AI Traceability Obfuscation

5 automated security scanners

Training Data Provenance Obscuring

Purpose: The Training Data Provenance Obscuring Scanner is designed to uncover hidden data sources and methods used in AI training datasets. It aims to detect dataset source concealment, data origin misrepresentation, collection method obscuring, lack of documentation, and falsification of compliance certifications, ensuring transparency and trust in AI systems.

What It Detects:

Dataset Source Concealment: Identifies vague or missing references to the sources of datasets, including generic statements about data acquisition without specific details.
Data Origin Misrepresentation: Looks for discrepancies between stated data origins and actual sources, flagging claims of proprietary data when open-source alternatives are more likely.
Collection Method Obscuring: Identifies vague or overly technical descriptions of data collection methods, as well as a lack of transparency in how sensitive information was gathered.
Lack of Documentation: Checks for the absence of detailed documentation on dataset provenance and incomplete or non-existent security and privacy policies related to data handling.
Compliance Certifications Falsification: Searches for claims of compliance with standards (e.g., SOC 2, ISO 27001) without evidence, detecting references to certifications that are not supported by public documentation.

Inputs Required:

domain (string): The primary domain to analyze, providing the website address where data policies and statements can be found (e.g., “acme.com”).
company_name (string): The company name for which to search within its site for any relevant data policy disclosures (e.g., “Acme Corporation”).

Business Impact: Ensuring transparency in AI training datasets is crucial for maintaining trust and compliance with regulatory standards, thereby enhancing the security posture of AI systems and mitigating risks associated with unverified or concealed data origins and methods.

Risk Levels:

Critical: Findings that directly compromise the integrity or security of AI models by concealing dataset sources or methods without alternative verification.
High: Misrepresentations in data origin claims, potentially leading to misinterpretation of model training data, which could affect decision-making based on these datasets.
Medium: Unclear or overly technical descriptions of data collection and handling processes that may lead to operational inefficiencies or increased audit complexity.
Low: Absence of detailed documentation without clear implications for AI operations, where alternative verification methods can confirm dataset origins and practices.
Info: Informal references to compliance certifications not backed by public evidence, which might require further investigation but do not immediately impact model reliability or security.

Example Findings:

The company claims that all data is sourced from proprietary databases, yet lacks specific details on the composition of these datasets in their documentation.
A dataset description uses vague terms like “sophisticated algorithms” for collection without detailing the exact methods used, which could indicate potential obfuscation.

Model Origin Concealment

Purpose: The Model Origin Concealment Scanner is designed to detect and expose patterns of source model obscuring, provider relationship hiding, and development lineage concealment in AI models. This ensures transparency and accountability in the origins and development processes of these models, crucial for maintaining trust and accountability in AI deployments.

What It Detects:

Source Model Obscuring Patterns:
- Vague descriptions of model sources are flagged as they lack specificity.
- Generic references to “proprietary algorithms” without detailed explanations are detected.
- The absence of detailed model architecture documentation raises concerns about transparency.
- Claims of original development without supporting evidence may be misleading.
- Use of terms like “custom-built” without further explanation is scrutinized.
Provider Relationship Hiding:
- Lack of mentions of external providers or partners involved in model development indicates potential concealment.
- Omission of third-party contributions to the model suggests hiding relevant information.
- The absence of partnerships or collaborations with AI vendors raises questions about openness.
- Claims of internal development only, despite clear evidence of external involvement are flagged.
- Use of terms like “in-house” without supporting details may be deceptive.
Development Lineage Concealment:
- Vague descriptions of the development process can lead to uncertainty about model evolution and improvements.
- Omission of key milestones in model development might obscure critical information.
- The absence of detailed documentation on iterative improvements and updates indicates a lack of transparency.
- Claims of continuous improvement without specific examples or timelines are scrutinized for credibility.
- Use of terms like “ongoing enhancement” without further explanation is considered suspicious.
Policy Documentation Absence:
- Lack of security policies related to AI model development and deployment may leave the organization vulnerable.
- Omission of incident response plans specifically addressing AI models can lead to inadequate handling of potential incidents.
- The absence of data protection measures relevant to AI models raises concerns about compliance with legal and regulatory requirements.
- Claims of compliance with standards (e.g., SOC 2, ISO 27001) without supporting documentation may be unfounded.
- Use of terms like “compliant” without evidence is considered deceptive.
Compliance Certifications Omission:
- Lack of mentions of relevant compliance certifications (e.g., SOC 2, ISO 27001) suggests a lack of rigorous testing and verification.
- The omission of penetration test results or vulnerability assessments related to AI models indicates potential concealment of security weaknesses.
- Absence of third-party audits or reviews of AI model security practices raises questions about the reliability of the claims made.
- Claims of rigorous testing and auditing without specific references may be misleading.
- Use of terms like “audited” without supporting documentation is considered deceptive.

Inputs Required:

domain (string): Primary domain to analyze, e.g., acme.com. This helps in identifying the relevant policies and compliance statements on the company’s website.
company_name (string): Company name for statement searching, e.g., “Acme Corporation”. This aids in focusing the search on documents that pertain to the specific organization.

Business Impact: Ensuring transparency in AI model origins and development processes is crucial for maintaining trust and accountability in AI deployments. By detecting concealment practices, organizations can improve their security posture and compliance with relevant regulations and standards.

Risk Levels:

Critical: Conditions that could lead to significant risks such as legal penalties, reputational damage, or severe data breaches.
High: Conditions that pose a high risk of negative impacts on operations or strategic objectives due to concealment practices in AI model development.
Medium: Conditions where the risk is moderate but still requires attention and improvement in transparency and accountability practices for AI models.
Low: Conditions with minimal impact, typically informational findings indicating areas for potential enhancement in documentation and disclosure practices.
Info: Informational findings that do not directly affect security or compliance posture but suggest opportunities for improving transparency and documentation standards.

Example Findings:

A company claims to have developed an AI model internally without providing detailed development documentation, which could indicate a risk of concealment in the origin of their models.
Inadequate mention of external partnerships or contributions in model development processes might suggest hiding provider relationships and potential conflicts of interest.

Output Fingerprint Alteration

Purpose: The Output Fingerprint Alteration Scanner is designed to detect modifications in response patterns, changes in generation signatures, and removal of attribution evidence within organizational communications. This ensures transparency and integrity by identifying alterations in standard response formats, discrepancies in technical signatures, and attempts to obscure accountability through selective information disclosure.

What It Detects:

Response Pattern Modification: Identifies alterations in standard response formats or templates across different disclosures.
Generation Signature Changing: Monitors changes in the technical signatures of generated content, such as timestamps, hashes, or metadata, which may indicate unauthorized modifications.
Attribution Evidence Removal: Searches for removal or obfuscation of evidence pointing to internal or external sources of a breach, aiming to identify attempts to obscure accountability.
Policy Indicators Discrepancy: Checks for the presence and consistency of key security policy indicators in public documentation, highlighting missing or inconsistent references to critical policies like data protection, incident response, and access control.
Maturity Indicator Verification: Evaluates compliance certifications and maturity indicators such as SOC 2, ISO 27001, penetration testing, and vulnerability assessments, detecting discrepancies between stated capabilities and actual evidence of compliance.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com) - This is necessary for searching company sites to collect breach disclosure statements and policy documents related to security incidents, data breaches, and other relevant areas.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”) - Used in the search query to identify specific company information online.

Business Impact: This scanner is crucial as it helps maintain transparency and integrity within organizational communications by detecting unauthorized modifications that could lead to security breaches or misinformation. It ensures compliance with critical security policies, which is essential for maintaining trust and legal standing.

Risk Levels:

Critical: Failures in detection of significant alterations in response patterns, generation signatures, or removal of attribution evidence can lead to severe consequences such as undetected data breaches or regulatory non-compliance.
High: Moderate risks involve inconsistencies in policy indicators that could indicate inadequate security measures or lack of adherence to critical policies.
Medium: Lower risks are associated with minor discrepancies in maturity indicators, which might suggest room for improvement but do not pose immediate threats.
Low: Informational findings pertain to the presence and consistency of policy indicators, indicating a generally well-managed security posture without significant concerns.

Example Findings:

“Detected alteration in response format on /security-incident” - Indicates potential unauthorized changes in how incidents are disclosed.
“Evidence of external attribution removed from /newsroom” - Suggests attempts to conceal the source or origin of a breach, which could be indicative of malicious intent.

Model Capability Partitioning

Purpose: The Model Capability Partitioning Scanner is designed to analyze and interpret a company’s security documentation and public policies to assess how it divides its functionality, distributes capabilities, and separates integrated functions. This analysis helps in understanding the organization’s structure and identifying potential vulnerabilities resulting from improper partitioning of these elements.

What It Detects:

Functionality Division Indicators: The scanner identifies sections within security documentation that describe different departments or teams responsible for specific security functions such as handling by a “security team,” “IT department,” or “operations team.”
Capability Distribution Patterns: This includes detecting statements indicating the distribution of security capabilities across various entities within the organization, often indicated through phrases like “responsible for,” “handles,” and “manages.”
Integrated Function Separation Indicators: The scanner looks for instances where integrated functions are separated or isolated to prevent single points of failure, focusing on terms such as “separation of duties,” “isolated environments,” and “segmentation.”
Policy and Procedure Documentation: It analyzes security policies and procedures to ensure they cover all necessary aspects of functionality division and capability distribution, with keywords like “security policy,” “incident response,” “data protection,” and “access control” being crucial.
Compliance Certification Indicators: The scanner detects mentions of compliance certifications that indicate adherence to standards related to functionality partitioning, including terms such as “SOC 2,” “ISO 27001,” “penetration test,” and “vulnerability scan/assessment.”

Inputs Required:

domain (string): This is the primary domain of the company website that needs to be analyzed for security documentation.
company_name (string): The name of the company, used for searching specific terms within the provided domain.

Business Impact: Proper partitioning of functionality and capability distribution is crucial for maintaining a robust security posture, reducing the risk of unauthorized access and potential data breaches. Inefficient or improperly documented division can lead to significant vulnerabilities that may be exploited by malicious actors.

Risk Levels:

Critical: Findings that directly indicate missing or inadequately documented critical security policies or procedures.
High: Issues related to incomplete or misaligned separation of duties, where integrated functions are not clearly separated from each other.
Medium: Inconsistencies in the distribution of capabilities across different departments or teams without clear documentation.
Low: Informal mentions that do not significantly impact security but can be improved for better clarity and compliance with standards.
Info: General references to ongoing efforts or existing practices that are compliant but lack detailed documentation, indicating potential areas for improvement in the reporting structure.

Example Findings:

The company lacks a comprehensive “security policy” document that covers all aspects of security management within the organization.
There is no mention of specific teams responsible for handling data protection and incident response, which could lead to unclear roles during critical incidents.

Model Architecture Masking

Purpose: The Model Architecture Masking Scanner is designed to uncover hidden technical implementations, architectural obscurities, and design pattern complexities within company documentation. It aims to identify organizations that deliberately conceal their technology stack or security practices by analyzing key documents such as security policies, compliance certifications, and trust center information.

What It Detects:

Security Policy Indicators: Identifies the presence of prominent security policies like “security policy,” “incident response,” “data protection,” and “access control.”
Maturity Indicators: Looks for compliance with certifications such as SOC 2, ISO 27001, penetration testing results, and vulnerability scans.
Technical Implementation Concealment: Detects vague or generic language that avoids specific technical details about the architecture or security measures.
Design Pattern Hiding: Identifies obfuscated or hidden design patterns through terms suggesting complexity without clear explanation (e.g., “proprietary technology,” “advanced encryption”).
Architectural Approach Obscuring: Detects attempts to obscure the architectural approach through ambiguous language or lack of detailed documentation.

Inputs Required:

domain (string): The primary domain name of the organization for analysis, such as “acme.com.”
company_name (string): The legal name of the company, used for searching relevant statements and documents, e.g., “Acme Corporation.”

Business Impact: This scanner is crucial for assessing an organization’s transparency regarding its technical security practices. Understanding these aspects can significantly impact how stakeholders perceive and interact with the organization in terms of trust, compliance, and risk management.

Risk Levels:

Critical: Severe obfuscation that directly impacts critical security policies or exposes significant vulnerabilities without remediation.
High: Substantial concealment that could lead to inadequate protection against threats, requiring immediate attention for clarification and improvement.
Medium: Moderate levels of ambiguity in documentation that may require further investigation but do not pose immediate risks.
Low: Minimal to no impact on security posture, indicating a lower risk profile unless the organization’s operations significantly expand or change.
Info: Informal language or lack of specific detail that does not affect current security practices but could be indicative of future issues without proactive monitoring and documentation improvement.

Note: Risk levels are inferred based on the severity of concealment detected in company documentation.

Example Findings:

A company’s privacy policy contains vague language about data protection, suggesting potential concealment of specific technical measures to safeguard customer information.
An organization claims compliance with ISO 27001 but lacks detailed evidence or documented practices that align with the standard’s requirements.