Built for the parts of service delivery that customers can actually feel.
IronNOC attaches to your Windows and Linux fleet across AWS, Azure, and GCP and runs a single compliance engine over all of it. When a control fails, the agent stages a full change record with its reverse command already computed, then pauses. Nothing production-facing executes on its own. If a change could knock a workload over at 3am, it sits in the mobile queue until a human reads it and taps approve.
Most managed services sell you a team and a shared Slack channel. Underneath, the work is still manual. Engineers paste runbook steps at 3am, write a post-mortem two weeks later, and retag a backlog of the same tickets when the drift comes back next quarter. Nothing compounds across that cycle.
IronNOC was built to strip the middle layer out. Automation handles the 90% that doesn't need a decision: cleanups, tag backfills, policy resets. Anything business-critical routes to the on-call approver with the plan attached. Humans come in where a human call is actually the point, which is a smaller share than most MSPs admit.
Every artifact follows a fixed shape. The agent treats each violation pattern as a bounded input and returns the same remediation plan byte-for-byte on every run against it. Jira, Splunk, a spreadsheet for the auditor. The downstream tooling reads every output in exactly one format, regardless of which tool is catching it.
How we think about the work.
Deterministic output or nothing
The engine runs at zero temperature against a bounded set of input shapes. Feed a given compliance failure in and the change plan that comes back is identical on every call. When an assessor digs into the history of a specific rule, the audit record reads flat across every prior run, which is the entire point of building it this way.
Governance is the primary surface
Most compliance pain lives after the workload is deployed. SOC 2 drift, CIS violations, stale identities, unencrypted resources. That's where IronNOC spends 90% of its cycles, across AWS, Azure, and GCP. Account adoption and day-two work are served by the agent as edge cases of the governance loop, not separate products.
Mobile push for every business-critical change
The agent never touches production without an explicit approval. Storage cleanups, tag backfills, and policy corrections go through on their own cadence. Things that could cost real money or block users land in the mobile queue with the plan, the reverse command, and the backup ID already attached to the record.
Self-healing, not self-deciding
The agent checks its own output against the compliance baseline after every run. If a change didn't close the violation the way the plan predicted, it replays the chain or escalates. What it never does is second-guess the baseline. That call belongs to the operator and stays with the operator.
Every action leaves an audit trail
Nothing the agent does is throwaway work. Every change stacks the same ordered chain of artifacts onto the record before the next run even starts. The audit evidence accumulates as a natural side effect of running the service, not a separate reporting step somebody has to keep up with on their own calendar.
Every engagement follows the same four phases.
Bring the environment in
We attach to your cloud accounts through read-only access first. The agent walks the resource graph: instances, tags, IAM surface, security groups, encryption state, key policies. No writes happen on this pass. What we're building is the picture the baseline check will run against.
Map the compliance surface
The current state gets graded against your target frameworks. SOC 2 CC6.1, CIS 4.1, HIPAA §164.312, PCI-DSS 3.4. Each failing control sits in a prioritised backlog alongside the framework citation, a risk score, and the exact plan the agent would run against it once write permission is granted.
Agent starts clearing violations
Non-business-critical violations clear on their own. Policy drift, unencrypted test buckets, tagging gaps, leftover test accounts. Business-critical items wait in the mobile approval queue with the full plan already attached for you to read. You tap once on your phone and the agent executes on the tap.
Continuous governance
The agent keeps your compliance posture under continuous read. New violations pick up the standard chain as soon as the detector flags them. Drift doesn't pile up between audits any more, and the evidence pack is always ready for whichever framework assessor shows up next.
The automation layer runs at the OS level on Windows and Linux, so it applies wherever the workload actually lives. S3 buckets, Azure storage accounts, and GCS buckets all get the same treatment the moment public exposure drifts beyond the baseline rule.
Publicly readable buckets, unencrypted volumes, wide-open ingress, missing MFA, over-privileged identities, missing tags, stale users, audit log gaps, and more. Every pattern in the library produces the same ordered artifact chain regardless of which cloud tripped it.
Every remediation carries the tag of the exact control article it closes. CC6.1, 4.1, §164.312, 3.4. No loose category mapping. The audit trail tells the assessor exactly which specific control was being enforced for any given change in the record.
Solo founder, built the automation layer from scratch. Currently running IronNOC full-time out of Ghaziabad. Early access phase is open to the first three design partners at founder pricing, and onboarding runs through the founder directly rather than a support tier.
If the current model feels fragmented, start there.
That single framing tends to reveal more about the real next step than a scoped RFP ever does.
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