performing-ai-driven-osint-correlation（执行 AI 驱动的 osint 关联）

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: mkdir -p /tmp/osint
2. Enumerate usernames across platforms with Sherlock: sherlock "targetusername" --output /tmp/osint/sherlock-results.txt --csv
3. Harvest emails, subdomains, and hosts with theHarvester: theHarvester -d targetdomain.com -b all -f /tmp/osint/harvester-results.json
4. Run a SpiderFoot passive scan via REST API: curl -s http://localhost:5001/api/scan/start \ -d "scanname=target-recon&scantarget=targetdomain.com&usecase=passive" \ | jq '.scanid'
5. Export SpiderFoot results when scan completes: SCAN_ID="<scanid_from_step_3>" curl -s "http://localhost:5001/api/scan/${SCAN_ID}/results?type=all" \ -o /tmp/osint/spiderfoot-results.json
6. Query breach databases for email exposure (example with HIBP API): 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: 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: 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) 3. Identify contradictions or potential false positives. 4. Flag high-risk exposures (breached credentials, PII leaks, infrastructure overlaps). 5. 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.choices[0].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
2. Perform entity resolution — deduplicate and merge related identities: 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: 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"\n**Risk 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
2. Optional — Import correlation graph into Maltego for visualization: # 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

Tools & Systems

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:

{
  "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.