
Analyzing Cloud Storage Access Patterns
- 438 installs
- 27.3k repo stars
- Updated August 2, 2026
- mukul975/anthropic-cybersecurity-skills
analyzing-cloud-storage-access-patterns is a cybersecurity agent skill that detects abnormal S3, GCS, and Azure Blob access via log analysis for developers and security engineers who need to spot data exfiltration before
About
analyzing-cloud-storage-access-patterns is a cybersecurity agent skill (version 1.0, Apache-2.0) that helps security engineers review who and what is accessing cloud object storage across AWS S3, Google Cloud Storage, and Azure Blob Storage. The skill guides analysts to query CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics, then build hourly and per-principal baselines for request volume, object counts, and source IP history. Detection rules flag after-hours access outside 8am–6pm, bulk downloads exceeding 100 GetObject calls per hour from one principal, new source IPs absent from a 30-day baseline, and ListBucket enumeration spikes suggesting reconnaissance. A bundled Python 3.8+ agent.py script using boto3 automates CloudTrail lookup and writes prioritized findings to s3_access_report.json. The skill maps detections to five MITRE ATT&CK techniques including T1530 and T1567.002. Reach for analyzing-cloud-storage-access-patterns during incident response, threat hunting, or SOC playbook work when validating cloud storage monitoring coverage.
- Frames cloud storage access patterns for anomaly and misconfiguration review
- Supports solo builders auditing bucket policies, IAM paths, and cross-account access
- Aligns with cybersecurity skill packs for agent-guided storage forensics
- Fits post-build hardening before launch or after architecture changes
Analyzing Cloud Storage Access Patterns by the numbers
- 438 all-time installs (skills.sh)
- +24 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #527 of 2,203 Security skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 438 |
|---|---|
| repo stars | ★ 27.3k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 2, 2026 |
| Repository | mukul975/anthropic-cybersecurity-skills ↗ |
How do you detect abnormal S3 bucket access patterns?
Review who and what is accessing cloud object storage so you can spot risky ACLs, stale public buckets, and unusual access before a breach.
Who is it for?
Security engineers and SOC analysts investigating cloud storage incidents or building object-storage detection rules on AWS, GCS, or Azure.
Skip if: Teams without CloudTrail Data Events or equivalent audit logging enabled on sensitive buckets.
When should I use this skill?
A developer or security engineer asks to audit S3/GCS/Azure Blob access, hunt exfiltration, or investigate risky ACLs and public bucket exposure.
What you get
Prioritized JSON access report with bulk-download alerts, after-hours events, new-IP findings, enumeration spikes, and statistical baselines.
- s3_access_report.json
- Prioritized anomaly alert list
- Access baseline statistics
By the numbers
- Version 1.0 skill covering AWS S3, GCS, and Azure Blob Storage
- Flags bulk downloads above 100 GetObject calls per hour
- Maps to 5 MITRE ATT&CK techniques including T1530 and T1567.002
Files
Analyzing Cloud Storage Access Patterns
When to Use
- When investigating security incidents that require analyzing cloud storage access patterns
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with cloud security concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
1. Install dependencies: pip install boto3 requests 2. Query CloudTrail for S3 Data Events using AWS CLI or boto3. 3. Build access baselines: hourly request volume, per-user object counts, source IP history. 4. Detect anomalies:
- After-hours access (outside 8am-6pm local time)
- Bulk downloads: >100 GetObject calls from single principal in 1 hour
- New source IPs not seen in the prior 30 days
- ListBucket enumeration spikes (reconnaissance indicator)
5. Generate prioritized findings report.
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.jsonExamples
CloudTrail S3 Data Event
{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
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API Reference: Cloud Storage Access Pattern Analysis
AWS CLI - CloudTrail Lookup
aws cloudtrail lookup-events \
--lookup-attributes AttributeKey=ResourceType,AttributeValue=AWS::S3::Object \
--start-time 2024-01-15T00:00:00Z \
--output jsonCloudTrail S3 Data Event Structure
{
"EventTime": "2024-01-15T10:30:00Z",
"EventName": "GetObject",
"Username": "analyst",
"CloudTrailEvent": "{\"sourceIPAddress\":\"10.0.0.1\",\"userAgent\":\"aws-cli\",\"requestParameters\":{\"bucketName\":\"data\",\"key\":\"file.csv\"},\"userIdentity\":{\"arn\":\"arn:aws:iam::123:user/analyst\"}}"
}Key S3 Event Names
| Event | Meaning |
|---|---|
| GetObject | Object download |
| PutObject | Object upload |
| DeleteObject | Object deletion |
| ListBucket / ListObjectsV2 | Bucket enumeration |
| GetBucketPolicy | Policy read |
| PutBucketPolicy | Policy modification |
Detection Thresholds
| Anomaly | Threshold | Severity |
|---|---|---|
| Bulk download | >100 GetObject/hr per user | Critical |
| After-hours | Access outside 08:00-18:00 UTC | Medium |
| New source IP | IP not in 30-day baseline | High |
| Enumeration | >20 ListBucket per user | High |
boto3 CloudTrail Client (alternative)
import boto3
client = boto3.client("cloudtrail")
response = client.lookup_events(
LookupAttributes=[{"AttributeKey":"ResourceType","AttributeValue":"AWS::S3::Object"}],
StartTime=datetime(2024,1,15),
MaxResults=50
)
events = response["Events"]#!/usr/bin/env python3
"""Cloud Storage Access Pattern Analyzer - Detects abnormal S3/GCS/Azure Blob access via CloudTrail."""
import json
import logging
import argparse
import subprocess
from collections import defaultdict
from datetime import datetime, timedelta
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)
def query_cloudtrail_s3_events(bucket_name, hours_back=24):
"""Query CloudTrail for S3 data events on a specific bucket."""
start_time = (datetime.utcnow() - timedelta(hours=hours_back)).strftime("%Y-%m-%dT%H:%M:%SZ")
cmd = [
"aws", "cloudtrail", "lookup-events",
"--lookup-attributes", f"AttributeKey=ResourceType,AttributeValue=AWS::S3::Object",
"--start-time", start_time,
"--output", "json",
]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
if result.returncode != 0:
logger.error("CloudTrail query failed: %s", result.stderr[:200])
return []
events = json.loads(result.stdout).get("Events", [])
s3_events = []
for event in events:
ct_event = json.loads(event.get("CloudTrailEvent", "{}"))
req_params = ct_event.get("requestParameters", {})
if req_params.get("bucketName") == bucket_name or not bucket_name:
s3_events.append({
"timestamp": event.get("EventTime", ""),
"event_name": event.get("EventName", ""),
"username": event.get("Username", ""),
"source_ip": ct_event.get("sourceIPAddress", ""),
"user_agent": ct_event.get("userAgent", ""),
"bucket": req_params.get("bucketName", ""),
"key": req_params.get("key", ""),
"user_arn": ct_event.get("userIdentity", {}).get("arn", ""),
})
logger.info("Retrieved %d S3 events for bucket '%s'", len(s3_events), bucket_name or "all")
return s3_events
def detect_bulk_downloads(events, threshold=100):
"""Detect bulk GetObject operations from a single principal."""
user_downloads = defaultdict(list)
for event in events:
if event["event_name"] == "GetObject":
user_downloads[event["user_arn"]].append(event)
alerts = []
for user_arn, downloads in user_downloads.items():
if len(downloads) >= threshold:
keys = [d["key"] for d in downloads]
alerts.append({
"user_arn": user_arn,
"download_count": len(downloads),
"unique_keys": len(set(keys)),
"source_ips": list({d["source_ip"] for d in downloads}),
"first_access": downloads[0]["timestamp"],
"last_access": downloads[-1]["timestamp"],
"severity": "critical",
"indicator": "Bulk download (potential exfiltration)",
})
logger.info("Found %d bulk download alerts", len(alerts))
return alerts
def detect_after_hours_access(events, business_start=8, business_end=18):
"""Detect access outside business hours."""
after_hours = []
for event in events:
try:
ts = event["timestamp"]
if isinstance(ts, str):
dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
else:
dt = ts
hour = dt.hour
if hour < business_start or hour >= business_end:
event["indicator"] = f"After-hours access at {hour:02d}:00 UTC"
event["severity"] = "medium"
after_hours.append(event)
except (ValueError, AttributeError):
continue
logger.info("Found %d after-hours access events", len(after_hours))
return after_hours
def detect_new_source_ips(events, known_ips=None):
"""Detect access from IP addresses not in the known baseline."""
if known_ips is None:
known_ips = set()
new_ip_events = []
for event in events:
ip = event["source_ip"]
if ip and ip not in known_ips and not ip.startswith("AWS Internal"):
event["indicator"] = f"New source IP: {ip}"
event["severity"] = "high"
new_ip_events.append(event)
unique_new = len({e["source_ip"] for e in new_ip_events})
logger.info("Found %d events from %d new source IPs", len(new_ip_events), unique_new)
return new_ip_events
def detect_enumeration(events, threshold=20):
"""Detect ListBucket/ListObjects enumeration patterns."""
user_listings = defaultdict(int)
for event in events:
if event["event_name"] in ("ListBucket", "ListObjects", "ListObjectsV2"):
user_listings[event["user_arn"]] += 1
alerts = []
for user_arn, count in user_listings.items():
if count >= threshold:
alerts.append({
"user_arn": user_arn,
"list_count": count,
"severity": "high",
"indicator": "Bucket enumeration spike (reconnaissance)",
})
return alerts
def build_access_baseline(events):
"""Build statistical baseline of normal access patterns."""
hourly_counts = defaultdict(int)
user_counts = defaultdict(int)
ip_set = set()
for event in events:
try:
ts = event["timestamp"]
if isinstance(ts, str):
dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
hourly_counts[dt.hour] += 1
except (ValueError, AttributeError):
pass
user_counts[event["user_arn"]] += 1
if event["source_ip"]:
ip_set.add(event["source_ip"])
return {
"hourly_distribution": dict(hourly_counts),
"user_request_counts": dict(user_counts),
"known_ips": list(ip_set),
"total_events": len(events),
}
def generate_report(events, bulk_alerts, after_hours, new_ips, enum_alerts, baseline):
"""Generate cloud storage access analysis report."""
report = {
"timestamp": datetime.utcnow().isoformat(),
"total_events_analyzed": len(events),
"bulk_download_alerts": bulk_alerts,
"after_hours_access": len(after_hours),
"new_source_ip_events": len(new_ips),
"enumeration_alerts": enum_alerts,
"baseline_summary": {
"known_ips": len(baseline.get("known_ips", [])),
"total_baseline_events": baseline.get("total_events", 0),
},
"sample_after_hours": after_hours[:10],
"sample_new_ips": new_ips[:10],
}
total_alerts = len(bulk_alerts) + len(enum_alerts) + (1 if new_ips else 0)
print(f"CLOUD STORAGE REPORT: {len(events)} events, {total_alerts} alerts")
return report
def main():
parser = argparse.ArgumentParser(description="Cloud Storage Access Pattern Analyzer")
parser.add_argument("--bucket", default="", help="S3 bucket name to analyze")
parser.add_argument("--hours-back", type=int, default=24)
parser.add_argument("--bulk-threshold", type=int, default=100)
parser.add_argument("--known-ips-file", help="File with known IP baselines")
parser.add_argument("--output", default="s3_access_report.json")
args = parser.parse_args()
events = query_cloudtrail_s3_events(args.bucket, args.hours_back)
baseline = build_access_baseline(events)
known_ips = set(baseline.get("known_ips", []))
if args.known_ips_file:
with open(args.known_ips_file) as f:
known_ips.update(line.strip() for line in f if line.strip())
bulk_alerts = detect_bulk_downloads(events, args.bulk_threshold)
after_hours = detect_after_hours_access(events)
new_ips = detect_new_source_ips(events, known_ips)
enum_alerts = detect_enumeration(events)
report = generate_report(events, bulk_alerts, after_hours, new_ips, enum_alerts, baseline)
with open(args.output, "w") as f:
json.dump(report, f, indent=2, default=str)
logger.info("Report saved to %s", args.output)
if __name__ == "__main__":
main()
Related skills
How it compares
Pick analyzing-cloud-storage-access-patterns over generic log-review skills when you need statistical baselines and MITRE-mapped detections specifically for object-storage exfiltration.
FAQ
Which cloud providers does analyzing-cloud-storage-access-patterns support?
analyzing-cloud-storage-access-patterns covers AWS S3 via CloudTrail Data Events, Google Cloud Storage via GCS audit logs, and Azure Blob Storage via Azure Storage Analytics. The bundled agent.py script currently automates AWS CloudTrail S3 event lookup with boto3.
What anomalies does analyzing-cloud-storage-access-patterns detect?
analyzing-cloud-storage-access-patterns flags after-hours access outside 8am–6pm, bulk GetObject downloads above 100 calls per hour, source IPs not seen in a 30-day baseline, and ListBucket enumeration spikes. Findings export to a prioritized JSON report.
What prerequisites does analyzing-cloud-storage-access-patterns require?
analyzing-cloud-storage-access-patterns requires Python 3.8+, pip install boto3 requests, AWS CLI access for CloudTrail queries, and authorization to analyze storage logs. Run python scripts/agent.py --bucket NAME --hours-back 24 --output s3_access_report.json.
Is Analyzing Cloud Storage Access Patterns safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.