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Why Octic

AI infrastructure is ungoverned. That is the problem we built Octic to solve.

Most organizations know they have an AI governance problem. Agents multiply untracked. Models connect to data nobody approved. Endpoints appear and disappear between audits. The tools built for traditional infrastructure were never designed for this.

Octic is an AI control plane. It maps the AI in your estate at the API, network, and kernel layers, decides what is trusted, shows what AI does at runtime, and turns each risk into a finding with an owner. Agentic fixes your team approves are coming late 2026.

01 — The challenge

The problem is structural, not tactical.

Shadow AI spreads quietly

Any team with a credit card can spin up an AI agent. Any developer with an API key can deploy an MCP server, and any engineer can install a coding agent on a laptop. The result is AI running in production and on laptops that no central team knows about.

API-only scanners catch what is registered. They miss the agent a contractor deployed last Tuesday, the model proxy running on a personal AWS account, the MCP server someone stood up in a dev namespace and forgot to tear down. By the time these show up in an audit, the risk has been live for a while.

Coding agents add a new version of the problem. Is anyone running Claude or Codex on a personal account? What did last month's token bill buy? Without visibility on each host, neither question has a good answer.

Compliance gaps widen every quarter

Auditors and reviewers are asking new questions. Which AI models have access to customer data? Who approved this AI agent? Too often the answer is a spreadsheet someone last updated three months ago.

The gap between what reviewers expect and what organizations can show grows with every new AI agent. Manual inventories go stale as soon as they are written. Point-in-time scans give you a snapshot, but AI infrastructure changes between scans.

The problem is not that teams lack good intentions. The problem is that existing tools were built for static infrastructure. AI resources are dynamic, interconnected, and multiplying faster than anyone can track by hand.

Nobody owns the problem

Security says it is an engineering problem. Engineering says it is a platform problem. Platform says they did not know those agents existed. The CISO asks for an inventory and gets three conflicting lists.

Without a system that derives ownership, every AI resource is an orphan. Orphans accumulate risk. They connect to models nobody approved, call APIs nobody monitors, and process data nobody consented to share.

This is not a people failure. It is a tooling failure. You would not run a production Kubernetes cluster without a control plane. Why run AI agents across the organization without one?

The gap in existing tools

API-only discovery is table stakes. It is not governance.

Most tools in this space do one thing: query cloud provider APIs and return a list of AI resources. That covers what is registered. It misses what is not — the agents deployed outside official channels, the MCP servers running in dev namespaces, the model proxies hiding behind generic service names.

Even when these tools find resources, they stop at the inventory. No ownership assignment. No trust evaluation. No policy enforcement. No remediation. You get a list and a dashboard — and a Jira ticket for someone to manually triage every finding.

Octic is not another scanner. It is a control plane. Discovery is the starting point, not the product. The value is in what happens after discovery: derived ownership, managed and custom policies, runtime insight down to the kernel, and findings with owners and deadlines. Agentic remediation your team approves is coming late 2026.

02 — How Octic helps

A control plane, not another dashboard.

Continuous visibility, not periodic scans

Octic discovers AI continuously at three layers: API, network, and kernel. Kernel-level sensors on servers and laptops (Linux and Windows today) see the AI apps, packages, and processes on each host. Network detection catches what no API lists, such as unregistered MCP servers and shadow model proxies. Coverage grows as you roll sensors out.

How discovery works

Trust out of the box

Octic-managed policies apply from the start, with enhanced protections, and your custom policies run alongside them. A multi-level ownership engine derives who owns each AI resource, and every resource is approved, unreviewed, or flagged. The goal is simple: nothing in your estate without someone accountable for it.

How trust works

Evidence for reviews

Octic keeps a living map of your AI estate, with ownership, trust decisions, and policy history. When a reviewer asks what AI is running, who owns it, and who approved it, the answer is already on record. Evidence is a byproduct of how the platform works, not a report you assemble by hand.

How trust works

What coding agents do, and what they cost

Agentic Coding Insights shows each engineer's Claude Code and Codex sessions, the prompts behind them, and spend split into categories like feature work and docs. It runs on every host with an Octic sensor, with no per-app setup, and flags sessions on personal accounts. More coding agents are coming.

See Agentic Coding Insights

Findings with owners, fixes coming late 2026

When Octic finds a risk, it opens a finding with an owner, an SLA, and an event history you can export as CSV. An exception is time-limited and needs an admin's approval. Coming late 2026: remediation agents that propose a specific fix, such as quarantining a flagged AI agent or revoking credentials, that runs only after someone on your team approves it.

How remediation works

AI infrastructure will only grow more complex. The organizations that govern it effectively will move faster — not slower — because they will know exactly what is running, who owns it, and whether it is safe. That is what Octic delivers. Discover. Trust. Observe. Remediate. Continuously.

See what's running, what it costs, and who owns it.

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