Performing AI-Driven OSINT Correlation

When to Use

• You have collected raw OSINT data from multiple tools and sources but need to identify connections, contradictions, and patterns across them.
• You need to build a unified intelligence profile for a target entity (person, organization, or infrastructure) from fragmented data.
• Traditional manual correlation is too slow or error-prone for the volume of data collected.
• You want confidence-scored assessments of identity linkage across platforms rather than simple keyword matching.

Prerequisites

• Python 3.10+ with requests, json, and csv libraries
• Sherlock installed (pip install sherlock-project)
• theHarvester installed (pip install theHarvester)
• SpiderFoot 4.0+ running on localhost:5001
• Access to an LLM API (OpenAI, Anthropic, or local model via Ollama)
• Optional: Maltego CE for graph visualization of correlation results
• Optional: API keys for Shodan, VirusTotal, HaveIBeenPwned, Hunter.io

Workflow

Legal & Ethical Requirements

• Obtain documented written authorization before any investigation
• Establish lawful basis for data processing (law enforcement, corporate policy, etc.)
• Define PII retention limits and data handling procedures
• Comply with local privacy regulations (GDPR, CCPA, etc.)

Phase 1 — Multi-Source OSINT Collection

1. Create the working directory for all OSINT outputs:

bash mkdir -p /tmp/osint

1. Enumerate usernames across platforms with Sherlock:

bash sherlock "targetusername" --output /tmp/osint/sherlock-results.txt --csv

1. Harvest emails, subdomains, and hosts with theHarvester:

bash theHarvester -d targetdomain.com -b all -f /tmp/osint/harvester-results.json

1. Run a SpiderFoot passive scan via REST API:

bash curl -s http://localhost:5001/api/scan/start \ -d "scanname=target-recon&scantarget=targetdomain.com&usecase=passive" \ | jq '.scanid'

1. Export SpiderFoot results when scan completes:

bash SCAN_ID="<scanid_from_step_3>" curl -s "http://localhost:5001/api/scan/${SCAN_ID}/results?type=all" \ -o /tmp/osint/spiderfoot-results.json

1. Query breach databases for email exposure (example with HIBP API):

bash curl -s -H "hibp-api-key: ${HIBP_KEY}" \ -H "User-Agent: OSINT-Correlation-Skill" \ "https://haveibeenpwned.com/api/v3/breachedaccount/target@example.com" \ -o /tmp/osint/breach-results.json

Phase 2 — Data Normalization

1. Normalize all collected data into a common schema. Create a unified JSON structure that tags each finding with its source, timestamp, and data type:

bash cat > /tmp/osint/normalize.py << 'EOF' import json, csv, sys, os from datetime import datetime

findings = ]

# Normalize Sherlock CSV results sherlock_path = "/tmp/osint/sherlock-results.txt" if os.path.exists(sherlock_path): with open(sherlock_path) as f: for row in csv.DictReader(f): findings.append({ "source": "sherlock", "type": "social_profile", "platform": row.get("name", ""), "url": row.get("url_user", ""), "username": row.get("username", ""), "status": row.get("status", ""), "collected_at": datetime.utcnow().isoformat() })

# Normalize theHarvester JSON results harvester_path = "/tmp/osint/harvester-results.json" if os.path.exists(harvester_path): with open(harvester_path) as f: data = json.load(f) for email in data.get("emails", ]): findings.append({ "source": "theHarvester", "type": "email", "value": email, "collected_at": datetime.utcnow().isoformat() }) for host in data.get("hosts", ]): findings.append({ "source": "theHarvester", "type": "hostname", "value": host, "collected_at": datetime.utcnow().isoformat() })

# Normalize SpiderFoot results sf_path = "/tmp/osint/spiderfoot-results.json" if os.path.exists(sf_path): with open(sf_path) as f: for item in json.load(f): findings.append({ "source": "spiderfoot", "type": item.get("type", "unknown"), "value": item.get("data", ""), "module": item.get("module", ""), "collected_at": datetime.utcnow().isoformat() })

with open("/tmp/osint/normalized-findings.json", "w") as f: json.dump(findings, f, indent=2)

print(f"Normalized {len(findings)} findings from {len(set(f'source'] for f in findings))} sources") EOF python3 /tmp/osint/normalize.py

Phase 3 — AI-Driven Correlation

1. Send normalized findings to an LLM for cross-source correlation analysis:

bash cat > /tmp/osint/correlate.py << 'PYEOF' import json, os from openai import OpenAI # or anthropic, ollama, etc.

client = OpenAI(api_key=os.environ"OPENAI_API_KEY"])

with open("/tmp/osint/normalized-findings.json") as f: findings = json.load(f)

correlation_prompt = f"""You are an OSINT analyst. Analyze these findings collected from multiple sources and produce a correlation report.

For each identity or entity you detect:

1. List all linked accounts/profiles with the evidence connecting them.
2. Assign a confidence score (0.0-1.0) for each linkage based on:

• Exact username match across platforms (high)
• Similar usernames with shared metadata (medium)
• Same email in breach data and registration (high)
• Co-occurring infrastructure (IP, domain) (medium)
• Temporal correlation of account creation dates (low-medium)

1. Identify contradictions or potential false positives.
2. Flag high-risk exposures (breached credentials, PII leaks, infrastructure overlaps).
3. Produce a structured JSON report.

Raw findings: {json.dumps(findings:500], indent=2)} """

response = client.chat.completions.create( model="gpt-4o", messages= {"role": "system", "content": "You are an expert OSINT analyst specializing in identity correlation and link analysis."}, {"role": "user", "content": correlation_prompt} ], temperature=0.1, response_format={"type": "json_object"} )

report = json.loads(response.choices0].message.content)

with open("/tmp/osint/correlation-report.json", "w") as f: json.dump(report, f, indent=2)

print(json.dumps(report, indent=2)) PYEOF python3 /tmp/osint/correlate.py

1. Perform entity resolution — deduplicate and merge related identities:

bash cat > /tmp/osint/resolve.py << 'PYEOF' import json

with open("/tmp/osint/correlation-report.json") as f: report = json.load(f)

# Extract entities and build a link graph entities = report.get("entities", ]) print(f"Identified {len(entities)} distinct entities") for entity in entities: name = entity.get("identifier", "unknown") confidence = entity.get("confidence", 0) links = entity.get("linked_accounts", ]) risk = entity.get("risk_level", "unknown") print(f" {confidence:.0%}] {name} — {len(links)} linked accounts — risk: {risk}") PYEOF python3 /tmp/osint/resolve.py

Phase 4 — Reporting and Visualization

1. Generate a final intelligence profile in Markdown:

bash cat > /tmp/osint/report.py << 'PYEOF' import json from datetime import datetime

with open("/tmp/osint/correlation-report.json") as f: report = json.load(f)

md = f"# OSINT Correlation Report\n\n" md += f"Generated: {datetime.utcnow().isoformat()}Z\n\n" md += "## Entity Profiles\n\n"

for entity in report.get("entities", ]): eid = entity.get("identifier", "Unknown") conf = entity.get("confidence", 0) md += f"### {eid} (Confidence: {conf:.0%})\n\n" md += "| Source | Platform | Evidence |\n|--------|----------|----------|\n" for link in entity.get("linked_accounts", ]): md += f"| {link.get('source','')} | {link.get('platform','')} | {link.get('evidence','')} |\n" md += f"\nRisk Level: {entity.get('risk_level', 'N/A')}\n\n" for flag in entity.get("flags", ]): md += f"- ⚠️ {flag}\n" md += "\n"

with open("/tmp/osint/intelligence-profile.md", "w") as f: f.write(md)

print("Report written to /tmp/osint/intelligence-profile.md") PYEOF python3 /tmp/osint/report.py

1. Optional — Import correlation graph into Maltego for visualization:

bash # Export entities as Maltego-compatible CSV for manual import cat > /tmp/osint/maltego_export.py << 'PYEOF' import json, csv

with open("/tmp/osint/correlation-report.json") as f: report = json.load(f)

with open("/tmp/osint/maltego-import.csv", "w", newline="") as f: writer = csv.writer(f) writer.writerow("Entity Type", "Value", "Linked To", "Link Label", "Confidence"]) for entity in report.get("entities", ]): for link in entity.get("linked_accounts", ]): writer.writerow( link.get("type", "Alias"), link.get("value", ""), entity.get("identifier", ""), link.get("evidence", ""), link.get("confidence", "") ])

print("Maltego CSV exported to /tmp/osint/maltego-import.csv") PYEOF python3 /tmp/osint/maltego_export.py

Key Concepts

| Concept | Description | |---------|-------------| | Cross-Source Correlation | Matching identifiers (usernames, emails, IPs) across independent OSINT sources to establish entity linkage | | Confidence Scoring | Assigning probabilistic confidence (0.0–1.0) to each linkage based on evidence strength and corroboration | | Entity Resolution | Deduplicating and merging records that refer to the same real-world entity across fragmented datasets | | False Positive Detection | Using AI reasoning to identify coincidental matches versus genuine identity links | | Multi-Vector Intelligence | Combining findings from social media, DNS, breach data, and infrastructure into a single threat picture | | Link Analysis | Graph-based examination of relationships between entities, accounts, and infrastructure |

Tools & Systems

| Tool | Role in Workflow | |------|-----------------| | Sherlock | Username enumeration across 400+ social platforms | | theHarvester | Email, subdomain, and host discovery from public sources | | SpiderFoot | Automated OSINT collection across 200+ modules | | Maltego | Graph-based visualization of entity relationships | | LLM API (GPT-4, Claude, Ollama) | Cross-source reasoning, pattern detection, and confidence scoring | | HaveIBeenPwned | Breach exposure and credential leak detection |

Common Scenarios

• Threat Actor Attribution: Correlate a suspicious username found in a phishing campaign with social media profiles, domain registrations, and breach data to build an attribution profile.
• Attack Surface Mapping: Link discovered subdomains, emails, and employee social accounts to understand an organization's full external exposure.
• Insider Threat Investigation: Cross-reference an employee's known accounts with dark web marketplace activity and breach databases.
• Brand Impersonation Detection: Identify accounts across platforms mimicking a target brand by correlating registration patterns, naming conventions, and temporal signals.

Output Format

The final output is a structured JSON correlation report and a Markdown intelligence profile containing:

json
{
  "meta": {
    "target": "targetdomain.com",
    "sources_used": ["sherlock", "theHarvester", "spiderfoot", "hibp"],
    "total_findings": 247,
    "generated_at": "2025-01-15T14:30:00Z"
  },
  "entities": [
    {
      "identifier": "john.target",
      "confidence": 0.92,
      "linked_accounts": [
        {
          "source": "sherlock",
          "platform": "GitHub",
          "value": "john.target",
          "evidence": "Exact username match, bio references targetdomain.com",
          "confidence": 0.95
        }
      ],
      "risk_level": "high",
      "flags": [
        "Credentials exposed in 2 breaches (2022, 2023)",
        "Admin email for targetdomain.com found in public WHOIS"
      ]
    }
  ],
  "contradictions": [],
  "recommendations": []
}

Verification

• Confirm that each linked account has been independently verified against at least two sources before assigning confidence > 0.8.
• Cross-check AI-generated correlations manually for a random sample (10–20%) to validate accuracy.
• Verify that no false positives from common usernames (e.g., "admin", "test") inflated entity profiles.
• Ensure breach data timestamps are current and from reputable aggregators.
• Validate that the final report does not include stale or retracted OSINT data.