Team Dynamics

5 automated security scanners

Security Responsibility Disputes

Purpose: The Security Responsibility Disputes Scanner is designed to analyze breach disclosure language and other relevant statements for signs of blame deflection tactics. It aims to detect when organizations attempt to shift accountability to external actors, technology vendors, or employees by using phrases such as “nation-state actor,” “highly sophisticated,” or blaming third-party vendors without providing technical justification.

What It Detects:

Blame Deflection Patterns:
- Nation-State Actor Claims: Phrases like “nation-state actor”, “state-sponsored” are used to deflect blame onto external entities, indicating an attempt to shift responsibility from internal failures.
- Sophistication Claims: Terms such as “highly sophisticated”, “unprecedented level”, or “zero-day exploit” are employed without sufficient technical evidence to support the claim.
Third-Party Blame Patterns:
- Vendor/Partner Responsibility Shifting: Language like “third-party vendor”, “managed service provider”, or “contractor” is used to frame responsibility for security incidents, shifting blame away from internal failures.
- Supply Chain Attack Framing: Emphasis on supply chain attacks as the cause of a breach rather than acknowledging internal security measures that could have prevented such an attack.
Employee Scapegoating:
- Rogue Employee Framing: Use of terms like “rogue employee”, “insider threat” without considering broader access control issues within the organization.
- Individual Termination Announcements: Focus on individual actions rather than systemic failures in security protocols and policies.
Passive Voice and Vagueness:
- Passive Construction Frequency: Sentences using passive voice, such as “was accessed”, “were compromised”, can obscure the actual cause of an incident, allowing for blame deflection.
- Unclear Causality Statements: Phrases like “has been determined” without clear attribution or evidence to support the claim can be misleading in discussions about security incidents.
Technology Failure Emphasis:
- Product/Vendor Name Prominence: Overemphasis on specific products or vendors, potentially overlooking broader issues such as configuration errors or lack of necessary updates.
- Zero-Day Exploit Focus: Mention of zero-day exploits can be sensationalized without providing details about the actual vulnerability exploited or how it was mitigated post-breach.

Inputs Required:

domain (string): Primary domain to analyze, representing the main website for which breach disclosure statements are being searched.
company_name (string): The name of the company whose security incidents are being analyzed, used for searching relevant breach disclosure statements on their site.

Business Impact: This scanner is crucial as it helps organizations identify and address the tactics that adversaries use to deflect responsibility from systemic security failures, policy gaps, or leadership negligence. Correcting these issues can lead to improved security posture, better risk management, and more effective incident response strategies.

Risk Levels:

Critical: Conditions where blame deflection patterns are detected without sufficient technical evidence supporting the claims of nation-state actors or sophisticated attacks.
High: When third-party vendor responsibility is shifted without providing clear justification for such a shift in the context of supply chain security.
Medium: Passive voice usage in breach disclosure statements that obscure actual causes, potentially allowing for blame deflection.
Low: Minimal impact findings related to vague or unclear causality statements and when specific products or vendors are mentioned without significant influence on overall risk assessment.
Info: Informational findings such as the presence of third-party vendor names in statements where no clear shift in responsibility is evident, or minimal use of passive voice that does not significantly affect understanding of the incident.

Example Findings:

“The company announced a data breach without specifying if it was due to nation-state actor involvement or sophisticated cyber attack.”
“A third-party vendor was mentioned as responsible for a security incident, but no technical evidence supports this claim.”

Career Advancement Politics

Purpose: The Career Advancement Politics Scanner is designed to analyze breach disclosure language and implementation details in order to detect visibility-driven security work, resume-driven implementation, and credit-seeking behavior. This tool helps organizations identify when priorities are placed on public appearances and personal gains over effective security practices.

What It Detects:

Blame Deflection Patterns: The scanner identifies claims of nation-state actors without evidence, uses vague descriptions like “highly sophisticated” or “unprecedented level,” and flags zero-day exploits mentioned without CVE details.
Passive Voice Usage: It looks for passive constructions such as “was accessed,” “were compromised,” and “has been determined.”
Minimization of Impact: The scanner detects phrases that downplay the severity, such as “limited number of” or “no evidence of,” and uses cautionary language like “out of an abundance of caution.”
Third-Party Blame Patterns: It checks for vendor/partner responsibility shifting, looks for supply chain attack framing, and blames third-party vendors.
Employee Scapegoating: The scanner identifies “rogue employee” or “insider” framing and detects individual termination announcements without acknowledging systemic failures.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com) - This is necessary for searching company sites for incident disclosures.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”) - This helps in identifying relevant breach disclosure statements during the search process.

Business Impact: Identifying and addressing these patterns is crucial as they can lead to misallocation of resources, ineffective security strategies, and a false sense of security. Proper handling of such disclosures ensures that organizations focus on robust security measures rather than superficial public relations efforts.

Risk Levels:

Critical: Conditions where the scanner identifies clear evidence of nation-state involvement without proper attribution or detailed information.
High: When vague descriptions or unsubstantiated claims are present, potentially misleading stakeholders about the severity and nature of the breach.
Medium: Use of passive voice in disclosures that obfuscate responsibility or impact, and cases where impacts are downplayed significantly.
Low: Minimal use of cautionary language without substantial impact on security posture, or when third-party vendor responsibilities are accurately assigned.
Info: Informal findings indicating potential issues that may need further investigation for clarification but do not pose immediate critical risks.

Example Findings:

“The company claimed a data breach occurred due to highly sophisticated nation-state actor involvement without providing supporting evidence.”
“A security incident was reported as ‘was accessed’ without specifying the exact nature or extent of the intrusion.”

Risk Assessment Politicization

Purpose: The Risk Assessment Politicization Scanner is designed to analyze breach disclosure language and detect patterns that indicate subjective risk inflation or deflation. It helps organizations identify whether they are exaggerating risks for public relations purposes or minimizing them to avoid accountability.

What It Detects:

Blame Deflection Patterns:
- Phrases like “nation-state actor,” “state-sponsored,” indicating external blame without evidence.
- Mentioning specific APT groups (e.g., Fancy Bear, Lazarus) without technical justification.
- Overuse of terms like “sophisticated” or “advanced” without detailed attack vectors.
- Shifting responsibility to vendors or partners without acknowledging internal failures.
- Framing incidents as due to rogue employees or insiders, avoiding systemic issues.
Passive Voice Usage:
- Sentences like “systems were accessed,” “data was compromised,” which avoid direct accountability.
- Descriptions lacking clear actors responsible for the breach.
- Unclear causality statements about how breaches occurred, avoiding specific causes.
Minimization Language:
- Phrases like “limited number of affected,” downplaying the scale of the incident.
- Using phrases like “no evidence of” to dismiss potential issues.
- Overusing statements like “out of an abundance of caution.”
- Vague language about who might be impacted, avoiding specific numbers.
Threat Exaggeration:
- Using terms like “unprecedented level” to inflate the severity of the breach.
- Mentioning zero-day exploits without providing CVE details or technical context.
- Overusing APT terminology without concrete evidence.
Impact Exaggeration:
- Using terms like “highly sophisticated,” “unprecedented,” to exaggerate the impact of the breach.
- Focusing on product or vendor failures without acknowledging policy gaps or configuration issues.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial for organizations as it helps in identifying and addressing the manipulation of risk assessments, which can lead to misinformation and ineffective security strategies. It ensures that risks are accurately reported and managed, contributing to a more transparent and responsible approach to cybersecurity disclosures.

Risk Levels:

Critical: Conditions where there is clear evidence of intentional risk inflation for public relations or strategic gain without substantiated technical details.
High: Situations where the scanner detects passive voice usage extensively in breach descriptions, indicating potential minimization of risks.
Medium: Findings that suggest a mix of blame deflection and minimization language, requiring further investigation into the accuracy and transparency of risk disclosures.
Low: Minimal instances of overuse of sophisticated or unprecedented descriptors, generally indicative of routine risk communication practices.
Info: Informal findings such as isolated use of vendor-related terms without clear strategic intent, typically considered minor in the context of overall risk assessment practices.

Example Findings:

“The company claimed that their systems were accessed by a nation-state actor despite lacking concrete evidence.”
“A breach description used passive voice constructions extensively, minimizing the severity of the data compromise.”
“Risk assessments exaggerated the impact of the cyber attack to heighten public concern about potential losses.”

Cross-functional Integration Barriers

Purpose: The Cross-functional Integration Barriers Scanner is designed to analyze breach disclosure language and uncover signs of internal barriers that hinder effective response and recovery from security incidents. By identifying linguistic patterns in disclosures, press releases, and other public communications, the scanner helps organizations detect collaboration resistance, information hoarding, and process isolation within their ranks.

What It Detects:

Blame Deflection Patterns: The scanner identifies nation-state actor claims, sophistication claims without concrete evidence, and zero-day exploits that lack specific CVE details.
Passive Voice Usage: It detects passive constructions such as “was accessed”, “were compromised”, and “has been determined” which avoid direct accountability.
Minimization of Impact: The scanner picks up on limited scope claims and no evidence assertions, downplaying the extent of the breach or the consequences thereof.
Third-party Blame Patterns: It flags vendor/partner responsibility language and supply chain attacks that do not address internal controls.
Employee Scapegoating: The scanner identifies rogue employee framing and individual termination announcements that evade addressing systemic issues within the organization.

Inputs Required:

domain (string): Primary domain to analyze, such as acme.com. This helps in searching for incident disclosures on the company’s website.
company_name (string): Company name for statement searching, e.g., “Acme Corporation”. This aids in identifying relevant breach disclosure statements during the analysis phase.

Business Impact: Detecting internal barriers within organizations is crucial as it directly impacts an organization’s ability to respond effectively and efficiently to security incidents. By uncovering collaboration resistance, information hoarding, and process isolation, the scanner helps prevent potential damage from escalating and ensures that organizational assets are protected more robustly against future threats.

Risk Levels:

Critical: Conditions where nation-state actors are falsely claimed, sophisticated attacks without evidence, and highly specialized vulnerabilities that evade detection are considered critical risks.
High: Passive voice usage in breach disclosures and limited scope claims indicating a lack of transparency can be classified as high severity issues.
Medium: Sophistication claims that do not match the actual complexity of the attack or minimization tactics that obfuscate the impact could be deemed medium risk.
Low: Informational findings such as third-party vendor responsibility statements without concrete evidence might be considered low risk, but still indicative of potential integration issues.
Info: Rogue employee framing and individual termination announcements are informative in terms of organizational culture but can also indicate internal control weaknesses.

Example Findings:

“The company claimed that a sophisticated cyber attack was perpetrated by a nation-state actor without providing concrete evidence, indicative of blame deflection.”
“During the investigation, it was noted that multiple systems were accessed passively mentioned in the disclosure statement, suggesting a lack of direct accountability.”
“A third-party vendor was blamed for the breach before any internal investigation could confirm or refute this claim, indicating potential mismanagement of supply chain risks.”

This structured approach to documenting findings ensures clarity and understanding of the scanner’s capabilities and limitations in enhancing organizational security posture.

Incident Attribution Disputes

Purpose: The Incident Attribution Disputes Scanner is designed to analyze breach disclosure language and other relevant statements to detect blame deflection tactics. These tactics involve shifting accountability to external actors, technology vendors, or employees rather than acknowledging systemic security failures, policy gaps, or leadership negligence.

What It Detects:

Blame Deflection Patterns:
- Nation-State Actor Claims: Phrases like “nation-state actor”, “state-sponsored”, indicating blame is shifted to external entities.
- Sophistication Claims: Terms such as “highly sophisticated”, “unprecedented level”, or “zero-day exploit” used without technical justification.
Third-Party Blame Patterns:
- Vendor/Partner Responsibility Shifting: Language like “third-party vendor”, “managed service provider”, or “contractor” blamed for the incident.
- Supply Chain Attack Framing: Mention of supply chain attacks or vulnerabilities in third-party systems.
Employee Scapegoating:
- Rogue Employee Framing: Terms such as “rogue employee”, “insider threat”, or individual termination announcements to deflect blame from systemic issues.
Passive Voice and Vagueness:
- Passive Construction Frequency: Use of passive voice constructions like “was accessed”, “were compromised”.
- Unclear Causality Statements: Phrases that avoid clear attribution, such as “has been determined” or “out of an abundance of caution”.
Technology Failure Emphasis:
- Product/Vendor Name Prominence: Overemphasis on specific technologies or vendors without acknowledging configuration issues.
- Zero-Day Exploit Claims: Mention of zero-day exploits without CVE details.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial as it helps in identifying the true sources of breaches, enabling organizations to take appropriate corrective actions and improve their security posture by addressing systemic issues rather than merely blaming external parties or individuals.

Risk Levels:

Critical: Findings that directly indicate a highly sophisticated and potentially state-sponsored attack with no technical details provided.
High: Notable blame deflection tactics, such as sophistically framed claims of zero-day exploits without accompanying CVE details.
Medium: Passive voice constructions in breach disclosure statements indicating unclear causality or lack of clear attribution to specific incidents.
Low: Emphasis on specific technology vendors without concrete evidence of systemic configuration issues.
Info: Informational findings that do not directly impact security but may indicate areas for improvement in communication practices.

Example Findings:

“The breach was disclosed with phrases suggesting a sophisticated attack, mentioning ‘highly sophisticated’ and ‘zero-day exploit’ without detailed technical evidence.”
“Statements use passive voice constructions like ‘was accessed’ and avoid clear attribution to specific actors or events.”
“Overemphasis on specific technology vendors in the report highlights potential configuration issues that need further investigation.”

This structured approach helps users understand the scanner’s capabilities, inputs, business impact, risk levels, and what it identifies in practice.