Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

All comments will be made public as-is, with no edits or redactions. Please be careful to not include confidential business or personal information, otherwise sensitive or 

protected information, or any information you do not wish to be posted. 

Comment Template for Submit comments by August 19, 2 

Responses to NIST Artifical 

Intelligence Risk 

Management Framework 

General RFI Topics (Use as many lines as you like) Response # Responding 

organization 

Responder's 

name 

Paper 

Section (if 

applicable) 

Response/Comment (Include 

rationale) 

Suggested change 

Responses to Specific Request for information (pages 11,12, 13 

and 14 of the RFI) 

1. The greatest challenges in improving how AI actors manage AIrelated risks – where “manage” means identify, assess, prioritize, 

respond to, or communicate those risks; 

In order to understand the AI-related risks that impact AI deployment, we 

need to adequately model the risks. With respect to security, privacy and 

fairness, we especially need to understand and model the realistic 

threats. So that different options ranging from how models are built to 

deployment scenarios could be considered. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

Please see 

the attached 

paper. 

2. How organizations currently define and manage characteristics of 

AI trustworthiness and whether there are important characteristics 

which should be considered in the Framework besides: accuracy, 

explainability and interpretability, reliability, privacy, robustness, 

safety, security (resilience), and mitigation of harmful bias, or harmful 

outcomes from misuse of the AI; 

To my knowledge, these are main criteria considered in current practice. 

Still, I believe robustness to attacks such as poisoning or test time attacks 

need to be one of the important principles to be considered. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

Type: E - Editorial, G - General T - Technical 1 of 3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

3. How organizations currently define and manage principles of AI 

trustworthiness and whether there are important principles which 

should be considered in the Framework besides: transparency, 

fairness, and accountability; 

4. The extent to which AI risks are incorporated into different 

organizations' overarching enterprise risk management – including, 

but not limited to, the management of risks related to cybersecurity, 

privacy, and safety; 

Clearly, there is important synergy between cybersecurity, privacy and AI 

risks. For example, a cyber attack may be used to poison an AI model 

training data to insert backdoors into the AI model. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

5. Standards, frameworks, models, methodologies, tools, guidelines 

and best practices, and principles to identify, assess, prioritize, 

mitigate, or communicate AI risk and whether any currently meet 

the minimum attributes described above; 

I believe we need a new risk management framework that is tailored to 

different aspects of AI deployment ranging from the data collection to 

model building. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

6. How current regulatory or regulatory reporting requirements 

(e.g., local, state, national, international) relate to the use of AI 

standards, frameworks, models, methodologies, tools, guidelines and 

best practices, and principles; 

7. AI risk management standards, frameworks, models, 

methodologies, tools, guidelines and best practices, principles, and 

practices which NIST should consider to ensure that the AI RMF 

aligns with and supports other efforts; Please the attached summary of such a proposal. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

8. How organizations take into account benefits and issues related to 

inclusiveness in AI design, development, use and evaluation – and 

how AI design and development may be carried out in a way that 

reduces or manages the risk of potential negative impact on 

individuals, groups, and society. 

I believe that every aspect of the AI pipeline need to be revisited for 

understanding these risks. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

Type: E - Editorial, G - General T - Technical 2 of 3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

9. The appropriateness of the attributes NIST has developed for the 

AI Risk Management Framework. (See above, “AI RMF Development 

and Attributes”); 

I think it is a great start. It may need to be tweaked as different needs 

are discovered. 

University of Texas 

at Dallas 

Murat 

Kantarcioglu 

10. Effective ways to structure the Framework to achieve the desired 

goals, including, but not limited to, integrating AI risk management 

processes with organizational processes for developing products and 

services for better outcomes in terms of trustworthiness and 

management of AI risks. Respondents are asked to identify any 

current models which would be effective. These could include – but 

are not limited to – the NIST Cybersecurity Framework or Privacy 

Framework, which focus on outcomes, functions, categories and 

subcategories and also offer options for developing profiles reflecting 

current and desired approaches as well as tiers to describe degree of 

framework implementation; and 

11. How the Framework could be developed to advance the 

recruitment, hiring, development, and retention of a knowledgeable 

and skilled workforce necessary to perform AI-related functions 

within organizations. 

12. The extent to which the Framework should include governance 

issues, including but not limited to make up of design and 

development teams, monitoring and evaluation, and grievance and 

redress. 

Type: E - Editorial, G - General T - Technical 3 of 3