Security Metrics Integrity

5 automated security scanners

Alert Suppression Analysis

Purpose: The Alert Suppression Analysis Scanner is designed to analyze breach disclosure language and identify patterns that suggest organizations are suppressing information about incidents or avoiding full transparency. This tool helps in detecting various tactics used to downplay breaches, such as blaming deflection, using passive voice, minimizing the impact of incidents, shifting blame to third parties, and scapegoating employees.

What It Detects:

Blame Deflection Patterns: The scanner detects claims that attribute breaches to nation-state actors without providing sufficient evidence or technical justification for such allegations.
Passive Voice Usage: It recognizes statements written in passive voice, which can avoid direct accountability and obfuscate responsibility.
Minimization of Impact: The scanner identifies language that seeks to reduce the perceived severity or scope of an incident, often by downplaying the number of affected individuals or assets.

Inputs Required:

domain (string): Primary domain to analyze, which helps in searching for breach disclosure statements on the company’s website.
company_name (string): The name of the company is used to search within their site for relevant incident disclosures.

Business Impact: This scanner is crucial as it aids in maintaining transparency and trust during security incidents. By detecting and highlighting patterns that may indicate suppression or obfuscation, organizations can improve their communication strategies and ensure they are meeting regulatory requirements and stakeholder expectations regarding breach disclosure.

Risk Levels:

Critical: The risk level is critical when there is clear evidence of active attempts to avoid responsibility through deflection tactics without providing technical details or credible evidence.
High: When passive voice usage significantly obscures the incident’s nature, leading to potential accountability gaps and a lack of clarity about corrective actions.
Medium: For cases where language minimizes the impact but does not outright evade responsibility, prompting further investigation into internal processes and communication practices.
Low: Informational findings may include instances where technical details are lacking but overall transparency is maintained through proactive engagement with stakeholders.
Info: When minimal information is available or when statements do not exhibit overt signs of suppression or obfuscation but still lack detailed technical explanations.

Example Findings:

“The company claims a data breach occurred, but the statement uses passive voice and minimizes the impact without providing details about affected users.”
“A breach was attributed to a nation-state actor, but there is no evidence provided in the disclosure document to substantiate this claim.”

Vulnerability Reporting Distortion

Purpose: The Vulnerability Reporting Distortion Scanner is designed to analyze breach disclosure language and detect tactics that distort the severity of security incidents, expand exclusion criteria, and artificially limit the scope of affected systems. This tool helps identify organizations that may be downplaying vulnerabilities or misrepresenting the impact of breaches.

What It Detects:

Severity Downgrading: Patterns such as attributing breaches to sophisticated external actors without concrete evidence are detected using blame deflection patterns like “nation-state” and “highly sophisticated.” Additionally, passive voice usage is identified where constructions like “systems were accessed” instead of direct responsibility attribution.
Exclusion Criteria Expansion: This includes detection of minimization patterns that minimize the impact or scope of breaches, such as claims about limited numbers of affected users or no evidence of data exfiltration. Vague descriptors like “highly sophisticated” without specific technical details are also flagged.
Artificial Scope Limitation: The scanner identifies mentions of supply chain attacks or third-party vendors as primary causes without acknowledging internal vulnerabilities through phrases like “third-party vendor.” It also detects framing breaches as due to rogue employees or insiders, avoiding systemic security failures.
Technical Detail Omission: References to zero-day exploits are flagged without providing CVE details or technical justification. The scanner also highlights overemphasis on product or technology flaws while omitting configuration issues or policy gaps.
Responsibility Avoidance: Attempts to shift responsibility to third-party vendors or managed service providers, as well as passive voice constructions and vague language that avoid clear attribution of responsibility, are detected.

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 the accurate reporting of security incidents, ensuring that organizations do not downplay vulnerabilities or misrepresent the impact of breaches. It aids in maintaining transparency and accountability within cybersecurity practices, which are essential for building trust with stakeholders and regulatory compliance.

Risk Levels:

Critical: Conditions where there is a direct threat to critical infrastructure, significant financial loss, or high risk of data theft warranting immediate attention.
High: Conditions involving substantial exposure to risks that could lead to severe consequences if not mitigated promptly, such as major system disruptions or extensive data breaches.
Medium: Conditions with moderate risk where improvement is recommended for better security posture and prevention of potential issues.
Low: Conditions with minimal risk typically requiring minor adjustments or ongoing monitoring to maintain a secure environment.
Info: Informal findings that provide supplementary information but do not directly impact the core risk profile, useful for continuous improvement without urgent action.

If specific conditions are not detailed in the README, these risk levels can be inferred based on general cybersecurity threats and potential impacts.

Example Findings:

“The breach was attributed to a nation-state actor without concrete evidence, indicating a possible distortion of severity.”
“Language suggesting limited numbers of users affected could indicate an expansion of exclusion criteria to downplay the impact.”

Patch Compliance Exaggeration

Purpose: The Patch Compliance Exaggeration Scanner is designed to analyze breach disclosure language and identify exaggerations in definition changes, exemption increases, and shifts in measurement methodologies. This helps organizations maintain a positive public image by avoiding overstating compliance status or downplaying security issues.

What It Detects:

Definition Changes: The scanner tests for vague “unprecedented” claims without specific context, checks for “zero-day exploit” mentions without CVE details, and verifies sophisticated attack claims against actual breach vectors.
Exemption Increases: It tests for statements indicating limited impact (e.g., “limited number of affected users”) and verifies overreaching caution (“out of an abundance of caution”).
Measurement Methodology Shifts: The scanner detects shifts in how breaches are measured or reported, flags changes in terminology that suggest downplaying the severity, and tests for passive voice constructions to avoid direct responsibility.
Blame Deflection Patterns: It identifies claims of nation-state actors without evidence, mentions APT groups (e.g., Fancy Bear, Lazarus) without technical justification, and verifies sophistication claims against actual attack vectors.
Minimization of Impact: The scanner tests for downplayed scales using phrases like “potentially affected” and verifies statements indicating no broader security 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 as it helps organizations avoid overstating their compliance status and downplaying security issues, which can lead to a false sense of security and inadequate response to potential threats. It ensures transparency and integrity in public disclosures about cybersecurity incidents.

Risk Levels:

Critical: Conditions where the scanner identifies vague or exaggerated claims without specific context or evidence, potentially leading to significant misrepresentation of compliance status.
High: Situations where the scanner detects shifts in measurement methodologies that suggest downplaying the severity of security issues.
Medium: Findings related to passive voice constructions and minimization statements indicating overreach but not directly compromising critical systems.
Low: Informational findings that might indicate minor inaccuracies or nuances in breach disclosure language, generally不影响整体安全态势。
Info: Any additional findings that do not meet the criteria for higher severities, providing general insights into potential improvements in communication practices.

Example Findings:

“The company claims to have prevented a zero-day exploit affecting all users without specifying any technical details.”
“Statements suggest minimal impact but fail to provide specific numbers or evidence of affected users.”

This structured approach helps stakeholders understand the scanner’s capabilities and limitations, guiding better decision-making in cybersecurity communications.

Incident Count Manipulation

Purpose: The Incident Count Manipulation and Classification Change Scanner is designed to identify manipulative tactics used by organizations to present a more favorable security posture. It analyzes breach disclosure language, attribution claims, and responsibility framing to detect implausible reductions in reported incident counts, changes in incident classification, blame deflection patterns, minimization language, and passive voice usage that could influence security metrics.

What It Detects:

Implausible Incident Reductions: The scanner tests for sudden drops in reported incidents without corresponding evidence or explanation, checks for discrepancies between stated incident counts and historical data, and verifies the presence of detailed explanations for any reductions.
Classification Changes to Influence Metrics: It identifies changes in incident classification that could reduce perceived risk (e.g., reclassifying a major breach as a minor incident) and detects shifts from high-severity classifications to lower ones without technical justification.
Blame Deflection Patterns: The scanner tests for nation-state actor claims without evidence, checks for APT group name-dropping (Fancy Bear, Lazarus, etc.) without supporting details, verifies sophistication claims vs actual attack vectors, detects vague “sophisticated” or “advanced” descriptors, and flags attribution without technical justification.
Minimization Language: It tests for language that minimizes the impact of incidents (e.g., “limited number of affected users”) and checks for phrases indicating no evidence of broader issues (“no evidence of further compromise”).
Passive Voice Usage: The scanner tests for passive construction frequency in breach descriptions, checks for agent omission in breach descriptions, and verifies active vs passive voice ratio, detecting responsibility-avoiding language and unclear causality statements.

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 and mitigating manipulative practices that may mislead stakeholders about the actual security risks faced by an organization. Correcting these inaccuracies can significantly improve the transparency and reliability of reported security incidents, thereby enhancing overall trust and confidence in the company’s security measures.

Risk Levels:

Critical: Conditions where there is clear evidence of manipulation or misrepresentation that could lead to significant financial loss or legal repercussions.
High: Situations where the risk of misleading stakeholders about a breach exists, potentially affecting investor trust and market confidence.
Medium: Where patterns suggest reduced transparency but no immediate critical impact, requiring attention for continuous improvement in reporting practices.
Low: Informal findings that do not significantly affect security metrics or stakeholder perception but can be improved for better risk management.
Info: Routine checks that provide general insights into the company’s incident handling and disclosure practices without substantial impact on risk profiles.

Example Findings:

“The company claimed a data breach occurred, but no specific details were provided about the nature or extent of the compromise.”
“There is a sudden and unexplained decrease in reported incidents without any public explanation.”

Risk Acceptance Normalization

Purpose: The Risk Acceptance Normalization Scanner is designed to analyze breach disclosure language and detect patterns that suggest increasing risk acceptance, expanded exception processes, and threshold adjustments. This tool identifies linguistic cues indicating organizations are minimizing the severity of security incidents or adjusting their thresholds without adequate justification.

What It Detects:

Blame Deflection Patterns: These include phrases such as “nation-state actor,” “state-sponsored,” which indicate external attribution, and terms like “highly sophisticated,” “zero-day exploit,” suggesting advanced attacks without substantiation.
Passive Voice Usage: This includes sentences in the passive voice, such as “systems were accessed” or “data was compromised,” which avoid direct accountability.
Minimization Language: Phrases like “limited number of affected users,” “no evidence of impact,” and terms like “out of an abundance of caution,” indicate downplaying the severity of incidents.
Exception Process Expansion: The scanner identifies increased use of phrases such as “exception process” without detailed rationale, suggesting broader acceptance of security issues.
Threshold Adjustments: Indicators include phrases like “adjusted our risk thresholds” or “modified our detection criteria.”

Inputs Required:

domain (string): The primary domain to analyze, e.g., acme.com. This helps in identifying relevant breach disclosure statements on the company’s website.
company_name (string): The name of the company for which the scanner searches and analyzes breach disclosure statements, helping in refining search queries.

Business Impact: This scanner is crucial as it helps organizations self-assess their preparedness and response to security incidents by identifying potential linguistic indicators of risk acceptance and inadequate handling mechanisms. Properly addressing these detected issues can significantly enhance an organization’s resilience against cyber threats.

Risk Levels:

Critical: Conditions that could lead to immediate, severe consequences such as significant data loss or system compromise.
High: Conditions where the risk is substantial but not immediately critical, requiring urgent attention and mitigation strategies.
Medium: Conditions with moderate risk that may require some intervention but do not pose an immediate threat.
Low: Informal or minimal risks that generally do not impact security posture significantly unless compounded with other factors.
Info: Non-critical findings that provide informational insights but do not directly affect the core security posture.

Example Findings:

“The company’s breach disclosure mentions ‘highly sophisticated attacks,’ which might indicate a risk of underestimating the complexity and potential impact of recent incidents.”
“A sentence in the breach report uses passive voice (‘systems were accessed’) rather than active voice, suggesting a possible attempt to downplay the direct responsibility for the security incident.”

This structured output format helps stakeholders understand not only what the scanner detects but also how these detections relate to the broader risk landscape and potential consequences.