Tenable was granted a patent for machine-learning vulnerability scoring.

{
  "signal_id": "6932234c-d487-520e-adec-772dc630c0e2",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-09-30T17:39:14+00:00",
  "company": {
    "name": "Tenable",
    "domain": "tenable.com"
  },
  "data": {
    "cpc": [
      {
        "code": "H04L 63/1433",
        "label": "Transmission of digital information"
      },
      {
        "code": "G06F 18/214",
        "label": "Electric digital data processing"
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        "label": "Electric digital data processing"
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        "label": "Computing arrangements based on specific computational models"
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    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Machine Learning",
      "Cyber Security",
      "Information Security"
    ],
    "detail": "In September 2026 Tenable patented technology for estimating vulnerability severity from descriptions, used in cybersecurity assessment and network protection workflows.",
    "summary": "Tenable was granted a patent for machine-learning vulnerability scoring.",
    "event_at": "2026-09-29",
    "inventors": [
      {
        "name": "Cathal Mullaney",
        "status": "unresolved",
        "is_primary": true
      }
    ],
    "tech_area": "networking / data transmission",
    "event_kind": "grant_date",
    "grant_date": "2026-09-29",
    "source_url": "https://patents.google.com/patent/US12750392",
    "cpc_primary": {
      "code": "H04L 63/1433",
      "label": "Transmission of digital information"
    },
    "patent_title": "Automatic generation of vulnerability metrics using machine learning",
    "patent_number": "US12750392",
    "uspto_assignee": "Tenable, Inc.",
    "patent_abstract": "Techniques, methods and/or apparatuses that enable generation of vulnerability vectors of newly identified vulnerabilities (e.g., Common Vulnerability Exposures (CVEs)). Based on the textual description of the vulnerability, vulnerability vectors are generated. The generated vulnerability vectors may represent a prediction of how a third-party vulnerability scorer (e.g., United State National Vulnerability Database (US NVD)) would score the identified vulnerability.",
    "precision_class": "structural",
    "invention_explanation": "A machine-learning system that turns a newly reported vulnerability’s text into a structured risk profile and predicts how an established scoring service would rate it.",
    "technologies_mentioned": [
      "machine learning",
      "vulnerability management",
      "cybersecurity scoring",
      "network security"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/6932234c-d487-520e-adec-772dc630c0e2 \
  -H "X-API-KEY: $AUTOBOUND_API_KEY"

curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"tenable.com","limit":20}'