Analyzing Indicators of Compromise

When to Use

Use this skill when:

A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

Prerequisites

VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
AbuseIPDB API key for IP reputation checks
MISP instance or TIP for cross-referencing against known campaigns
Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

Workflow

Step 1: Normalize and Classify IOC Types

Before enriching, classify each IOC:

IPv4/IPv6 address: Check if RFC 1918 private (skip external enrichment), validate format
Domain/FQDN: Defang for safe handling (evil[.]com), extract registered domain via tldextract
URL: Extract domain + path separately; check for redirectors
File hash: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
Email address: Split into domain (check MX/DMARC) and local part for pattern analysis

Defang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.

Step 2: Multi-Source Enrichment

VirusTotal (file hash, URL, IP, domain):

import vt

client = vt.Client("YOUR_VT_API_KEY")

# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")

# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()

AbuseIPDB (IP addresses):

import requests

response = requests.get(
    "https://api.abuseipdb.com/api/v2/check",
    headers={"Key": "YOUR_KEY", "Accept": "application/json"},
    params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")

MalwareBazaar (file hashes):

response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
    print(result["data"][0]["tags"], result["data"][0]["signature"])

Step 3: Contextualize with Campaign Attribution

Query MISP for existing events matching the IOC:

from pymisp import PyMISP

misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
    print(event["Event"]["info"], event["Event"]["threat_level_id"])

Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).

Step 4: Assign Confidence Score and Disposition

Apply a tiered decision framework:

Block (High Confidence ≥ 70%): ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
Monitor/Alert (Medium 40–69%): 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
Whitelist/Investigate (Low <40%): ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
False Positive: Legitimate business service incorrectly flagged; document and exclude from future alerts

Step 5: Document and Distribute

Record findings in TIP/MISP with:

All enrichment data collected (timestamps, source, score)
Disposition decision and rationale
Blocking actions taken (firewall, proxy, DNS sinkhole)
Related incident ticket number

Export to STIX indicator object with confidence field set appropriately.

Key Concepts

Term	Definition
IOC	Indicator of Compromise — observable network or host artifact indicating potential compromise
Enrichment	Process of adding contextual data to a raw IOC from multiple intelligence sources
Defanging	Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation
False Positive Rate	Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds
Sinkhole	DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely
TTL	Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days

Tools & Systems

VirusTotal: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments
AbuseIPDB: Community-maintained IP reputation database with 90-day abuse report history
MalwareBazaar (abuse.ch): Free malware hash repository with YARA rule associations and malware family tagging
URLScan.io: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage
Shodan: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment

Common Pitfalls

Blocking shared infrastructure: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.
VT score obsession: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.
Missing defanging: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.
No expiration policy: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.
Over-relying on single source: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.

Verification Criteria

Confirm successful execution by validating:

[ ] All prerequisite tools and access requirements are satisfied
[ ] Each workflow step completed without errors
[ ] Output matches expected format and contains expected data
[ ] No security warnings or misconfigurations detected
[ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

SOC 2: CC7.1 (Monitoring), CC7.2 (Anomaly Detection)
ISO 27001: A.6.1 (Threat Intelligence), A.16.1 (Security Incident Management)
NIST 800-53: PM-16 (Threat Awareness), RA-3 (Risk Assessment), SI-5 (Security Alerts)
NIST CSF: ID.RA (Risk Assessment), DE.AE (Anomalies & Events)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

# Install via CLI
npx claw-grc skills add analyzing-indicators-of-compromise

# Or load dynamically via MCP
grc.load_skill("analyzing-indicators-of-compromise")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

SHA-256 chain hashing ensures no step can be modified after execution
Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.

Analyzing Indicators of Compromise

When to Use

Use this skill when:

A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

Prerequisites

VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
AbuseIPDB API key for IP reputation checks
MISP instance or TIP for cross-referencing against known campaigns
Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

Workflow

Step 1: Normalize and Classify IOC Types

Before enriching, classify each IOC:

IPv4/IPv6 address: Check if RFC 1918 private (skip external enrichment), validate format
Domain/FQDN: Defang for safe handling (evil[.]com), extract registered domain via tldextract
URL: Extract domain + path separately; check for redirectors
File hash: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
Email address: Split into domain (check MX/DMARC) and local part for pattern analysis

Defang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.

Step 2: Multi-Source Enrichment

VirusTotal (file hash, URL, IP, domain):

import vt

client = vt.Client("YOUR_VT_API_KEY")

# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")

# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()

AbuseIPDB (IP addresses):

import requests

response = requests.get(
    "https://api.abuseipdb.com/api/v2/check",
    headers={"Key": "YOUR_KEY", "Accept": "application/json"},
    params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")

MalwareBazaar (file hashes):

response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
    print(result["data"][0]["tags"], result["data"][0]["signature"])

Step 3: Contextualize with Campaign Attribution

Query MISP for existing events matching the IOC:

from pymisp import PyMISP

misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
    print(event["Event"]["info"], event["Event"]["threat_level_id"])

Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).

Step 4: Assign Confidence Score and Disposition

Apply a tiered decision framework:

Block (High Confidence ≥ 70%): ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
Monitor/Alert (Medium 40–69%): 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
Whitelist/Investigate (Low <40%): ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
False Positive: Legitimate business service incorrectly flagged; document and exclude from future alerts

Step 5: Document and Distribute

Record findings in TIP/MISP with:

All enrichment data collected (timestamps, source, score)
Disposition decision and rationale
Blocking actions taken (firewall, proxy, DNS sinkhole)
Related incident ticket number

Export to STIX indicator object with confidence field set appropriately.

Key Concepts

Term	Definition
IOC	Indicator of Compromise — observable network or host artifact indicating potential compromise
Enrichment	Process of adding contextual data to a raw IOC from multiple intelligence sources
Defanging	Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation
False Positive Rate	Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds
Sinkhole	DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely
TTL	Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days

Tools & Systems

VirusTotal: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments
AbuseIPDB: Community-maintained IP reputation database with 90-day abuse report history
MalwareBazaar (abuse.ch): Free malware hash repository with YARA rule associations and malware family tagging
URLScan.io: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage
Shodan: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment

Common Pitfalls

Blocking shared infrastructure: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.
VT score obsession: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.
Missing defanging: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.
No expiration policy: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.
Over-relying on single source: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.

Verification Criteria

Confirm successful execution by validating:

[ ] All prerequisite tools and access requirements are satisfied
[ ] Each workflow step completed without errors
[ ] Output matches expected format and contains expected data
[ ] No security warnings or misconfigurations detected
[ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

SOC 2: CC7.1 (Monitoring), CC7.2 (Anomaly Detection)
ISO 27001: A.6.1 (Threat Intelligence), A.16.1 (Security Incident Management)
NIST 800-53: PM-16 (Threat Awareness), RA-3 (Risk Assessment), SI-5 (Security Alerts)
NIST CSF: ID.RA (Risk Assessment), DE.AE (Anomalies & Events)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

# Install via CLI
npx claw-grc skills add analyzing-indicators-of-compromise

# Or load dynamically via MCP
grc.load_skill("analyzing-indicators-of-compromise")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

SHA-256 chain hashing ensures no step can be modified after execution
Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.

Prerequisites

Analyzing Indicators of Compromise

When to Use

Prerequisites

Workflow

Step 1: Normalize and Classify IOC Types

Step 2: Multi-Source Enrichment

Step 3: Contextualize with Campaign Attribution

Step 4: Assign Confidence Score and Disposition

Step 5: Document and Distribute

Key Concepts

Tools & Systems

Common Pitfalls

Verification Criteria

Compliance Framework Mapping

Deploying This Skill with Claw GRC

Agent Execution

Audit Trail Integration

Continuous Compliance

Use with Claw GRC Agents

Tags

Related Skills

Analyzing Threat Intelligence Feeds

Processing STIX Taxii Feeds

Automating IOC Enrichment

Collecting Open Source Intelligence

Generating Threat Intelligence Reports

Analyzing Campaign Attribution Evidence

Prerequisites

Analyzing Indicators of Compromise

When to Use

Prerequisites

Workflow

Step 1: Normalize and Classify IOC Types

Step 2: Multi-Source Enrichment

Step 3: Contextualize with Campaign Attribution

Step 4: Assign Confidence Score and Disposition

Step 5: Document and Distribute

Key Concepts

Tools & Systems

Common Pitfalls

Verification Criteria

Compliance Framework Mapping

Deploying This Skill with Claw GRC

Agent Execution

Audit Trail Integration

Continuous Compliance

Use with Claw GRC Agents

Tags

Related Skills

Analyzing Threat Intelligence Feeds

Processing STIX Taxii Feeds

Automating IOC Enrichment

Collecting Open Source Intelligence

Generating Threat Intelligence Reports

Analyzing Campaign Attribution Evidence