Executive summary
- What broke: S3 access controls allowed unintended public reads.
- Why it matters: data exposure is often silent and scalable (scraping).
- How to catch it: alert on policy/ACL changes and unexpected object reads.
- How to prevent it: Block Public Access + least-privilege policies + guardrails.
Scope & safety
This is designed as a safe lab concept:
- Use a dedicated test environment (no real data).
- Only synthetic objects (e.g., dummy text files).
- Teardown after testing to minimize cost and risk.
Architecture
Minimal setup: one S3 bucket, a few sample objects, and logging/visibility enabled. Optional: central log bucket + Security Hub integration.
Diagram placeholder: add a simple box diagram here later.
The misconfiguration
Common real-world causes:
- Public access granted via bucket policy during “quick testing” and never reverted.
- Over-broad principals (e.g., “*”) or permissive conditions.
- Confusion between ACLs, bucket policy, and Block Public Access settings.
Why this is dangerous
- Data exposure: sensitive documents, exports, logs, backups.
- Recon: leaked configs/keys can unlock deeper compromise.
- Scale: automated scraping can download large volumes quickly.
Exploitation simulation (isolated lab)
Kept intentionally high-level and defender-focused. The goal is to validate what “exposure” looks like and what your telemetry captures.
- Confirm an unauthenticated/low-priv principal can read a known test object.
- Observe which logs record the access and what fields you can alert on.
- Verify that once fixed, the same access attempt fails (regression check).
Detection strategy
- Change detection: alert on bucket policy/ACL updates and public-access setting changes.
- Access detection: watch for unusual read patterns on sensitive prefixes.
- Config monitoring: continuously evaluate buckets for public exposure.
You’re aiming for both: (1) “someone made it public,” and (2) “someone accessed it.”
Remediation
- Enable and enforce Block Public Access (account + bucket).
- Replace permissive policies with least-privilege statements.
- Rotate/remove any data that should never have been exposed.
Hardening checklist
- Guardrails to prevent public bucket policies (policy checks / org controls).
- Centralized logging + alerts for high-risk storage changes.
- Tagging + ownership on buckets (who is responsible when alerts fire?).
- Periodic review of buckets and access analyzer findings.
Interview talking points
- I focused on both prevention (guardrails) and detection (signals + alerts).
- I defined blast radius and prioritized the fastest safe remediation first.
- I validated the fix by re-running the same access attempt and confirming it fails.