CYBERSECURITY

Cloud Security Posture & Vulnerability Remediation Prioritization

Cut the alert pile down to the bugs that are actually exploitable.

Cybersecurity engineering teams are overwhelmed by thousands of vulnerability alerts generated by static code scanners, container image checkers, and cloud configuration tools. By uploading raw scan exports, CVE threat intelligence feeds, asset criticality databases, and patch deployment records, security leads can prioritize remediation on actual business exposure rather than raw severity score.

The platform maps vulnerability severity against asset network accessibility and application dependencies, isolating the vital fraction of bugs that pose genuine exploitable risk. Developers can query the dataset for the exact files needing urgent patching, so teams eliminate security noise, streamline patching workflows, and harden infrastructure efficiently.

PRIORITIZE

Talk to Your Scan Data

exploitableasset exposure

Which CVEs are genuinely exploitable?|

Find Insights

• 41% of criticals sit on "internal-only assets"

• "internet-exposed + sensitive DB" is the real 6%

Enterprise RAG & Custom Search

Use Steeped AI's enterprise search engines to let security engineers instantly search CVE databases and internal codebase logs in plain language. Ask which services import a vulnerable package and get the exact files back with citations.

AI Topic Mapping

Use Steeped AI's AI topic mapping to turn scanner output and advisory text into measurable classes like deserialization risk, misconfiguration, and exposed secret. Alert triage stops depending on who happens to recognize a CVE.

Data Preparation

Use Steeped AI's data preparation to reconcile scan exports, asset inventories, and patch records into one clean dataset, unifying asset identifiers across tools. The same host named three ways by three scanners becomes one asset.

Scans from 4 tools were merged into one schema

Asset IDs across scanners were reconciled to one host

CVE records were joined to live threat intelligence

Removed column "legacy_scan_tag" because 86% were blank

Duplicate findings for one asset were merged

generating new dataset

NEW DATA

Relational Metrics

Steeped AI's automated metric breakouts calculate the intersecting risk between severity score, asset internet exposure, and database sensitivity. Real deterministic math replaces the spreadsheet a security lead rebuilds every sprint.

Base Column

finding_record ▾

Value Column

exposure_signal ▾

Base Column finding_record ×Value Column ×

41%offinding_record=internal_only

flag for review

exploit risk

22%

flagged count

533

finding count

1,300

see examples

6%offinding_record=exposed_sensitive

flag for review

exploit risk

100%

flagged count

finding count

1,300

see examples

Predictive Power Score

Steeped AI's regression predictive power determines which vulnerability types most strongly predict actual exploit attempts in production. Patch queues get ordered by evidence rather than by CVSS alone.

Expand

Rank Exploit Predictors

Exposed + Sensitive

48%

Known Exploit Kit

36%

Unpatched 90d+

28%

High CVSS Alone

Base Column

vuln_class ▾

Value Column

exploit_attempt ▾

48%ofExposed+Sensitive=Exploit Attempt

predictive power

92%

attempt count

144

finding count

300

see examples

Insight Deeplinks

Click any prioritized finding to open what sits beneath it with insight discovery deeplinks: the column values, the relational metrics, and the raw scan records behind the score. An engineer follows one CVE into the next before opening a ticket.

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.