Security That Knows.
Not Security That Guesses.
Most security infers trouble from flow and signals after a change has already run. AuthorIOM works from a continuously synchronized model of running state, governs what is allowed, and can execute and verify approved remediation. This is not a security product bolted onto infrastructure. It is what security looks like when the environment is modeled: the model already knows what is intended, so what is permitted is a question it can answer.
Compare observed state to intended state before a symptom becomes an incident.
What should exist
Permitted topology, ownership, policy, configuration, and transitions.
Compare expected state, observed state, and connected change.
What is happening
Identity, configuration, traffic, infrastructure state, and recent change.
Compare flow to intent — and the false positives disappear.
A flow on its own is ambiguous. The same connection can be routine or hostile depending on what was supposed to happen. Because the IOM holds declared intent for every resource, security can compare observed flow against intended behavior — turning a stream of anomalies into a short list of real signals.
Expected
Behavior that aligns with the model is filtered out. No alert, no noise, no analyst time.
Real signal
Behavior that contradicts the model is escalated with full context — ownership, dependencies, and blast radius.
A gap to close
Activity with no modeled intent is flagged to be reconciled — never silently ignored.
The result is fewer false positives, less alert fatigue, and analysts working real deviations instead of chasing smoke.
Nothing deploys until every guardrail is met.
Traditional security responds after a change has executed and damage has surfaced. AuthorIOM checks connected change against policy, ownership, dependencies, and intent; denies what is unsafe; executes approved remediation; and verifies the outcome against running state.
From forensics to prevention.
- Validation before execution, not forensics after the incident
- Real signals from the model instead of inferred anomalies
- Blast radius known before a change ever runs
- Flow compared to intent — false positives filtered out
- Ground-truth state of every resource, continuously reconciled
- Built-in guardrails work out of the box; advanced teams can connect existing security, SIEM, and CNAPP platforms
Run it as SaaS — or inside your own perimeter.
AuthorIOM is agentless either way: it connects read-only to start, with nothing to install on your hosts. It runs as SaaS — a shared tenant with your data segregated, or your own dedicated tenant — or, for organizations with stricter security or data-residency requirements, inside your own cloud or on-prem: the same operating model, inside your perimeter.
SaaS
A shared tenant with your data segregated, or your own dedicated tenant. Agentless and read-only to start — nothing to host, nothing to maintain.
Self-hosted — your cloud or on-prem
For organizations whose security review or data-residency rules require it, AuthorIOM runs inside your own cloud instance or on-prem — keeping the model and its data within your boundary.
Related reading
Works With Your Stack
Keep ServiceNow, Terraform, and your ITSM — AuthorIOM governs above them. No rip-and-replace.
Read →Why Zero Trust Is not Enough
Identity decides who connects. It never decides whether a change is admissible against the model.
Read →How It Works
How AuthorIOM validates connected change against the model before it reaches infrastructure.
Read →Secure AI-Ready Infrastructure
Define bounded authority, human gates, permitted action, fail-safe behavior, and verification for AI.
Read →Questions we hear a lot.
How does an Infrastructure Operating Model make security proactive?
Flow-based tools infer trouble after the fact by watching traffic and logs. An IOM holds the authoritative state, intent, ownership, dependencies, and policy of every resource and validates connected change before it executes, so non-compliant changes are denied before they run instead of investigated after an incident.
How does comparing flow to intent reduce false positives?
Because the IOM holds declared intent for every resource, observed flow can be compared against intended behavior. Traffic that matches intent is filtered out and only deviations from the model are escalated, which removes the noise and alert fatigue that flow-only tools generate.
See security move before the incident.
Find out where your environment is governed by the model — and where it still relies on detecting smoke.
Proactive security is what the operating model makes possible. See the operating model and how it works.