> For the complete documentation index, see [llms.txt](https://docs.inopli.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.inopli.com/drp/surface-web-monitoring/surface-web.md).

# Surface Web

The *<mark style="color:green;">Surface Web</mark>* monitors open, searchable parts of the internet to uncover brand misuse, exposed assets, and security-relevant traces that often precede incidents. Coverage spans public sites, code repositories, domain/certificate ecosystems, DNS and subdomains, cloud storage, and official/third-party app stores. The goal is to turn open-web noise into concrete findings tied to your brands, people, and infrastructure.

### What We Monitor

* <mark style="color:green;">**Public web & targeted searches**</mark>**:** Open pages and indexed content are queried for sensitive strings, exposed API documentation (Swagger/OpenAPI, Postman collections), and other security artefacts. Crawling of corporate sites helps reveal credentials, configs, and unintended data exposure. &#x20;
* <mark style="color:green;">**Public code repositories**</mark>**:** GitHub, GitLab, and Bitbucket are scanned for secrets, configuration files, and proprietary material, including, checks across recent commit history.&#x20;
* <mark style="color:green;">**Domains, certificates & look-alikes**</mark>**:** Subdomains and newly issued certificates are tracked to surface fresh assets and attack infrastructure. Look-alike risks are identified using domain-variation techniques and similarity analysis. &#x20;
* <mark style="color:green;">**DNS & infrastructure signals**</mark>**:** DNS records are validated to catch misconfigurations; exposed hosts/services are mapped and fingerprinted to identify technologies and versions in use.&#x20;
* <mark style="color:green;">**Cloud storage exposure**</mark>**:** Publicly accessible buckets and containers across S3, GCP, and Azure are discovered through optimized permutation strategies and assessed for sensitive content. &#x20;

### How We Detect

Findings are produced by combining OSINT scanning with validation steps that reduce false positives. Web searches feed into structured analysis pipelines; subdomains are enumerated. Domain look-alikes are generated and ranked by similarity models. DNS checks and service fingerprinting validate real exposure. cloud buckets are enumerated and verified for public access and sensitive files; app-store look-alikes are confirmed with textual and visual similarity methods.     &#x20;

#### Why This Matters

Open-web traces frequently foreshadow phishing, fraud, data leakage, or abuse of brand and infrastructure. Early visibility into misconfigured services, leaked secrets, look-alike domains, risky app listings, and exposed cloud storage enables rapid takedown and containment, strengthening the organization’s overall digital risk protection posture.