+Data Source Lineage & Origin Disclosure

Data Source Lineage & Origin Disclosure

Description

Mechanisms exist to ensure Artificial Intelligence and Autonomous Technologies (AAT) publicly disclose information with sufficient detail to assess:
(1) Content lineage; and
(2) The origin of data used by the AAT.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Disclose data sources used by AI tools in user documentation

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Policy requiring disclosure of AI training data sources

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Formal AI transparency policy
∙ Data source disclosure in user documentation

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ AI transparency program
∙ Public disclosure of data lineage
∙ Model cards with data sourcing

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise AI transparency framework
∙ Automated disclosure generation
∙ Regulatory compliance tracking for AI transparency

SCR-CMM

Level 0 Not Performed

Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.

Level 1 Performed Informally

Artificial Intelligence and Autonomous Technology (AAT) domain capabilities are ad hoc and inconsistent. Capability criteria associated with this control may include:
▪ Policies, standards & procedures associated with AAT domain capabilities provide limited coverage due to the depth and breadth of the existing documentation.
▪ AAT-related processes are expected to follow the organization's existing processes (e.g., incident response, asset management, change control, risk assessments, etc.).
▪ No formal Governance, Risk & Compliance (GRC) team exists to provide AAT oversight, where the Chief Information Officer (CIO), or similar function, governs technology decisions what is acceptable for AAT within the organization.

Level 2 Planned Tracked

Artificial Intelligence and Autonomous Technology (AAT) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity.
▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are documented and maintained by process owners.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD).
▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards, procedures & guidelines).
▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function.
▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform).
▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls).
▪ An implemented and operational capability exists to ensure Artificial Intelligence and Autonomous Technologies (AAT) publicly disclose information with sufficient detail to assess:
(1) Content lineage; and
(2) The origin of data used by the AAT.

Level 4 Quantitatively Controlled

Utilize SCR-CMM Level 3 criteria definitions:
▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control.
▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.

Level 5 Continuously Improving

Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions:
▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control.
▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies.
▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.

1. Overview

1.1 References

1.2 Identified Requirements

1.3 Related Regulations

2. Identified Requirements

Requirements

3. Related Regulations

Regulations
EULAW Article 53 Obligations for providers of general-purpose AI models

Article 53

Obligations for providers of general-purpose AI models

1.   Providers of general-purpose AI models shall:

(a)

draw up and keep up-to-date the technical documentation of the model, including its training and testing process and the results of its evaluation, which shall contain, at a minimum, the information set out in Annex XI for the purpose of providing it, upon request, to the AI Office and the national competent authorities;

(b)

draw up, keep up-to-date and make available information and documentation to providers of AI systems who intend to integrate the general-purpose AI model into their AI systems. Without prejudice to the need to observe and protect intellectual property rights and confidential business information or trade secrets in accordance with Union and national law, the information and documentation shall:

(i)

enable providers of AI systems to have a good understanding of the capabilities and limitations of the general-purpose AI model and to comply with their obligations pursuant to this Regulation; and

(ii)

contain, at a minimum, the elements set out in Annex XII;

(c)

put in place a policy to comply with Union law on copyright and related rights, and in particular to identify and comply with, including through state-of-the-art technologies, a reservation of rights expressed pursuant to Article 4(3) of Directive (EU) 2019/790;

(d)

draw up and make publicly available a sufficiently detailed summary about the content used for training of the general-purpose AI model, according to a template provided by the AI Office.

2.   The obligations set out in paragraph 1, points (a) and (b), shall not apply to providers of AI models that are released under a free and open-source licence that allows for the access, usage, modification, and distribution of the model, and whose parameters, including the weights, the information on the model architecture, and the information on model usage, are made publicly available. This exception shall not apply to general-purpose AI models with systemic risks.

3.   Providers of general-purpose AI models shall cooperate as necessary with the Commission and the national competent authorities in the exercise of their competences and powers pursuant to this Regulation.

4.   Providers of general-purpose AI models may rely on codes of practice within the meaning of Article 56 to demonstrate compliance with the obligations set out in paragraph 1 of this Article, until a harmonised standard is published. Compliance with European harmonised standards grants providers the presumption of conformity to the extent that those standards cover those obligations. Providers of general-purpose AI models who do not adhere to an approved code of practice or do not comply with a European harmonised standard shall demonstrate alternative adequate means of compliance for assessment by the Commission.

5.   For the purpose of facilitating compliance with Annex XI, in particular points 2 (d) and (e) thereof, the Commission is empowered to adopt delegated acts in accordance with Article 97 to detail measurement and calculation methodologies with a view to allowing for comparable and verifiable documentation.

6.   The Commission is empowered to adopt delegated acts in accordance with Article 97(2) to amend Annexes XI and XII in light of evolving technological developments.

7.   Any information or documentation obtained pursuant to this Article, including trade secrets, shall be treated in accordance with the confidentiality obligations set out in Article 78.

Linked Issues

Issuelinks
is related to Annual
is related to relative Control Weighting = 09
is related to Process
is related to Protect
blocks Inability to maintain individual accountability
blocks Improper assignment of privileged functions
blocks Privilege escalation
blocks Unauthorized access
blocks Lost, damaged or stolen asset(s)
blocks Loss of integrity through unauthorized changes
blocks Business interruption
blocks Data loss / corruption
blocks Reduction in productivity
blocks Information loss / corruption or system compromise due to technical attack
blocks Information loss / corruption or system compromise due to non‐technical attack
blocks Loss of revenue
blocks Cancelled contract
blocks Diminished competitive advantage
blocks Diminished reputation
blocks Fines and judgements
blocks Unmitigated vulnerabilities
blocks System compromise
blocks Inability to support business processes
blocks Incorrect controls scoping
blocks Lack of roles & responsibilities
blocks Inadequate internal practices
blocks Inadequate third-party practices
blocks Lack of oversight of internal controls
blocks Lack of oversight of third-party controls
blocks Illegal content or abusive action
blocks Inability to investigate / prosecute incidents
blocks Improper response to incidents
blocks Ineffective remediation actions
blocks Expense associated with managing a loss event
blocks Inability to maintain situational awareness
blocks Third-party cybersecurity exposure
blocks Third-party physical security exposure
blocks Third-party supply chain relationships, visibility and controls
blocks Third-party compliance / legal exposure
blocks Use of product / service
blocks Reliance on the third-party
  • Secure Controls Framework -

    The Secure Controls Framework® (SCF)

    "The SCF is the Common Controls Framework™ (CCF), the world's most comprehensive cybersecurity and data privacy metaframework - it is also free to use. The entire concept is building secure, compliant and resilient capabilities in the most efficient and cost-effective manner possible.

    The SCF is more than just a unified control catalog, since its included content creates a playbook for Governance, Risk & Compliance (GRC) capabilities. Used globally by organizations of every size, the SCF is a robust and scalable solution for security, compliance and resilience controls. As a comprehensive security framework, the SCF maps 1,400+ controls across 200+ laws, regulations, and industry frameworks so you can implement once and comply everywhere.

    Like it or not, cybersecurity is a protracted war on an asymmetric battlefield, where the threats are everywhere and as defenders we have to make the effort to work together to help improve cybersecurity and data privacy practices, since we all suffer when massive data breaches occur or when cyber attacks have physical impacts. Hackers share information on attack methods with other hackers, so why shouldn’t the good guys share information on how to best protect an organization? We decided to take action and make a difference, since we feel it is too important to wait for someone else to fix the problems that exist.

    The SCF is made up of volunteers, mainly specialists within the cybersecurity profession, who focus on GRC and the cybersecurity side of data privacy. These are auditors, engineers, architects, incident responders, consultants and other specialists who live and breathe these topics on a daily basis. The end product is "expert-derived content" that makes up the SCF." https://securecontrolsframework.com/ 

    Terms & Conditions

    The SCF End User License Agreement (EULA) governs the use of the Secure Controls Framework® (SCF) under the Creative Commons Attribution-No Derivatives 4.0 International Public License.
Impressum German English