMDB, observability, and existing policy-as-code. It sits above them as the layer that validates change, not a replacement for any of them.
Does AuthorIOM only read from my tools, or can it act on them?
Both. It reads from each connected system to build and maintain the model — read-only first. Once governance is on, the same connectors run both ways: it can open a ticket in ServiceNow, push updated code to your repository, or initiate a provisioning action — while changes made directly in those tools notify AuthorIOM to update the model, keeping it in sync.
Does governing change slow down deployments?
No. The Decision API is a synchronous pre-apply check: approved changes proceed at pipeline speed, and only changes that would violate policy are stopped — with a structured reason returned to the pipeline or agent.
Do we have to rewrite our existing policies?
No. Policy is authored against the model and builds on the policy-as-code you already have, validated against your historical change decisions before anything is gated in production.
How is audit handled?
Every decision — allow or deny, and the policy that fired — is recorded against the model. Audit becomes a query against that record instead of a forensic reconstruction across logs and tickets.
How does AuthorIOM relate to AIOps platforms?
AuthorIOM sits beneath your AIOps stack as the model it runs on. It ingests from observability and AIOps tools as signal, and returns an allow or deny decision before any change executes — the pre-execution authority that AIOps tools do not hold.
Related reading
The Authority Gap
The space between what your tools do and what governance requires — and what falls through it.
Read →Why AI Fails
Agents act at machine speed against context they cannot see. The model is the missing guardrail.
Read →Board Brief
The one-page case for treating infrastructure authority as a board-level concern.
Read →Every Enterprise Needs an Authority Layer.
Start Here.
Read-only modeling proves authority before any governance is activated. No production risk until the model is validated.