AI Compliance Evasion

5 automated security scanners

Documentation Misrepresentation

Purpose: The Documentation Misrepresentation Scanner is designed to analyze breach disclosure language and other forms of documentation to detect manipulation of process descriptions, divergence between stated controls and actual implementation, and exaggeration of control effectiveness. This tool helps identify organizational dishonesty and prevents learning from security incidents by detecting blame deflection patterns, third-party responsibility shifting, employee scapegoating, passive voice usage, and emphasis on technology failures.

What It Detects:

Blame Deflection Patterns:
- Detects phrases like “nation-state actor,” “state-sponsored,” or “highly sophisticated” without evidence.
- Identifies mentions of specific APT groups (e.g., Fancy Bear, Lazarus) without technical justification.
- Flags vague descriptors such as “sophisticated” or “advanced” without detailing the actual attack vectors.
Third-Party Blame Patterns:
- Detects language that shifts blame to third-party vendors or partners.
- Identifies mentions of supply chain attacks without addressing internal controls.
- Flags attributions to managed service providers without acknowledging internal security gaps.
Employee Scapegoating:
- Detects language that blames “rogue employees” or “insiders” without discussing systemic control failures.
- Identifies announcements focusing on individual terminations rather than broader security issues.
Passive Voice and Vagueness:
- Tests for the use of passive voice constructions like “was accessed,” “were compromised,” or “has been determined.”
- Checks for descriptions that omit the agent responsible for the breach.
- Flags statements with vague causality, such as “out of an abundance of caution.”
Technology Failure Emphasis:
- Detects excessive emphasis on specific products or vendors without addressing configuration issues.
- Identifies mentions of zero-day exploits without providing CVE details.
- Flags exclusive focus on software flaws over policy gaps.

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 detecting potential misrepresentations and exaggerations in security documentation, which can lead to underestimation of risks or overconfidence in existing controls. It aids in maintaining a realistic view of the organization’s security posture and improving decision-making based on accurate information.

Risk Levels:

Critical: Findings that directly impact critical systems or involve significant data exposure without adequate mitigation.
High: Findings that indicate potential unauthorized access, substantial data loss risks, or significant compliance violations.
Medium: Findings that suggest vulnerabilities in less critical areas but still pose a risk to overall security.
Low: Informal findings that may require attention but do not significantly impact the organization’s security posture.
Info: Non-critical issues that provide general information or suggestions for improvement without immediate risks.

Example Findings:

A breach disclosure statement uses passive voice constructions extensively, making it difficult to determine the exact sequence of events and potential responsibility.
The company claims a highly sophisticated APT group was involved in an attack, but lacks specific technical evidence to support this claim.

Compliance Control Simulation

Purpose: The Compliance_Control_Simulation Scanner is designed to analyze breach disclosure language and organizational configurations to detect temporary measures, audit-specific settings, and demonstration environment indicators that may indicate compliance evasion or misrepresentation of security posture. This tool helps organizations identify potential issues related to short-term solutions, audit-related vulnerabilities, and simulated testing environments, ensuring a more robust and accurate representation of their security stance.

What It Detects:

Temporary Measure Implementation: Detection of phrases indicating interim solutions rather than permanent fixes, highlighting time-bound remediation efforts that lack long-term strategies.
Audit-Specific Configurations: Recognition of configurations activated only during audit periods or compliance checks, which revert to less secure states outside these times.
Demonstration Environment Indicators: Detection of language suggesting the use of demo environments for security testing or incident response, indicating sandboxed or controlled setups used for simulations rather than real-world security practices.
Blame Deflection Patterns: Analysis of linguistic patterns that deflect blame onto external actors, technology vendors, or employees, obscuring internal failures and shortcomings in security management.
Passive Voice and Vagueness: Detection of passive voice constructions avoiding direct accountability for security incidents, leading to unclear attributions of responsibility.

Inputs Required:

domain (string): Primary domain to analyze, such as acme.com, providing the scope of the website for breach disclosure statement analysis.
company_name (string): Company name for statement searching, e.g., “Acme Corporation,” used in context-specific searches and linguistic pattern matching within company communications.

Business Impact: This scanner is crucial as it helps organizations self-assess their security posture by identifying potential evasions of compliance requirements and misrepresentations of security measures. Correcting these issues can lead to improved long-term security strategies, enhanced audit outcomes, and more accurate risk assessments across various regulatory environments.

Risk Levels:

Critical: Findings include clear evidence of temporary or evasive measures that significantly impact the organization’s compliance status without a clear remediation plan.
High: Detected configurations are specifically designed for compliance audits but revert to less secure states post-audit, indicating potential misrepresentation and inadequate permanent solutions.
Medium: Indicators suggest use of demo environments or other temporary setups for security simulations rather than real operational practices, posing moderate risk depending on the criticality of affected systems.
Low: Passive voice constructions in breach disclosures may indicate a need for clearer communication strategies but does not significantly impact overall compliance status.
Info: Informational findings such as generic language around system access or breaches without specific indicators of evasion or misrepresentation, generally considered less impactful on security posture.

Example Findings:

“During our recent audit, we enabled additional logging for compliance checks, but this setting reverts to default after the audit period ends.”
“We used a demo environment to simulate an attack scenario and assess response capabilities, indicating a reliance on demonstration setups rather than production environments.”

Audit Scope Limitation

Purpose: The Audit Scope Limitation Scanner is designed to identify and highlight critical assessment boundaries, evaluation exclusions, and review limitations within company disclosures, press releases, and security incident pages. This tool aims to ensure transparency and completeness of reported security assessments by pinpointing areas not covered in the audit, types of vulnerabilities omitted, and any caveats regarding the thoroughness of the audit findings.

What It Detects:

Assessment Boundary Constraints: The scanner identifies statements that explicitly exclude specific areas or systems from the assessment scope, as well as indications of certain vulnerabilities or threats not being evaluated.
Evaluation Exclusions: It locates phrases suggesting particular types of evaluations have been omitted, such as penetration testing or code reviews, and detects omissions in security controls or configurations during the assessment process.
Review Limitations: The scanner recognizes limitations in the review process, including time constraints, resource limitations, or scope restrictions, and highlights any caveats regarding the completeness and thoroughness of the audit findings.
Blame Deflection Patterns: It detects claims attributing breaches to external actors without sufficient evidence and identifies vague descriptors like “sophisticated” or “advanced” that lack technical justification.
Passive Voice and Vagueness: The scanner tests for passive construction frequency in breach descriptions, checks for agent omission in breach descriptions indicating a lack of clear accountability.

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 aiming to maintain a robust security posture by ensuring that all aspects of their operations, including critical systems and potential vulnerabilities, are thoroughly assessed and reported on. It helps in maintaining transparency with stakeholders and enhancing trust through clear communication of audit limitations and findings.

Risk Levels:

Critical: Conditions where the scanner identifies exclusions from assessment without adequate justification or when statements clearly shift blame to external actors without evidence.
High: When significant vulnerabilities are omitted from evaluation, especially those deemed critical for security posture, due to time constraints or other limitations.
Medium: Findings that indicate incomplete coverage of potential threats or areas not covered in the assessment but do not meet the criteria for high severity.
Low: Informational findings that provide context on what is and isn’t included in the audit scope, useful for understanding the boundaries of the current review.
Info: General disclosures about limitations and exclusions which are informative but not directly critical or high risk.

Example Findings:

“The security assessment did not cover the international subsidiaries due to jurisdictional complexities.”
“No evaluation was conducted on mobile application vulnerabilities, as they were deemed out of scope for this audit.”
“The breach was initially attributed to a nation-state actor without concrete evidence, raising concerns about blame deflection.”
“Vulnerabilities related to physical security measures were not assessed due to the remote work policy during the incident.”

This structured approach ensures that stakeholders are informed about the limitations and constraints of the audit, allowing for more informed decision-making and strategic planning regarding cybersecurity investments and improvements.

Data Handling Mischaracterization

Purpose: The Data Handling Mischaracterization Scanner is designed to analyze breach disclosure language in order to detect and expose misrepresentations of how data was handled, its usage purpose, and retention practices. This tool helps organizations identify potential security failures or compliance issues by detecting patterns such as nation-state actor claims, passive voice usage, minimization language, misleading descriptions about data usage, and vague retention statements.

What It Detects:

Blame Deflection Patterns: Detection of phrases like “nation-state actor” without supporting evidence, sophisticated claims without specific details, and mentions of zero-day exploits lacking CVE references.
Passive Voice Usage: Recognition of sentences constructed in a passive voice which can obscure actual actions taken by the organization.
Minimization Language: Identification of statements that minimize the impact of data breaches or misrepresentations about how long data is retained, often used to conceal security issues.
Usage Purpose Misrepresentation: Detection of claims about data usage that do not align with the actual practices employed by the organization.
Retention Practice Obscuring: Recognition of vague statements about retention periods which can be interpreted as a lack of transparency regarding data handling durations.

Inputs Required:

domain (string): The primary domain under investigation, such as acme.com, to search for breach disclosure statements.
company_name (string): The specific company name used in the search query to locate relevant breach disclosure statements.

Business Impact: This scanner is crucial for organizations aiming to maintain transparency and integrity in their data handling practices. Misrepresentations about data handling can lead to a lack of trust, potential compliance issues, and increased risk of security breaches. Identifying such mischaracterizations early on helps in improving organizational resilience and adherence to regulatory standards.

Risk Levels:

Critical: Findings that directly implicate nation-state actors without sufficient evidence or describe highly sophisticated attacks with no CVE details can be critical.
High: Passive voice usage in breach statements, especially when it obscures actual actions taken by the organization, is considered high risk.
Medium: Vague retention statements and misleading descriptions about data usage are potentially medium-risk findings that require further investigation to ensure compliance with data handling policies.
Low: Minimal impact statements and cautious language used out of an abundance of caution can be low-risk if they do not significantly affect the organization’s security posture or regulatory compliance.
Info: Informational findings such as vague terms like “highly sophisticated” without specific details are considered purely informational unless they escalate to higher risk levels due to lack of evidence or context.

Example Findings:

The scanner might flag a statement indicating that data was accessed during an incident, even if the exact method is not specified, which could be indicative of passive voice usage and potentially conceal actions taken by the organization.
A claim about limited impact on records might be detected as it could represent minimization language aimed at downplaying the severity of the breach, suggesting a need for transparency in data handling practices.

Testing Result Manipulation

Purpose: The Testing Result Manipulation Scanner is designed to analyze breach disclosure language and detect patterns that indicate alterations in evaluation outcomes, biased selection of performance metrics, and manipulation of test scenarios. This ensures organizations maintain transparency about their security posture and avoid misrepresenting the results of security evaluations or tests.

What It Detects:

Blame Deflection Patterns: Detection includes identifying claims of nation-state actor involvement without evidence, state-sponsored attacks, highly sophisticated attacks, unprecedented breaches, and zero-day exploits with insufficient details.
Passive Voice Usage: The scanner identifies the use of passive voice in statements about system access, data compromise, information acquisition, and determinations made during security incidents.
Minimization of Impact: It detects attempts to downplay the number of affected parties, lack of significant issue evidence, abundance of caution statements, and potential impact on users.
Third-Party Blame Patterns: This includes shifting responsibility to vendors/partners, framing attacks as coming from the supply chain, blaming managed service providers, scapegoating contractors/consultants, and attributing outsourcing as the primary cause.
Employee Scapegoating: The scanner flags the use of rogue employees or insiders for incident framing, emphasizes individual termination announcements, acknowledges systemic control failures through HR actions, and frames incidents as isolated occurrences.

Inputs Required:

domain (string): Primary domain to analyze, which helps in searching for breach disclosure statements on the company’s website.
company_name (string): Company name is used for specific statement searching to ensure relevant findings are captured.

Business Impact: This scanner plays a crucial role in maintaining the integrity of security evaluation reports and ensuring that organizations do not misrepresent their security status, which can lead to misinformed decision-making and ineffective risk mitigation strategies.

Risk Levels:

Critical: Conditions where there is clear evidence of manipulation or evasion tactics directly impacting critical security aspects such as data confidentiality, integrity, and availability are considered critical.
High: Findings that significantly affect the organization’s ability to manage risks effectively, potentially leading to substantial financial losses or reputational damage, are classified as high risk.
Medium: Conditions where there is a moderate level of manipulation or evasion but still impact security posture and reporting integrity are considered medium risk.
Low: Informational findings that do not significantly affect the organization’s security posture but may indicate potential issues warranting attention are categorized as low risk.
Info: Any conditions that provide minimal to no direct threat to security but offer insights into operational or procedural improvements are classified as informational.

If specific risk levels are not detailed in the README, these inferred categories can guide assessment based on severity and impact.

Example Findings:

“The company’s breach disclosure statement uses passive voice extensively without providing concrete details of the incident.”
“A vendor is blamed for a security issue despite clear evidence that internal processes were not followed correctly.”