# Agent Skill Package: analyzing-cloud-storage-access-patterns You are loading a published Agent Skill. Follow SKILL.md exactly. Supporting files from the original zip are inlined below. When SKILL.md says to read `references/...` or `scripts/...`, use the matching FILE section here — do not say the file is missing. Canonical URL: https://skill.hk/s/analyzing-cloud-storage-access-patterns.md Human page: https://skill.hk/s/analyzing-cloud-storage-access-patterns Files (4): - SKILL.md - LICENSE - references/api-reference.md - scripts/agent.py ======================================================================== FILE: SKILL.md ======================================================================== --- name: analyzing-cloud-storage-access-patterns description: Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules. domain: cybersecurity subdomain: cloud-security tags: - cloud-security - aws-s3 - gcs - azure-blob-storage - cloudtrail - data-access-anomaly - exfiltration-detection version: '1.0' author: mahipal license: Apache-2.0 atlas_techniques: - AML.T0024 - AML.T0056 nist_ai_rmf: - MEASURE-2.7 - MAP-5.1 - MANAGE-2.4 nist_csf: - PR.IR-01 - ID.AM-08 - GV.SC-06 - DE.CM-01 mitre_attack: - T1530 - T1567.002 - T1619 - T1078.004 - T1048 --- # 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. ```bash python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json ``` ## Examples ### CloudTrail S3 Data Event ```json {"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"}, "sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}} ``` ======================================================================== FILE: LICENSE ======================================================================== Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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See the License for the specific language governing permissions and limitations under the License. ======================================================================== FILE: references/api-reference.md ======================================================================== # API Reference: Cloud Storage Access Pattern Analysis ## AWS CLI - CloudTrail Lookup ```bash aws cloudtrail lookup-events \ --lookup-attributes AttributeKey=ResourceType,AttributeValue=AWS::S3::Object \ --start-time 2024-01-15T00:00:00Z \ --output json ``` ## CloudTrail S3 Data Event Structure ```json { "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) ```python 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"] ``` ======================================================================== FILE: scripts/agent.py ======================================================================== #!/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()