About Adversa.AI 

Comments are provided by Eugene Neelou and Alex Polyakov, experts in cybersecurity, artificial 

intelligence, and risk management. Both are founders of Adversa.AI, a world-class Israeli startup on a 

mission to increase trust in AI systems. 

Recently, Adversa.AI has released the unique report called “The Road to Secure and Trusted AI: The 

Decade of AI Security Challenges” that demonstrates the progress toward AI trustworthiness over the 

past 10 years across academia, government, and industry. 

Besides many insights and infographics, the Adversa Report has introduced 2 essential resources for 

the development of the AI Risk Management Framework: 

1. The Map of Trustworthy AI covers all principles and requirements for trusted AI. 

2. The Lifecycle for Secure AI describes activities for all security stages for AI systems. 

Please find our comments first and the mentioned resources in the end. 

Adversa’s Comments for the NIST AI RMF 

Challenges in Managing AI Risk (RFI Topic 1) 

There are various challenges in how AI-related risks are managed, including technical and organizational 

problems. However, reports show that AI risks are often mismanaged because companies are not 

simply aware of them. 

To learn about such risks so they could be addressed in the first place, companies should have AI risk 

awareness training programs not just for AI engineering but for everyone involved in AI projects. This 

is similar to how cybersecurity is everyone's concern and security awareness programs are essential for 

cyber resilience. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

We propose that NIST could list activities such as risk awareness explicitly as a prerequisite for 

successful AI risk management. Thus, organizations will know what they need to protect from. 

Principles of AI Trustworthiness (RFI Topic 3) 

There are many dimensions of AI trustworthiness. Unfortunately, terms for them often have unclear 

and confusing definitions. Also, the same terms may be used interchangeably or describe both 

characteristics and principles of AI trustworthiness. The absence of system thinking confuses AI 

stakeholders and prevents them from addressing AI risks efficiently. 

Moreover, the dimensions of AI trustworthiness are managed by very different teams with different 

skillsets. To be actionable, the terms must be grouped by types of AI risks and aligned with 

organizational structure so it’s easier to discuss with the right AI stakeholders and identify the right 

teams to manage those AI risks. 

We suggest the following groups of principles for AI trustworthiness: 

1. “Reliable AI” principles describe how AI systems operate in normal conditions. 

Reliable AI principles include characteristics of Robustness, Accountability, Transparency. 

2. “Resilient AI” principles describe how AI systems operate in adversarial conditions. 

Resilient AI principles include characteristics of Security, Privacy, Safety. 

3. “Responsible AI” principles describe how AI systems comply with social norms. 

Responsible AI principles include characteristics of Fairness, Ethics, Sustainability. 

We propose that NIST could provide a clear hierarchy and definitions while ensuring that principles 

cover all characteristics of AI trustworthiness. The terms should be defined the way it’s clear how 

different dimensions of AI trustworthiness relate, overlap, or complement each other. Thus, it would 

be clear which teams are more suitable to manage certain types of AI risks. 
 

 

 

 

 

 

 

 

 

 

 

 

 

AI Governance Issues (RFI Topic 12) 

AI governance is an essential part of the AI risk management framework. It’s not possible to address AI 

risk efficiently without handling organizational, technical, and operational issues. 

We propose that NIST could cover all stages of AI development from the first ideas and prototypes to 

the maintenance of production AI systems. It’s important to emphasize that AI risk management is not 

a one-time project but rather a regular process that requires team training, operational guidelines, 

incident response plans, and appropriate tooling. Thus, organizations will be empowered to overcome 

challenges toward efficient AI risk management. 

Adversa’s Resources for AI Risk Management 

Adversa.AI is a world-class team that develops AI risk management methods and technologies. 

Recently, Adversa.AI has released the unique report called “The Road to Secure and Trusted AI: The 

Decade of AI Security Challenges” that demonstrates the progress toward AI trustworthiness over the 

past 10 years across academia, government, and industry. 

Besides many insights and infographics, the Adversa Report has introduced 2 essential resources for 

the development of the AI Risk Management Framework: 

1. The Map of Trustworthy AI covers all principles and requirements for trusted AI. 

2. The Lifecycle for Secure AI describes activities for all security stages for AI systems. 

Please find these resources below. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Adversa’s Map of Trustworthy AI 

The Map of Trustworthy AI covers principles and requirements for AI systems. 

“Reliable AI” principles describe how AI systems operate in normal conditions. 

• Robustness means the abilities of AI systems to function as intended, behave predictably 

especially in situations of uncertainty, and fail gracefully in case of fatal errors. 

• Accountability explains how to operationalize AI systems in a responsible way by providing 

human oversight and control over AI behaviors and outcomes. 

• Transparency introduces mechanisms for traceable and explainable AI decisions as well as 

policies that respect human rights to know when they interact with AI systems. 

“Resilient AI” principles describe how AI systems operate in adversarial conditions. 

• Safety is required for mission-critical situations and human-computer interactions, where an AI 

system should prevent any harm to living beings or the environment. 

• Security makes AI systems resilient to malicious activities and adversarial attacks such as 

general software security attacks or specific attacks against AI algorithms. 

• Privacy demands AI systems to implement adequate data governance mechanisms, data 

protection, and access controls for collected and inferred data. 

“Responsible AI” principles describe how AI systems comply with social norms. 

• Fairness enables inclusion, diversity, and accessibility by introducing practices for examining 

bias and counting the interests of everyone affected by AI systems. 

• Ethics touches on moral principles of interaction between humans and AI systems, which 

should respect human rights and empower people, not the opposite. 

• Sustainability lets organizations use AI algorithms to make people's lives better by meeting 

today’s needs without compromising the interests of future generations. 
 

 

 

Adversa’s Lifecycle for Secure AI 

The Lifecycle for Secure AI describes the required steps for all security stages for AI systems. 

The four areas represent the AI system’s stages from inception to maturity and looped back for 

continuous improvement. The steps are ordered from basic to complex with later steps relying on 

preceding outcomes. 

This lifecycle corresponds with NIST Cybersecurity Framework and Gartner’s Adaptive Security 

Architecture, popular reference frameworks for cybersecurity lifecycle management.