Phishing and Social Engineering Incidents

This dataset provides a comprehensive record of phishing and social engineering incidents, including attack details, affected parties, incident analysis, and customer education efforts. It is designed to support security monitoring, risk assessment, and the development of targeted prevention strategies for organizations. Ideal for incident response teams, compliance reporting, and awareness program evaluation.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Security incident tracking and analysis

Sample rows

preview · 8 of 500 rows · all 21 columns
incident_idstringtarget_groupstringemployee_affected_countintegerdata_compromisedbooleanlocation_countrystringreport_datedatetimeincident_typestringattack_vectorstringdetected_bystringincident_statusstringresolution_datedateimpact_levelstringcustomer_affected_countintegercustomer_education_datedatecustomer_education_typestringreported_bystringlocation_citystringlocation_statestringdescriptionstringprevention_measuresstringanalysis_summarystring
INC202300001customers0falseUSA2023-01-04T10:13:27phishingemailspam_filter_01resolved2023-01-05medium0blankblanksupport_bot_1New YorkNew YorkCustomer received suspicious email with fake login link. Phishing attempt detected during login process.Enabled multi-factor authentication for all customer accounts.Incident was contained and no data was compromised. Customer support notified.
INC202300002customers0trueUSA2023-01-08T14:39:10phone_scamphonecustomer_serviceclosed2023-01-09high5752023-01-11email_campaigncustomer_alert_23DallasTexasCustomer reported receiving fraudulent call requesting account details. Caller impersonated bank staff.Bank notified all customers of scam calls and blocked suspicious numbers.Large number of customers received scam calls. Some reported sharing information.
INC202300003executives4trueUSA2023-01-11T09:05:40social_engineeringphoneexecutive_assistantinvestigatingblankcritical0blankblankpa_ceoSan FranciscoCaliforniaExecutive received phone call from individual posing as IT support requesting credentials.Increased verification steps for IT requests to executives.Potential compromise of executive credentials under investigation.
INC202300004employees1falseUSA2023-01-15T16:27:33phishingemailautomated_filteropenblankmedium0blankblankit_sysChicagoIllinoisEmployee reported a suspicious email requesting password reset on internal system.Password reset requests flagged for manual review.No data loss detected. Employee educated on phishing risks.
INC202300005partners0falseCanada2023-01-21T11:20:15otherwebsitepartner_adminresolved2023-01-23medium0blankblankpartner_71MontrealQuebecPartner organization notified about malicious website mimicking official portal.Legal team initiated takedown of fraudulent website.No partner data compromised. Awareness raised among partners.
INC202300006customers0trueUK2023-02-02T13:44:58phishingSMScustomer_alert_12closed2023-02-04high9502023-02-06webinarsupport_bot_2LondonEnglandMass SMS campaign attempted to trick customers into clicking malicious links.SMS filtering enhanced. Message sender blocked.High number of customers targeted, some reported compromised accounts.
INC202300007employees3falseIndia2023-02-07T15:11:54social_engineeringSMShr_teaminvestigatingblankhigh0blankblankajohnsonBangaloreKarnatakaEmployee received SMS from unknown number requesting confidential project data.Blocked sender and alerted security team.SMS scam attempt. No employee credentials leaked.
INC202300008IT0falseIreland2023-02-11T08:18:21phishingwebsiteIT_monitoropenblankmedium0blankblankit_alert_04DublinLeinsterIT staff noticed fake login page hosted on compromised domain.Domain blacklisted for all internal access.Fake login page removed. IT staff notified employees.

What the 500 rows show

from the 500-row sample

Executives (target group) stands out: mean employee_affected_count is 2.6, against 0.43 for the rest.

  • 42%data_compromised = true
  • 0median employee_affected_count
  • 3customer education types
  • 4incident types
  • 4attack vectors
  • 4incident statuses
Mean employee_affected_count by target_group500 rows
0240.0custo…205 ro…1.3emplo…151 ro…2.6execu…46 rows0.0partn…43 rows0.0vendo…38 rows0.29IT17 rows
employee_affected_count500 rows, in bands of 1
016032032095571576036employee_affected_count →

Median 0, from 0 to 6.

location_country500 rows · top 10 of 17 values
  1. USA119
  2. India69
  3. Australia49
  4. UK38
  5. South Africa36
  6. Germany33
  7. Canada28
  8. Switzerland28
  9. Spain28
  10. Brazil20
21 columns by typefrom the column list below
  • string 15
  • integer 2
  • date 2
  • datetime 1
  • boolean 1

Columns

21 columns in four groups
blueprint · 21 columns
columntypedescriptionexample
Text 15 columns
incident_idstringUnique identifier for each reported incidentuniqueINC202300001
incident_typestringType of incident (e.g., phishing, social engineering, phone scam)phishing · social_engineering · phone_scam · otherphishing
target_groupstringGroup or department targeted by the incident (e.g., customers, employees, executives)6 groupscustomers
attack_vectorstringPrimary method used in the attack (e.g., email, phone, SMS, website)4 vectorsemail
descriptionstringDetailed description of the incidentCustomer received suspici…
detected_bystringPerson, team, or system that detected the incidentoptionalspam_filter_01
incident_statusstringCurrent status of the incident (e.g., open, investigating, resolved, closed)open · investigating · resolved · closedresolved
impact_levelstringSeverity or impact level of the incident (e.g., low, medium, high, critical)low · medium · high · criticalmedium
prevention_measuresstringSummary of prevention measures taken after the incidentoptionalEnabled multi-factor auth…
analysis_summarystringSummary of incident analysis and lessons learnedoptionalIncident was contained an…
customer_education_typestringType of customer education provided (e.g., email campaign, webinar, training)email_campaign · webinar · training · other · optionalemail_campaign
reported_bystringName or identifier of the person who reported the incidentoptionalsupport_bot_1
location_citystringCity where the incident occurred or was reportedoptionalNew York
location_statestringState or region where the incident occurred or was reportedoptionalNew York
location_countrystringCountry where the incident occurred or was reportedoptionalUSA
Numbers 2 columns
customer_affected_countintegerNumber of customers affected by the incident0 or more · optional0
employee_affected_countintegerNumber of employees affected by the incident0 or more · optional0
Dates and times 3 columns
report_datedatetimeDate and time when the incident was reported2023-01-04T10:13:27
resolution_datedateDate when the incident was resolved or closedoptional2023-01-05
customer_education_datedateDate when customer education or awareness was conductedoptional2023-01-11
True or false 1 column
data_compromisedbooleanIndicates if any sensitive data was compromisedfalse

Use it for

  • data compromis…42%208 of 500 rowsmean employee affecte…0.0cust…1.3empl…2.6exec…0.0part…

    A technology dashboard

    The data_compromised rate, employee_affected_count by target_group and a breakdown of location_country. Excel, Power BI or Tableau.

  • Why do the 46 executives rows have a mean employee_affected_count of 2.6?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable incidents with report_date, incident_type and target_group to fill a screen in front of a buyer.

Not quite right?

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This dataset500 rows21 columns
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blueprint · phishing-and-social-engineering-incidents

Behind this dataset

Same schema. As many rows as you need.

These 500 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Incident report unique ID
  • Attack vector: email, SMS, phone call, social media
  • Pretexting scenario documented
  • Customer fell victim or reported proactively
  • Information disclosed by customer
  • Financial loss if any
  • Fraudulent transactions reversed
  • Account security measures enhanced
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
phishing-and-social-engineering-incidents

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