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Introduction:
Penetration testing results from bug bounties, external audits, and internal red teams often arrive in scattered spreadsheets, lacking the cloud context needed for real prioritization. Wiz’s new Penetration Test Findings feature unifies all offensive security outputs into a single platform, mapping each finding to live cloud resources and automating ownership assignment. This turns fragmented data into actionable intelligence, enabling security teams to stop chasing reports and start closing critical gaps.
Learning Objectives:
- Aggregate pen‑test findings from bug bounties, AI assessments, and internal audits into a unified view for risk‑based prioritization.
- Leverage code‑to‑cloud mapping and cloud context on the Wiz Security Graph to identify exploitable paths and correct owners.
- Implement AI‑powered triage and automated remediation workflows to reduce manual noise and accelerate fix cycles.
You Should Know
- Unified Findings Ingest via Wiz API – Consolidate All Sources
Most teams juggle CSV exports from Bugcrowd, PDFs from auditors, and raw logs from internal tests. Wiz’s API allows you to ingest these findings programmatically, normalizing them into a common schema.
Step‑by‑step guide:
- Obtain your Wiz API token from the service account settings.
- Use `curl` to submit a finding (example for a critical SQL injection from a bug bounty):
curl -X POST "https://api.wiz.io/v1/findings" \
-H "Authorization: Bearer $WIZ_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"source": "bug_bounty",
"title": "SQL Injection in /api/users",
"severity": "CRITICAL",
"cloud_resource_id": "arn:aws:ec2:us-east-1:123456789012:instance/i-abc123",
"remediation": "Use parameterized queries",
"owner_email": "[email protected]"
}'
- For bulk uploads from a CSV, use a Python script:
import requests, csv
url = "https://api.wiz.io/v1/findings"
headers = {"Authorization": f"Bearer {token}"}
with open('findings.csv') as f:
for row in csv.DictReader(f):
requests.post(url, json=row, headers=headers)
This unifies all findings in Wiz’s dashboard, tagging each with its source (bug bounty, audit, pen‑test, AI) for later filtering.
- Mapping to Cloud Resources via the Security Graph
Raw findings become real risks when you see which cloud assets are actually affected. Wiz’s Security Graph connects every finding to live resources (EC2, S3, Lambda, etc.) and their relationships.
Step‑by‑step guide – querying graph for prioritization:
Use the Wiz GraphQL API to find all unpatched critical findings on internet‑facing EC2 instances:
query {
findings(
filter: { severity: CRITICAL, status: OPEN }
relationTo: { resourceType: EC2, hasPublicIp: true }
) {
title
resource { name, region, publicIp }
remediationSteps
}
}
Command‑line with `curl` and `jq`:
curl -X POST https://api.wiz.io/v1/graphql \
-H "Authorization: Bearer $WIZ_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"query":"query{findings(filter:{severity:CRITICAL}){title resource{id}}}}' \
| jq '.data.findings[] | {title, resource_id}'
Map each finding to a code repository using Wiz’s code‑to‑cloud labels – for example, an S3 bucket misconfiguration might point directly to the Terraform module that created it.
3. AI‑Powered Triage – Reduce Noise by 70%
Manual triage of duplicate or false‑positive findings wastes hours. Wiz’s AI engine clusters similar issues, deduplicates, and auto‑adjusts severity based on real‑time cloud context.
Step‑by‑step guide to simulate AI triage with a local script (using OpenAI or a rules engine):
1. Export recent findings as JSON.
- Run a Python script that embeds titles and clusters them:
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
findings = [{"id":1,"title":"S3 bucket public read"}, {"id":2,"title":"publicly readable S3 bucket"}]
embeddings = model.encode([f['title'] for f in findings])
duplicates = util.paraphrase_mining_embeddings(embeddings)
print("Potential duplicates:", duplicates)
- For production, integrate Wiz’s native AI triage by setting up a webhook. When a new finding arrives, Wiz automatically:
– Compares it against 10,000+ historical findings.
– Suppresses false positives (e.g., scanner‑generated noise).
– Raises priority if the affected resource holds PII or is directly linked to a public endpoint.
Windows PowerShell alternative for log parsing:
Get-Content findings.json | ConvertFrom-Json | Group-Object -Property title | Where-Object {$<em>.Count -gt 1} | ForEach-Object { Write-Host "Duplicate: $($</em>.Name)" }
4. Ownership Mapping & Automated Remediation Workflows
Knowing who owns a misconfigured bucket is half the battle. Wiz maps each resource to its IAM user, tag, or CI/CD pipeline owner, then pushes remediation tasks to Jira, Slack, or ServiceNow.
Step‑by‑step guide – set up automated ticket creation:
- In Wiz, go to Settings → Integrations → Jira.
- Provide your Jira URL, API token, and project key.
- Create a rule: When a CRITICAL finding is detected on a resource tagged with “owner:platform-team” → create Jira issue.
- The ticket includes the finding title, remediation steps, and a direct link to the cloud resource.
Manual CLI approach to fetch owner and notify Slack:
Get owner from AWS tag
aws ec2 describe-instances --instance-ids i-abc123 --query 'Reservations[bash].Instances[bash].Tags[?Key==<code>Owner</code>].Value' --output text
Send Slack alert
curl -X POST -H 'Content-type: application/json' \
--data '{"text":"CRITICAL: SQLi found on instance i-abc123 owned by team-platform"}' \
https://hooks.slack.com/services/YOUR/WEBHOOK
Automation reduces mean‑time‑to‑resolve (MTTR) from days to hours.
5. Code‑to‑Cloud Mapping for Developer‑Friendly Remediation
Developers need to see where a security issue originated in code. Wiz correlates findings with source code repositories, showing the exact line and commit.
Step‑by‑step – integrate SAST results and map to cloud:
- Run a SAST scanner (e.g., Semgrep) on your CI pipeline:
semgrep --config=p/owasp-top-ten --json -o sast_output.json ./src
- Use Wiz CLI to upload findings with repository context:
wiz findings upload --source sast --file sast_output.json --repo https://github.com/company/api --commit 7a8f3d
- Wiz automatically links the vulnerable code to the deployed cloud resource (e.g., a Lambda function built from that commit).
- Developers see in the Wiz dashboard: “Fix this SQL injection in `api/users.py` line 42 → commit
7a8f3d”.
Linux command to find recent commits affecting a file:
git log -p --follow -S "sql_query" -- src/api/users.py
This bridges the gap between AppSec and cloud security, enabling developers to fix what they broke.
6. Cost‑Effective Open Source Alternative – DefectDojo Integration
For budget‑constrained teams, DefectDojo offers a free, self‑hosted unified platform for managing pen‑test findings. (As noted by Karan Rajeshbhai Mungara, DefectDojo Pro’s dark mode isn’t needed – the free version works.)
Step‑by‑step – deploy DefectDojo on Linux (Docker):
Install Docker and Docker Compose sudo apt update && sudo apt install docker-compose -y Clone DefectDojo git clone https://github.com/DefectDojo/django-DefectDojo cd django-DefectDojo Start containers docker-compose up -d Access at http://localhost:8080 (default admin: admin / changeme)
Import findings from a Wiz CSV export:
DefectDojo accepts generic JSON/CSV. Write a small converter:
import csv
with open('wiz_export.csv') as infile, open('defectdojo_import.csv','w') as outfile:
reader = csv.DictReader(infile)
writer = csv.DictWriter(outfile, fieldnames=['','Description','Severity','Mitigation'])
for row in reader:
writer.writerow({'':row['finding_title'], 'Description':row['details'],
'Severity':row['risk_level'], 'Mitigation':row['remediation']})
Then upload to DefectDojo via its API or UI. While lacking Wiz’s real‑time cloud mapping, DefectDojo is excellent for aggregating static reports.
- Cloud Hardening Based on Pen‑Test Findings – Real Examples
Once findings are unified, act on the most common cloud misconfigurations discovered during pen tests. Below are actionable commands to harden an AWS environment using findings from Wiz.
Scenario A: Unencrypted S3 bucket found in a pen test
Remediate with AWS CLI:
Enable default encryption (AES-256)
aws s3api put-bucket-encryption --bucket my-bucket --server-side-encryption-configuration '{"Rules":[{"ApplyServerSideEncryptionByDefault":{"SSEAlgorithm":"AES256"}}]}'
Block public access
aws s3api put-public-access-block --bucket my-bucket --public-access-block-configuration BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true
Scenario B: Open RDP (port 3389) to 0.0.0.0/0 on Windows EC2
Use AWS security group modification:
aws ec2 revoke-security-group-ingress --group-id sg-12345678 --protocol tcp --port 3389 --cidr 0.0.0.0/0 aws ec2 authorize-security-group-ingress --group-id sg-12345678 --protocol tcp --port 3389 --cidr 192.168.1.0/24
Scenario C: Excessive IAM permissions (e.g., “” actions)
Apply least privilege with a policy generator:
aws iam put-role-policy --role-name vulnerable-role --policy-name RestrictivePolicy --policy-document file://restricted.json
Example `restricted.json`:
{
"Version": "2012-10-17",
"Statement": [{"Effect":"Allow","Action":["s3:GetObject"],"Resource":"arn:aws:s3:::my-bucket/"}]
}
Automate these remediations by triggering AWS CLI commands from Wiz webhooks – turning findings into self‑healing infrastructure.
What Undercode Say
- Unified visibility eliminates blind spots. Aggregating pen‑test, audit, and bug bounty findings in one platform with live cloud context makes risk prioritization actually work.
- AI triage is not hype – it reduces alert fatigue. By clustering duplicates and correlating with resource criticality, teams stop chasing false positives and focus on true exposures.
- Ownership mapping accelerates remediation. Linking each finding to a specific IAM user, tag, or code commit slashes the time spent on “who owns this?” from hours to seconds.
The real innovation is moving from static reports to dynamic, graph‑driven security. Wiz’s approach mirrors what mature DevSecOps teams have craved: actionable intelligence that respects both security and developer workflows. Even without a commercial tool, open‑source alternatives like DefectDojo provide a solid foundation – but they lack the real‑time cloud graph that makes modern cloud security effective. The convergence of offensive security findings with cloud posture management is the next frontier.
Prediction
Within 18 months, 70% of enterprise cloud security teams will adopt unified pen‑test findings platforms, making isolated Excel‑based reporting obsolete. AI will not only triage but also autonomously patch low‑risk findings (e.g., correcting bucket ACLs or rotating exposed keys) without human intervention. As code‑to‑cloud mapping matures, CI/CD pipelines will block deployments that reintroduce previously fixed findings, turning penetration testing into a continuous, preventive control rather than a point‑in‑time audit. Budget‑constrained teams will increasingly rely on hybrid models: Wiz for live cloud context, supplemented by DefectDojo for static aggregation – but the trend is unmistakably toward unified, graph‑powered platforms.
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