Security Tool Governance

5 automated security scanners

Visibility Gap Creation

Purpose: The Visibility Gap Creation Scanner is designed to analyze breach disclosure language and identify intentional blind spots, monitoring exclusions, and audit avoidance tactics used by organizations. It aims to detect where companies intentionally omit critical information about breaches, downplay the severity of incidents, or avoid accountability through various tactics such as blame deflection, passive voice usage, minimization of impact, third-party blame patterns, and employee scapegoating.

What It Detects:

Blame Deflection Patterns: This includes nation-state actor claims without evidence, state-sponsored attacks, highly sophisticated attacks, unprecedented levels of breaches, and zero-day exploits without details.
Passive Voice Usage: Systems were accessed, data were compromised, information was obtained, and determinations have been made are detected using specific patterns in the text.
Minimization of Impact: Patterns such as limited number of affected parties, no evidence of broader impact, abundance of caution statements, and potentially affected users are flagged by this scanner.
Third-Party Blame Patterns: This includes vendor/partner responsibility shifting, supply chain attack framing, managed service provider blame, contractor/consultant scapegoating, and outsourcing as a primary explanation.
Employee Scapegoating: Rogue employee or insider framing, individual termination announcements, lack of systemic control failure acknowledgment, HR action emphasis over security gaps, and isolated incident framing are detected through specific language usage.

Inputs Required:

domain (string): The primary domain to analyze, which helps in searching the company’s site for breach disclosure statements.
company_name (string): The name of the company is used for statement searching and identifying relevant information within the text.

Business Impact: This scanner is crucial as it aids in uncovering hidden vulnerabilities and ensuring that organizations are transparent about their security incidents, which directly impacts trust, regulatory compliance, and public confidence in cybersecurity measures.

Risk Levels:

Critical: Findings indicating direct state-sponsored attacks or highly sophisticated breaches without detailed evidence can be critical.
High: Significant minimization of impact or passive language usage that conceals the severity of an incident is considered high risk.
Medium: Patterns such as abundance of caution statements or limited number of affected parties, while not as severe, still indicate potential issues warranting attention.
Low: Informational findings may include instances where terminology could be clarified for better understanding but does not pose immediate critical risks.
Info: These are generally recommendations for improving transparency and clarity in communication without being directly tied to high risk scenarios.

Example Findings:

“The company failed to mention the specific number of users affected by a data breach, suggesting minimization of impact.”
“A statement deflects blame to a third party with vague language that avoids accountability.”

This structured output format helps stakeholders understand the purpose and capabilities of the scanner, as well as the potential risks it identifies in a clear and actionable manner.

Budget Allocation Politics

Purpose: The Budget Allocation Politics Scanner is designed to analyze organizational disclosures, press releases, and financial statements to detect disproportionate investments, resource hoarding, funding inequities, and potential misallocation of security resources within an organization. This tool helps in identifying hidden biases and inefficiencies in budget allocation that could impact the overall security posture of a company.

What It Detects:

Disproportionate Investment Claims: Excessive emphasis on specific departments or initiatives without adequate justification or evidence of significant investment in security compared to other areas.
Resource Hoarding Indicators: Statements suggesting monopolization of resources by certain teams or projects, and limitations on resource sharing across the organization.
Funding Inequities: Discrepancies between budget allocations as described in disclosures and actual needs, particularly underfunding of critical security areas relative to non-security initiatives.
Misallocation of Security Resources: Claims of robust security measures without concrete evidence of effective implementation or overemphasis on certain technologies or solutions while neglecting others based on identified threats or vulnerabilities.
Lack of Transparency in Budgeting: Vague statements about budget allocations, omission of specific details regarding security investments, and overall lack of clarity in financial disclosures related to security expenditures.

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 uncovering hidden biases and inefficiencies in budget allocation that can lead to misallocation of resources, potentially compromising the organization’s security posture. It ensures transparency and fairness in financial disclosures related to security expenditures, which is essential for maintaining a balanced and robust security strategy.

Risk Levels:

Critical: Conditions where there are clear discrepancies between stated investments and actual reported expenses that significantly impact the organization’s security budget or priorities.
High: Situations where resource allocation does not align with organizational threats or vulnerabilities, potentially leading to ineffective security measures.
Medium: Findings indicating potential misallocation of resources without strong evidence but still indicative of inefficient budgeting practices.
Low: Informal or vague statements about budget allocations that do not provide clear indications of misallocation but could be improved for clarity and specificity.
Info: General informational findings that do not necessarily indicate a significant issue but might suggest areas for better transparency or clarification in financial disclosures.

Example Findings:

“The company claims robust investment in AI technology without sufficient evidence to support this claim, potentially misallocating funds from other critical security areas.”
“There is evidence of exclusive access to funding and personnel within the cybersecurity team, which could indicate resource hoarding and underfunding of other essential security measures.”

Vendor Relationship Politics

Purpose: The Vendor Relationship Politics Scanner is designed to analyze organizational language in disclosures, press releases, and security incident pages to detect potential biases in procurement decisions. It helps identify if vendors are favored over technical merit or if decisions are influenced by factors unrelated to product quality and security.

What It Detects:

Relationship-Driven Procurement: The scanner identifies instances where a company expresses loyalty to specific vendors through statements like “long-standing partnership” or mentions of preferred suppliers without clear technical justification.
Non-Technical Selection Criteria: It detects an emphasis on non-security features such as “user-friendly interface” and vague performance claims that do not focus on technical specifications or security certifications.
Personal Preference Procurement: The scanner flags instances where personal preferences influence procurement decisions, indicated by phrases like “we chose this vendor because…” without organizational rationale.
Vendor Blame Patterns: It identifies attempts to shift responsibility for incidents onto vendors in reports and communications, including blaming managed service providers or third-party contractors.
Lack of Technical Justification: The scanner flags instances where product names are prominently featured without accompanying technical details or when zero-day exploits are emphasized without detailed Common Vulnerabilities and Exposures (CVE) information.

Inputs Required:

domain (string): The primary domain to analyze, such as “acme.com,” which helps in searching for relevant disclosure statements on the company’s website.
company_name (string): The name of the company being analyzed, used for identifying specific vendor mentions and other related statements during the analysis process.

Business Impact: This scanner is crucial as it helps organizations maintain a fair and objective evaluation of vendors, ensuring that procurement decisions are based on technical merit rather than personal relationships or non-technical criteria. Misleading language in disclosures can lead to poor security posture, vendor lock-in, and increased costs due to suboptimal technology choices.

Risk Levels:

Critical: The scanner identifies instances where the company’s website contains statements that directly imply favoritism towards a specific vendor without technical justification or competitive bidding processes.
High: There is an emphasis on non-technical features in procurement decisions, vague performance claims are made without supporting evidence, or personal preferences override objective evaluation criteria.
Medium: The scanner detects instances where the company’s language suggests a preference for certain vendors based on subjective factors such as ease of use or cost rather than detailed technical assessments.
Low: Informal decision-making processes and lack of clear explanations for vendor selections can be flagged by this scanner, indicating potential areas for improvement in procurement policies and practices.
Info: The scanner identifies instances where the company’s language does not explicitly indicate favoritism but still leans towards certain vendors without sufficient technical rationale or competitive process involvement.

Example Findings:

“The company frequently mentions ‘long-standing partnership’ with Vendor X in its press releases, suggesting a preference that is not supported by any specific performance metrics.”
“In the security incident report, the language shifts responsibility to Vendor Y without detailed technical analysis of the cause of the breach.”

Inter-team Tool Competition

Purpose: The Inter-team Tool Competition Scanner is designed to identify and analyze discrepancies in tool usage and communication practices among different teams within an organization. This tool aims to uncover parallel toolsets, team-specific implementations, siloed security stacks, lack of coordination language, and redundant tool usage by examining the language used in public disclosures, press releases, and other communications.

What It Detects:

Parallel Toolset Indicators: Detection of multiple mentions or overlapping functionalities of different tools (e.g., Splunk and ELK for log management).
Team-Specific Implementations: Recognition of team-specific language or acronyms related to tool usage, such as YARA rules used by the Red Team or Nessus for vulnerability scanning by the Blue Team.
Siloed Security Stacks: Identification of separate descriptions of security tools without integration mentions, indicating potential disjointed practices (e.g., Fortinet for network security and CrowdStrike for endpoint protection).
Lack of Coordination Language: Recognition of phrases suggesting independent operations or isolated incident responses, which may indicate a lack of cross-team collaboration.
Redundant Tool Usage: Identification of repeated tool mentions without logical justification, potentially indicating inefficient practices (e.g., using both Splunk and its Enterprise Security version for threat hunting).

Inputs Required:

domain (string): The primary domain to analyze, which helps in searching the company site for relevant incident disclosures.
company_name (string): The name of the company, used for statement searching and generating meaningful findings related to internal practices.

Business Impact: This scanner is crucial for understanding the integration and coordination among various security teams within an organization. Inefficient tool usage can lead to redundant deployments, increased costs, and potential gaps in overall security posture. Detecting these discrepancies early allows for proactive interventions to improve communication, resource allocation, and strategic alignment across different teams.

Risk Levels:

Critical: Conditions where multiple tools are mentioned without clear integration or overlapping functionalities that could lead to significant operational inefficiencies.
High: Isolated deployments of specific tools within a team’s scope, potentially indicating siloed practices that hinder cross-team collaboration and information sharing.
Medium: Repeated mentions of the same tool across different contexts without evident benefits, suggesting potential redundancy in tool usage.
Low: Minimal instances where tools are mentioned without clear implications for security operations or coordination among teams.
Info: Informal mentions that do not directly indicate any operational issues but can be useful for informational purposes to understand team practices better.

If the README doesn’t specify exact risk levels, infer them based on the scanner’s purpose and impact.

Example Findings:

“The Red Team uses both YARA rules and Nessus for vulnerability scanning, indicating potential redundancy in tool usage without clear justification.”
“Our network security is handled by Fortinet, while endpoint protection uses CrowdStrike, suggesting siloed practices that could affect overall security posture.”

Tool Sprawl Detection

Purpose:
The Tool Sprawl Detection Scanner is designed to identify and analyze duplicate functionality, overlapping capabilities, and redundant implementations within an organization’s toolset. By doing so, it aims to optimize security operations and minimize unnecessary costs associated with maintaining multiple tools for similar purposes.

What It Detects:

Duplicate Tools: Identifies multiple tools performing the same function, which can lead to resource duplication and inefficiencies.
- Example pattern: duplicate\\s+functionality
Overlapping Capabilities: Detects tools with similar capabilities that could be consolidated for better management and interoperability.
- Example pattern: similar\\s+features
Redundant Implementations: Finds instances where the same security measure is implemented multiple times using different tools, potentially wasting resources and complicating maintenance.
- Example pattern: redundant\\s+implementations
Inconsistent Tool Usage: Identifies inconsistencies in tool usage across departments or teams, which can lead to suboptimal use of available tools and potential gaps in security coverage.
- Example pattern: inconsistent\\s+usage
Outdated Tools: Detects tools that are outdated and may not be supported or effective in current security environments.
- Example pattern: outdated\\s+software

Inputs Required:

domain (string): The primary domain to analyze, which helps in identifying relevant public records and open-source intelligence sources related to the company’s operations.
- Example: acme.com
company_name (string): The name of the company for which the analysis is being conducted. This aids in searching for mentions of tools within the company’s repositories or public statements.
- Example: “Acme Corporation”

Business Impact:
The identification and consolidation of duplicate functionality, overlapping capabilities, and redundant implementations through this scanner can significantly enhance an organization’s security posture by reducing unnecessary tool usage, optimizing resource allocation, and ensuring that tools are aligned with the latest technological advancements. This leads to more efficient operations and better risk management within the cybersecurity framework.

Risk Levels:

Critical: Tools identified as having overlapping capabilities or redundant implementations without clear justifications may lead to significant security vulnerabilities and operational inefficiencies.
High: Inconsistent tool usage across departments can result in inconsistent security standards and potential gaps in coverage for critical functions.
Medium: Outdated tools not only waste resources but also expose the organization to unsupported technology risks, potentially compromising its security posture.
Low: Duplicate tools may initially seem redundant but are often necessary for specific operational needs that other tools do not cover adequately. This risk level is generally low unless such tools significantly overlap or duplicate core functionalities.
Info: Informational findings about tool usage patterns in public repositories can provide insights into the organization’s technological footprint and areas where further exploration might be beneficial, though they pose no immediate security risks.

Example Findings:

“Tool X and Tool Y both perform network scanning, which is redundant given that only one tool should handle such functions.”
“Both Tool A and Tool B offer intrusion detection features, suggesting consolidation to avoid overlap in capabilities.”
“Firewall rules are defined in both Tool C and Tool D across different departments, leading to potential inconsistencies and inefficiencies.”

This structured approach helps users understand the purpose, inputs, business impact, risk levels, and examples of findings from using the tool sprawl detection scanner effectively.