Cloud Misconfiguration Detection and Alerting
Cloud environments drift constantly — a developer opens a security group for a debugging session and forgets to close it, a storage bucket gets created with default permissions that are more permissive than the team realizes, an IAM role granted for a one-off migration never gets revoked. Native cloud consoles and periodic manual reviews catch a fraction of this, and the gap between when a misconfiguration is introduced and when it's noticed is often weeks, which is exactly the window an opportunistic scanner or a researcher running mass internet scans will find first.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 10-15 hrs/week of manual cloud config review across accounts.
How the automation works
We continuously monitor cloud configuration state across your AWS, Azure and GCP accounts against a defined secure baseline — public storage access, overly permissive security groups, unencrypted resources holding sensitive data, and IAM roles with unused broad permissions — and rank findings by actual exposure, not just rule violation: a public bucket with production customer data ranks above a public bucket that only ever held static marketing assets. Findings route to the owning team with the specific resource and drift detail, and anything reachable from the public internet with sensitive data exposure gets an immediate high-priority alert rather than sitting in a weekly digest. Auto-remediation is available only for a pre-approved, low-risk rule set — reverting a clearly accidental public ACL, for instance — and every auto-remediation action is logged and reversible; anything with a plausible legitimate use case routes to a human for a judgment call before anything changes.
Process flow
- 01
Cloud config change detected trigger
Configuration changes across monitored AWS, Azure and GCP accounts are picked up continuously via cloud-native config change feeds, not on a periodic scan schedule alone.
- 02
Evaluate against secure baseline ai
Each change is checked against a defined secure baseline covering public access, encryption, permissive security groups and unused broad IAM grants.
- 03
Rank by actual exposure ai
Violations are ranked by real exposure — what data or system the misconfigured resource actually holds or reaches — not just by which rule was violated, so a public bucket with customer PII outranks one with static assets.
- 04
Route to owning team with drift detail integration
Findings are routed to the team that owns the resource, with the specific configuration drift and remediation guidance attached, based on resource tagging and account ownership mapping.
- 05
Auto-remediate pre-approved low-risk rules only output
Only a pre-approved, narrow set of clearly accidental misconfigurations is auto-reverted, logged, and reversible; anything with a plausible legitimate use routes to a human for a decision.
- 06
Report posture trend over time output
Open findings, mean time to remediation, and recurring misconfiguration patterns by team are reported so systemic issues, not just individual incidents, get addressed.
Inputs
- Cloud provider config change feeds (AWS Config, Azure Policy, GCP SCC)
- Secure configuration baseline
- Resource ownership and tagging data
- Data sensitivity classification per resource
Outputs
- Exposure-ranked misconfiguration findings
- Team-routed remediation tickets
- Auto-remediation log with reversal record
- Cloud security posture trend report
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Ranking every rule violation with equal urgency floods teams with alerts and trains them to ignore the queue — exposure-based ranking, weighted by what data or system a misconfigured resource actually reaches, is what makes the alerting worth acting on instead of tuning out.
- Resource tagging and ownership metadata is often incomplete or stale in fast-moving cloud environments, and a finding routed by tag alone to a team that no longer owns that resource sits unaddressed while the actual owner never sees it.
- Auto-remediating a security group or IAM permission that looks wrong can break a legitimate integration someone set up deliberately — auto-remediation needs to be scoped to a narrow, pre-approved rule set with a clear reversal path, never applied broadly on rule-violation alone.
- A misconfiguration introduced by infrastructure-as-code (a bad Terraform module, a copied CloudFormation template) will keep reintroducing itself on every deploy if the detection only catches the live resource state and never flags the source template — recurring findings from the same IaC source need root-cause routing back to the code, not repeated one-off fixes.
Frequently asked questions
Does this automatically fix every misconfiguration it finds?
No — only a narrow, pre-approved set of clearly accidental low-risk issues is auto-remediated, logged and reversible. Anything with a plausible legitimate use case is routed to a human for a decision.
How does it prioritize findings across hundreds of resources?
By actual exposure, not rule count — a public resource holding sensitive production data is ranked far above the same rule violation on a resource that holds nothing sensitive.
Does it cover multi-cloud environments?
Yes, it monitors AWS, Azure and GCP configuration state against one defined secure baseline, so teams running across multiple providers get a single ranked findings queue instead of three separate consoles.
What happens when the same misconfiguration keeps reappearing?
Recurring findings are flagged separately and traced back to their source where possible — often an infrastructure-as-code template — so the fix addresses the root cause instead of the same instance repeatedly.