How do you catch compliance drift the day it appears?
A compliance AI Employee that sweeps your cloud infrastructure against policy every day for public buckets, untagged resources, and overly broad roles, files each finding where your team already works, and proposes the fix as a reviewed change that a named human applies, never applying it itself.
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Infrastructure drifts out of compliance quietly. A bucket gets made public for a one off, a resource ships without its tags, a role picks up a permission it no longer needs. Each change is small and reasonable in the moment, but they accumulate, and nobody notices until an audit or an incident surfaces them all at once. By then, reconstructing when each drift happened, and why, is the hard part. The common approaches each fall short. A quarterly manual audit finds drift late and in bulk, when the context is hardest to recover. A fixed rules engine catches only the checks it was configured for and nothing beyond them. And automatic remediation that fixes drift on its own can break a service that depended on the very configuration it 'corrected.' What's needed is a check against policy every day, with findings filed where the team already works and fixes proposed as reviewed changes rather than applied silently.
The compliance AI Employee runs a sweep once a day inside your own environment, with read only access to your cloud infrastructure and audit logs. It checks resources against policy for public buckets, untagged resources, and overly broad roles, and files what it finds to Slack. Remediation is proposed as a reviewed change and pauses for a named human's signature; nothing is applied automatically. It detects and proposes; your people apply.
See exactly how the work gets done.
Runs on a daily schedule
A scheduled run fires once a day. Each run starts clean in its own environment, so every day's check works from current infrastructure state rather than a cached picture of yesterday's.
Checks against your policy, not a fixed rule set
What counts as a violation travels with the AI Employee as a skill: which buckets may be public, which tags are required, and what a role should and shouldn't hold. It checks against your policy and picks up changes the moment you update it.
Connects to your systems, with permissions you set
It reads bucket policies, resource tags, and IAM roles from your cloud infrastructure, reads the audit logs to see when a configuration changed and what changed it, and posts findings to the Slack channel your team watches. Credentials stay in memory, never written to disk, never exposed to the model.
Surfaces the drift, proposes the fix
Its access to the cloud is read only. It inspects state, it never changes it. A newly public bucket becomes a finding and a draft policy change, an untagged resource a flag and a proposed tag set, an overly broad role a narrower policy held for review, each attached for a person to apply.
Pauses for a named human before anything changes
Every remediation stops for a named human's signature. A person reviews each proposed change and is the one who applies it; nothing is ever applied automatically.
Runs in your environment
Every sweep is isolated inside your own infrastructure. The data never leaves it; only the findings it files and the fixes it proposes do.
Read only on the infrastructure
Its access to the cloud and audit logs is read only. It inspects state and cannot change a bucket policy, a tag, or a role.
Every fix waits for a signature
Remediation is proposed, never applied automatically. A named human reviews each proposed change and is the one who applies it.
Credentials stay contained
Each credential connects with the permissions you set, stays in memory, is never written to disk, and is never exposed to the model or the logs.
You own every rule
The compliance policy, the skills, and the per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The drift that used to surface in bulk at audit time now arrives as small daily findings in Slack, each with a proposed fix a named human reviews before it's applied. The team reviews a change instead of reconstructing months of drift, and the infrastructure stays close to policy. The AI Employee detects and proposes; your people decide and apply.
Daily
Infrastructure rechecked against policy every day
Same day
Drift caught the day it appears, before it accumulates for an audit
0 automatic fixes
Every remediation proposed as a change and held for a named human to apply
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
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