+AI & Autonomous Technologies Potential Costs Analysis

AI & Autonomous Technologies Potential Costs Analysis

Description

Mechanisms exist to assess potential costs, including non-monetary costs, resulting from expected or realized Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related errors or system functionality and trustworthiness.

Possible Solutions & Considerations

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

∙ AI cost analysis (licensing, compute, maintenance, training data)
∙ AI governance program

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

∙ Formal AI cost analysis for each system
∙ AI governance program
∙ Total cost of ownership (TCO) assessment

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

∙ Quantified AI cost analysis including operational and hidden costs
∙ AI governance program
∙ NIST AI RMF Map function

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

∙ Structured AI cost analysis integrated with financial planning
∙ AI FinOps practices for cloud-based AI workloads
∙ NIST AI RMF Map function

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

∙ Enterprise AI cost management program
∙ AI FinOps and cloud cost optimization (e.g., AWS Cost Explorer, Azure Cost Management)
∙ Board-level AI investment and cost reporting
∙ NIST AI RMF Map function at scale

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 assess potential costs, including non-monetary costs, resulting from expected or realized AAT-related errors or system functionality and trustworthiness.

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. Übersicht

1.1 Referenzen

1.2 Identifizierte Anforderungen

1.3 Related Regulations

2. Identifizierte Anforderungen

Anforderungen

3. Related Regulations

Regulations
EULAW Article 9 Risk management system

Article 9

Risk management system

1.   A risk management system shall be established, implemented, documented and maintained in relation to high-risk AI systems.

2.   The risk management system shall be understood as a continuous iterative process planned and run throughout the entire lifecycle of a high-risk AI system, requiring regular systematic review and updating. It shall comprise the following steps:

(a)

the identification and analysis of the known and the reasonably foreseeable risks that the high-risk AI system can pose to health, safety or fundamental rights when the high-risk AI system is used in accordance with its intended purpose;

(b)

the estimation and evaluation of the risks that may emerge when the high-risk AI system is used in accordance with its intended purpose, and under conditions of reasonably foreseeable misuse;

(c)

the evaluation of other risks possibly arising, based on the analysis of data gathered from the post-market monitoring system referred to in Article 72;

(d)

the adoption of appropriate and targeted risk management measures designed to address the risks identified pursuant to point (a).

3.   The risks referred to in this Article shall concern only those which may be reasonably mitigated or eliminated through the development or design of the high-risk AI system, or the provision of adequate technical information.

4.   The risk management measures referred to in paragraph 2, point (d), shall give due consideration to the effects and possible interaction resulting from the combined application of the requirements set out in this Section, with a view to minimising risks more effectively while achieving an appropriate balance in implementing the measures to fulfil those requirements.

5.   The risk management measures referred to in paragraph 2, point (d), shall be such that the relevant residual risk associated with each hazard, as well as the overall residual risk of the high-risk AI systems is judged to be acceptable.

In identifying the most appropriate risk management measures, the following shall be ensured:

(a)

elimination or reduction of risks identified and evaluated pursuant to paragraph 2 in as far as technically feasible through adequate design and development of the high-risk AI system;

(b)

where appropriate, implementation of adequate mitigation and control measures addressing risks that cannot be eliminated;

(c)

provision of information required pursuant to Article 13 and, where appropriate, training to deployers.

With a view to eliminating or reducing risks related to the use of the high-risk AI system, due consideration shall be given to the technical knowledge, experience, education, the training to be expected by the deployer, and the presumable context in which the system is intended to be used.

6.   High-risk AI systems shall be tested for the purpose of identifying the most appropriate and targeted risk management measures. Testing shall ensure that high-risk AI systems perform consistently for their intended purpose and that they are in compliance with the requirements set out in this Section.

7.   Testing procedures may include testing in real-world conditions in accordance with Article 60.

8.   The testing of high-risk AI systems shall be performed, as appropriate, at any time throughout the development process, and, in any event, prior to their being placed on the market or put into service. Testing shall be carried out against prior defined metrics and probabilistic thresholds that are appropriate to the intended purpose of the high-risk AI system.

9.   When implementing the risk management system as provided for in paragraphs 1 to 7, providers shall give consideration to whether in view of its intended purpose the high-risk AI system is likely to have an adverse impact on persons under the age of 18 and, as appropriate, other vulnerable groups.

10.   For providers of high-risk AI systems that are subject to requirements regarding internal risk management processes under other relevant provisions of Union law, the aspects provided in paragraphs 1 to 9 may be part of, or combined with, the risk management procedures established pursuant to that law.

Linked Issues

Issuelinks
is related to Semi-Annual
is related to relative Control Weighting = 02
is related to Process
is related to Identify
is related to SCRM Focus Tier 1 STRATEGIC
is related to SCRM Focus Tier 2 OPERATIONAL
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 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
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