Overview

DataBreaches.net is one of the longest-running and most respected independent sources covering data breaches and data security incidents worldwide. Operated by privacy advocate and security researcher Dissent Doe (a pseudonymous journalist), the site has been tracking and reporting on data breaches since 2009, establishing itself as a critical early warning system and historical archive for the information security community. Unlike vendor-driven threat intelligence feeds, DataBreaches.net provides independent, investigative journalism on breach incidents, often uncovering details and publishing reports that mainstream media and even the breached organizations themselves have not yet disclosed.

For OSINT analysts, DataBreaches.net serves multiple intelligence functions. It acts as a near-real-time breach notification source that frequently reports incidents before official disclosures, provides detailed investigative analysis that goes beyond press releases and regulatory filings, maintains a historical archive enabling longitudinal analysis of breach trends, and tracks threat actor claims with independent verification efforts that help separate genuine breaches from fabricated claims.

The site's coverage is particularly strong in the healthcare sector (HIPAA-regulated entities in the United States), educational institutions, government agencies, and small-to-medium businesses that typically receive less attention from commercial threat intelligence providers. This focus on underreported breach categories makes it a valuable complement to commercial breach databases and vendor threat intelligence feeds.

DataBreaches.net also actively engages with threat actors on dark web forums, providing validated intelligence on ransomware group activities, data leak claims, and emerging threat actor techniques. This engagement, conducted within ethical journalism boundaries, provides intelligence that would otherwise require direct dark web monitoring capabilities.

Data Sources and Coverage

DataBreaches.net draws intelligence from a diverse array of sources, making it a meta-intelligence platform in its own right:

Source Category	Examples	Intelligence Value
Regulatory Filings	HHS breach portal, state AG notifications, GDPR enforcement actions	Official confirmation and scope data
Dark Web Monitoring	Ransomware leak sites, hacker forums, paste sites	Early warning, threat actor attribution
Direct Investigation	Independent research, source interviews, document analysis	Unique intelligence not available elsewhere
Public Disclosures	Company press releases, SEC filings, news reports	Confirmation and context
Court Records	Lawsuits, class actions, regulatory proceedings	Legal impact and settlement data
Freedom of Information	FOIA requests, public records requests	Government breach details

Coverage by Sector

Sector	Coverage Depth	Notable Focus Areas
Healthcare	Comprehensive	HIPAA breaches, patient data exposure, ransomware impact on care delivery
Education	Strong	K-12 and university breaches, student data protection failures
Government	Strong	Municipal, state, and federal agency incidents
Finance	Moderate	Banking incidents, fintech breaches, payment card compromises
Retail	Moderate	Point-of-sale breaches, e-commerce incidents
Technology	Good	SaaS provider breaches, cloud misconfigurations, supply chain incidents
Legal	Good	Law firm breaches, particularly those affecting privileged communications

API Integration and Data Collection

DataBreaches.net does not provide a formal API. The Prismatic Platform implements structured intelligence collection through RSS feed monitoring, web content extraction, and natural language processing of breach reports.

defmodule Prismatic.Osint.DataBreachesNet do
  @moduledoc """
  DataBreaches.net OSINT adapter for breach intelligence collection.

  Monitors and processes breach reports from DataBreaches.net, extracting
  structured intelligence including affected entities, threat actors,
  breach scope, and regulatory impact.
  """

  @feed_url "https://databreaches.net/feed/"

  @doc """
  Search for breach reports mentioning a specific entity.
  Returns structured breach intelligence with extracted indicators.
  """
  @spec search(String.t(), keyword()) :: {:ok, [map()]} | {:error, term()}
  def search(entity, opts \\ []) do
    since = Keyword.get(opts, :since, Date.add(Date.utc_today(), -365))
    limit = Keyword.get(opts, :limit, 50)

    with {:ok, articles} <- search_articles(entity, since: since, limit: limit),
         parsed <- Enum.map(articles, &extract_breach_intelligence/1) do
      {:ok, parsed}
    end
  end

  @doc """
  Retrieve recent breach reports filtered by sector.
  Returns structured breach summaries with threat actor attribution.
  """
  @spec by_sector(atom(), keyword()) :: {:ok, [map()]} | {:error, term()}
  def by_sector(sector, opts \\ []) do
    limit = Keyword.get(opts, :limit, 20)

    with {:ok, articles} <- fetch_sector_articles(sector, limit: limit),
         parsed <- Enum.map(articles, &extract_breach_intelligence/1) do
      {:ok, parsed}
    end
  end

  @doc """
  Track threat actor activity based on DataBreaches.net reporting.
  Returns a structured activity timeline with attributed incidents.
  """
  @spec threat_actor(String.t(), keyword()) :: {:ok, map()} | {:error, term()}
  def threat_actor(actor_name, opts \\ []) do
    since = Keyword.get(opts, :since, Date.add(Date.utc_today(), -180))

    with {:ok, articles} <- search_articles(actor_name, since: since),
         activity <- extract_threat_actor_timeline(articles, actor_name) do
      {:ok, %{
        actor: actor_name,
        incidents: activity.incidents,
        sectors_targeted: activity.sectors,
        techniques_observed: activity.techniques,
        timeline: activity.timeline,
        last_active: activity.last_seen,
        source: :databreaches_net,
        collected_at: DateTime.utc_now()
      }}
    end
  end

  @doc """
  Monitor RSS feed for new breach reports matching specified criteria.
  Supports continuous monitoring with configurable alert thresholds.
  """
  @spec monitor(keyword()) :: {:ok, pid()} | {:error, term()}
  def monitor(opts) do
    entities = Keyword.get(opts, :entities, [])
    sectors = Keyword.get(opts, :sectors, [])
    callback = Keyword.fetch!(opts, :callback)

    Prismatic.Osint.FeedMonitor.subscribe(%{
      feed_url: @feed_url,
      check_interval: :timer.minutes(15),
      filters: %{entities: entities, sectors: sectors},
      callback: callback
    })
  end

  defp extract_breach_intelligence(article) do
    %{
      title: article.title,
      url: article.url,
      published: article.published_at,
      entities_mentioned: extract_entities(article.content),
      threat_actors: extract_threat_actors(article.content),
      breach_type: classify_breach_type(article.content),
      records_affected: extract_record_count(article.content),
      sector: classify_sector(article.content),
      regulatory_actions: extract_regulatory_refs(article.content),
      summary: generate_intelligence_summary(article.content)
    }
  end
end

Query Examples

Practical intelligence collection scenarios using the DataBreaches.net adapter:

# Search for breaches affecting a specific organization
{:ok, reports} = Prismatic.Osint.DataBreachesNet.search("Acme Healthcare",
  since: ~D[2024-01-01],
  limit: 20
)

# Monitor healthcare sector breaches
{:ok, healthcare_breaches} = Prismatic.Osint.DataBreachesNet.by_sector(:healthcare,
  limit: 50
)

# Track LockBit ransomware group activity
{:ok, lockbit} = Prismatic.Osint.DataBreachesNet.threat_actor("LockBit",
  since: ~D[2024-06-01]
)

# Set up continuous monitoring for supply chain entities
{:ok, _monitor} = Prismatic.Osint.DataBreachesNet.monitor(
  entities: ["Vendor Corp", "Supplier Inc", "Partner LLC"],
  sectors: [:healthcare, :technology],
  callback: &Prismatic.Alerts.breach_notification/1
)

# Cross-correlate with other threat intelligence sources
{:ok, db_reports} = Prismatic.Osint.DataBreachesNet.search("Target Corp")
{:ok, hibp_data} = Prismatic.Osint.HaveIBeenPwned.domain("target-corp.com")
{:ok, vt_data} = Prismatic.Osint.Virustotal.domain("target-corp.com")

correlation = %{
  entity: "Target Corp",
  breach_reports: length(db_reports),
  credential_exposures: hibp_data.breach_count,
  malware_associations: length(vt_data.detected_urls),
  risk_assessment: calculate_composite_risk(db_reports, hibp_data, vt_data)
}

Use Cases

Threat Intelligence and Early Warning

DataBreaches.net frequently publishes breach reports before official disclosures by the breached organizations. This early warning capability is critical for organizations that need to assess supply chain exposure, monitor third-party risk, or respond to incidents affecting their data held by external parties. By integrating DataBreaches.net monitoring into the threat intelligence pipeline, the Prismatic Platform can generate alerts when supply chain partners, service providers, or industry peers experience data breaches.

The site's independent verification of threat actor claims adds significant value over raw dark web monitoring. When a ransomware group claims to have breached an organization, DataBreaches.net often investigates the claim's legitimacy, providing analysts with validated intelligence rather than unverified threat actor assertions.

Risk Assessment and Insurance

Breach frequency and severity data from DataBreaches.net supports quantitative risk assessment. By analyzing sector-specific breach rates, average record counts affected, regulatory penalty amounts, and class action settlement values, organizations can build data-driven risk models for cyber insurance underwriting, board-level risk reporting, and security investment justification.

The historical archive enables longitudinal trend analysis showing how breach patterns evolve over time, which sectors face increasing or decreasing risk, and how regulatory enforcement patterns change across jurisdictions.

Incident Response Context

When responding to a security incident, context from DataBreaches.net can significantly accelerate the response. If a threat actor is identified, historical reporting on that actor's tactics, techniques, and procedures (TTPs) provides immediate intelligence on what data they typically target, how they exfiltrate and monetize stolen data, their negotiation patterns in ransomware scenarios, and the typical timeline from compromise to public disclosure.

Regulatory Intelligence

DataBreaches.net's tracking of regulatory enforcement actions provides intelligence on how regulators in different jurisdictions respond to data breaches. This includes tracking HHS enforcement of HIPAA penalties, FTC actions against companies with inadequate security practices, GDPR enforcement by EU data protection authorities, and state attorney general actions in the United States. This regulatory intelligence supports compliance planning and breach response strategy.

Competitive and Market Intelligence

Breach reporting can reveal competitive intelligence about industry peers, including the maturity of their security programs, their choice of technology vendors and service providers, their incident response capabilities and timelines, and their regulatory compliance posture as revealed through enforcement actions.

Limitations and Constraints

Limitation	Impact	Mitigation
No official API	Collection requires RSS monitoring and web parsing	Robust RSS processing with NLP extraction
Single journalist operation	Coverage gaps possible, particularly non-English sources	Supplement with commercial threat intelligence feeds
Publication delay	Reports may lag behind real-time dark web activity	Combine with direct dark web monitoring where authorized
Subjective editorial judgment	Story selection reflects editor's priorities	Track multiple breach notification sources
Unstructured data	Reports are narrative text, not structured databases	NLP extraction pipeline with entity recognition
US-centric coverage	Stronger coverage of US incidents vs. global	Supplement with region-specific breach sources

Legal and Ethical Considerations

DataBreaches.net publishes information in the public interest, operating as a journalistic outlet focused on data security incidents. The Prismatic Platform's collection from this source operates within standard open-source intelligence boundaries. All data collected is publicly available through the website and RSS feeds. No authentication bypass or restricted access is involved.

When processing breach reports, the Prismatic Platform handles personally identifiable information extracted from breach narratives in compliance with GDPR and applicable data protection regulations. Victim notification data is processed only where there is a legitimate security or due diligence purpose. The platform implements data minimization, retaining only structured intelligence extractions rather than full article content, and applies appropriate retention periods.

Threat actor attribution information is handled with appropriate confidence levels, recognizing that early-stage attribution may be revised as investigations progress. The platform distinguishes between confirmed, probable, and claimed attributions in its intelligence products.

Platform Integration

DataBreaches.net feeds into the Prismatic Platform's threat intelligence correlation engine, contributing breach awareness signals that are cross-referenced with vulnerability data from NVD, credential exposure from Have I Been Pwned and DeHashed, threat indicators from ThreatFox, and attack surface data from the Prismatic Perimeter EASM module.

defmodule Prismatic.Pipeline.BreachIntelligence do
  @moduledoc """
  Breach intelligence pipeline correlating DataBreaches.net reports
  with other threat intelligence sources for comprehensive breach awareness.
  """

  def assess_entity_breach_exposure(entity_name, domain) do
    with {:ok, breach_reports} <- Prismatic.Osint.DataBreachesNet.search(entity_name),
         {:ok, credential_exposure} <- Prismatic.Osint.HaveIBeenPwned.domain(domain),
         {:ok, dehashed_results} <- Prismatic.Osint.DeHashed.search("email:@#{domain}") do
      %{
        entity: entity_name,
        breach_report_count: length(breach_reports),
        credential_exposures: credential_exposure.breach_count,
        dehashed_records: dehashed_results.total,
        risk_level: calculate_breach_risk(breach_reports, credential_exposure),
        latest_incident: latest_breach_date(breach_reports),
        recommendation: generate_breach_response_recommendation(breach_reports)
      }
    end
  end
end

Best Practices

Effective use of DataBreaches.net in OSINT operations requires understanding its strengths and appropriate role in a multi-source intelligence framework. Treat it as a high-quality early warning and investigative source rather than a comprehensive breach database. Establish automated RSS monitoring with entity and keyword matching to catch relevant reports promptly. Always cross-reference breach claims reported on the site with official disclosures and regulatory filings for confirmation.

When using DataBreaches.net for threat actor tracking, maintain temporal context for attributions that may evolve as investigations progress. Build threat actor profiles incrementally, updating confidence levels as additional reporting confirms or contradicts initial claims. Integrate findings with MITRE ATT&CK TTP mapping for structured threat actor characterization.

For supply chain risk monitoring, maintain a watchlist of critical third parties and configure automated alerts through the Prismatic Platform's monitoring framework. Historical breach patterns of supply chain partners should inform vendor risk assessments and contract security requirements.

Have I Been Pwned - Technical breach data and credential exposure checking
DeHashed - Breach data search engine for credential intelligence
Intelligence X - Dark web breach data indexing and historical content
NVD - Vulnerability data for understanding breach root causes
MITRE ATT&CK - TTP framework for threat actor characterization
ThreatFox - IOC sharing for malware and C2 infrastructure
VirusTotal - Multi-engine malware analysis for breach-related samples

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Created by Tomáš Korcak (korczis) | Open Source under GHL

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DataBreaches.net

Capabilities

Try DataBreaches.net

Overview

Data Sources and Coverage

Coverage by Sector

API Integration and Data Collection

Query Examples

Use Cases

Threat Intelligence and Early Warning

Risk Assessment and Insurance

Incident Response Context

Regulatory Intelligence

Competitive and Market Intelligence

Limitations and Constraints

Legal and Ethical Considerations

Platform Integration

Best Practices

Connect & Contribute

Intelligence Network

Related Content

Agent Ecosystem

Applications

Doctrine

DataBreaches.net

Capabilities

Try DataBreaches.net

Overview

Data Sources and Coverage

Coverage by Sector

API Integration and Data Collection

Query Examples

Use Cases

Threat Intelligence and Early Warning

Risk Assessment and Insurance

Incident Response Context

Regulatory Intelligence

Competitive and Market Intelligence

Limitations and Constraints

Legal and Ethical Considerations

Platform Integration

Best Practices

Related Sources

Connect & Contribute

Intelligence Network

Related Content

Agent Ecosystem

Applications

Doctrine