Operational Impact Track Record | Global Cyber Risk Data | Fraud Detection Data | Insurance | M&A | DORA | NIS2 | Supply Chain | Daily Refresh
# | hashid |
date |
origin |
actor |
actorContext |
victim |
victimLink |
victimCountry |
victimSector |
annLink |
annTitle |
annDescription |
annEntities |
victimDetails |
annDataTypes |
annAmount |
confidence |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx |
2 | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx |
3 | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx |
4 | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx |
5 | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx |
6 | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx |
7 | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx |
8 | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx |
9 | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx |
10 | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx |
... | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx |
# | victimLink |
victimSector |
victimCountry |
mentions |
actor |
origin |
confidence |
annTitle |
annLink |
annEntities |
annTitleEntities |
annPublished |
annCrawled |
parser |
parserDate |
annRaw |
annDescription |
annAuthor |
actorType |
breachID |
victimDetails |
victim |
annRawContents |
hashid |
annDataTypes |
actorContext |
victimDomain |
date |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx |
2 | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx |
3 | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx |
4 | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx |
5 | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx |
6 | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
7 | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx |
8 | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx |
9 | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx |
10 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx |
... | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
hashid
|
String | cc50729ec1900b9b4c65763c060923d7 | |
date
|
DateTime | 2021-08-28T07:00:35+00:00 | |
origin
|
String | extortion | |
actor
|
String | Marketo | |
actorContext
|
String | Marketo is a data extortion group that emerged in 2021. U... | |
victim
|
String | Luxottica Group S.p.A. | |
victimLink
|
String | www.luxottica.com | |
victimCountry
|
String | Italy | |
victimSector
|
String | Retail | |
annLink
|
String | https//marketo.cloud/lot/65/?bp1 | |
annTitle
|
String | Luxottica data for sale | |
annDescription
|
String | 16 GB Luxottica Group S.p.A. is an Italian eyewear conglo... | |
annEntities
|
String | [{"type":"ORG","name":"Luxottica Group S . p . A ."},{"ty... | |
victimDetails
|
String | {"geo":{"country":"Italy","countryCode":"IT"},"url":"http... | |
annDataTypes
|
String | ["CUSTOMERS","INTERNAL"] | |
annAmount
|
String | ||
confidence
|
String | B-2 |
Attribute | Type | Example | Mapping |
---|---|---|---|
victimLink
|
String | www.bancaditalia.it | |
victimSector
|
String | Banks | |
victimCountry
|
String | Italy | |
mentions
|
String | [] | |
actor
|
String | Noname05716T | |
origin
|
String | ddos | |
confidence
|
String | A-2 | |
annTitle
|
String | www.bancaditalia.it | |
annLink
|
String | ddosia://64459dfb339baed82159056c/64459dfc339baed82159056d | |
annEntities
|
String | [] | |
annTitleEntities
|
String | [] | |
annPublished
|
DateTime | 2023-04-24T00:00:00+00:00 | |
annCrawled
|
|||
parser
|
String | Noname05716T-parser | |
parserDate
|
DateTime | 2023-12-10T11:04:17+00:00 | |
annRaw
|
String | {"_id":"64467096c624ec0e58f9974f","target_id":"64459dfb33... | |
annDescription
|
String | ddos type: http. method: GET. port: 443. target ip: 85.15... | |
annAuthor
|
String | Noname05716T | |
actorType
|
String | ||
breachID
|
String | a2cec996f8aa2bdcf709086a1cd7f089 | |
victimDetails
|
String | {"geo":{"country":"Italy","countryCode":"IT"},"url":"http... | |
victim
|
String | Banca d'Italia | |
annRawContents
|
String | undefined | |
hashid
|
String | a2cec996f8aa2bdcf709086a1cd7f089 | |
annDataTypes
|
String | [] | |
actorContext
|
String | Noname05716 "Targets" collects data about Noname05716 tar... | |
victimDomain
|
String | bancaditalia.it | |
date
|
DateTime | 2023-04-24T00:00:00+00:00 |
Attribute | Type | Example | Mapping |
---|---|---|---|
String | Digital Intelligence Lab Srl | Company Legal Name | |
String | ibm.com | Company Domain | |
String | Advertising | Company Industry | |
Integer | 518210 | Company NAICS Code | |
String | United States of America | Country Name | |
String | >1bln or 250-500mln, or <1mln | Company Annual Revenue | |
String | less than 10, 250-500 employees, 1000-5000 employees, >10000 | Company Employee Count | |
Announce Title
|
String | ||
Announce Date
|
Date | ||
Announce Text
|
Text | ||
Announce Link
|
String | ||
Amount
|
String | ||
Involved Data Types
|
Text | e.g. PII data, Medical data, Intellectual Property record... | |
Actor
|
String | e.g. a Threat Actor or an Authority | |
ActorContext
|
Text | Conti is a notorious ransomware group known for its highl... | |
Evaluation
|
Text | A-2 |
Description
Country Coverage
History
Volume
120,000 | records |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License | Not available |
Yearly License | Available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh?
The “Operational Impact Track Record” bundle provides global cyber risk data of digital extortion, ransomware, data extortion attacks, and DDoS attacks that impact business operations, supporting fraud detection in many fields, including Insurance, Mergers & Acquisitions (M&A), and Supply Chain.
What is Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh used for?
This product has 5 key use cases. Digital Intelligence Lab recommends using the data for Supply Chain Intelligence, Mergers & Acquisitions (M&A), Know Your Customer (KYC), Risk Intelligence, and Company Risk Analysis. Global businesses and organizations buy Cyber Risk Data from Digital Intelligence Lab to fuel their analytics and enrichment.
Who can use Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh?
This product is best suited if you’re a Enterprise looking for Cyber Risk Data. Get in touch with Digital Intelligence Lab to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh go?
This product has 5 years of historical coverage. It can be delivered on a daily and on-demand basis.
Which countries does Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh cover?
This product includes data covering 240 countries like USA, China, Japan, Germany, and India. Digital Intelligence Lab is headquartered in Italy.
How much does Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh cost?
Pricing information for Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh is available by getting in contact with Digital Intelligence Lab. Connect with Digital Intelligence Lab to get a quote and arrange custom pricing models based on your data requirements.
How can I get Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh?
Businesses can buy Cyber Risk Data from Digital Intelligence Lab and get the data via REST API.
What is the data quality of Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh?
Digital Intelligence Lab has reported that this product has the following quality and accuracy assurances: 95% match rate. You can compare and assess the data quality of Digital Intelligence Lab using Datarade’s data marketplace.
What are similar products to Operational Impact Track Record Global Cyber Risk Data Fraud Detection Data Insurance M&A DORA NIS2 Supply Chain Daily Refresh?
This product has 3 related products. These alternatives include Bad Security Posture Indicators Global Cyber Risk Data 5 Year Historical, Daily Refresh, TagX - 5000+ Face Anti Spoofing Data Anti Spoofing Detection Face Recognition Fraud Detection KYC authentication Global coverage, and Opoint Trade Credit Risk Data Web Data Global Risk Data 235K+ Sources / 3M+ Articles Daily / 185 Languages / 220 Jurisdictions Risk Management. You can compare the best Cyber Risk Data providers and products via Datarade’s data marketplace and get the right data for your use case.