"AI-Adversarial Machine Learning" COI
Elevate Your "AI-Security" Skills
Dedicated to Advancing Responsible AI through AI-RMF knowledge sharing!
Site Information:
Welcome to the "AI-Adversarial Machine Learning", Community of Interest", an informational hub and community dedicated to advancing Responsible AI through knowledge sharing for NIST AI-Risk Management Framework (AI-RMF) methodologies. We merge Risk Management, AI Security, AI-Red Team Testing to help develop a holistic approach to AI-Adversarial Machine Learning as described in the NIST AI-100-2e2025. Our goal is to help professionals and organizations enhance their overall AI-Security capabilities.
Our structured approach begins with understanding AI-Risk Management, AI Security fundamentals, and AI Threat information, followed by deep dives into AI-Red Team Testing, adversarial attack strategies, and AI system hardening. Whether you're an AI developer, security researcher, or risk management professional, this site provides valuable insights, tools, and best practices to strengthen AI defenses against emerging threats.
Explore our key sections:
- AI-RMF: NIST based risk management approach.
- AI-Security: Foundational concepts in AI security, risk assessment, and defense strategies.
- AI-Threats: The evolving AI threat landscape, vulnerabilities, and real-world AI incidents.
- AI-Red Team Testing: A systematic approach to evaluating AI systems through simulated adversarial attacks
- Playground: Hands-on use case learning with AI security tools, red teaming techniques, and adversarial testing simulations.
Join us in building a more secure and resilient AI ecosystem by exploring cutting-edge research, industry frameworks, and AI Red Team strategies.
Learn More about NIST AI-Risk Management Framework
The National Institute of Standards and Technology (NIST) is a U.S. federal agency that develops standards, guidelines, and tools to ensure the reliability and security of technology, including artificial intelligence (AI). NIST's mission spans a wide array of fields from cybersecurity to physical sciences and engineering, aiming to promote innovation and industrial competitiveness.
In the realm of artificial intelligence, NIST introduced the AI Risk Management Framework (AI-RMF) to guide organizations in managing the risks associated with AI systems. The AI-RMF is designed to be a flexible and voluntary framework that helps stakeholders across various sectors understand, assess, and address the risks AI technologies can pose. This includes considerations for the ethical, technical, and societal implications of AI deployment. The framework emphasizes the importance of trustworthy AI, which means AI systems that are responsible, equitable, traceable, reliable, and governable while also being transparent, explainable, and accountable.
Fortifying Trust through Security of AI:
AI-Security:
AI security involves a series of steps and strategies aimed at protecting AI systems from vulnerabilities, ensuring they operate reliably, and are free from manipulation. Here are the main steps involved in securing AI systems:
- Risk Assessment:
- Data Security:
- Model Security:
- Adversarial AI Defense:
- Ethical and Legal Compliance:
- AI Governance:
- Incident Response:
- Research and Development:
Understanding the AI-Threat Landscape:
Overview
Our AI-Threat Landscape section serves as a critical resource for understanding the ever-evolving threats in the realm of artificial intelligence. As AI technologies integrate more deeply into various sectors, the potential for sophisticated threats grows. This section provides a comprehensive analysis of the current and emerging threats specific to AI systems, aiming to equip stakeholders with the knowledge required to identify, assess, and mitigate these risks effectively. Learn about
Identify Key Threats:
- Adversarial Attacks.
- Data Security Breaches.
- Model Theft and Reverse Engineering.
- AI Misuse.
Threat Mitigation Strategies:
- Adversarial Training.
- Robust Encryption Methods.
- Red Team Testing.
- Ethical AI Deployment.
AI Security Through "AI-Red Team Testing"
AI-Red Team Testing (AI-RTT) is a proactive approach to identifying vulnerabilities, harms and risks to better develop and deploy Responsible AI. The goal is to release safe and secure artificial intelligence (AI) systems, by simulating adversarial behaviors and stress-testing models under various conditions. This process ensures that AI systems are robust, secure, and aligned with organizational goals and ethical standards.
Here, we integrate AI-Red Team Testing with the principles and guidelines of the NIST AI-Risk Management Framework (AI-RMF) to deliver a structured and comprehensive Independent Verification and Validation (IV&V) of AI systems.
In this section, we will delve into the specific information, tools and techniques for:
- Setting-up Red Team Operations,
- ML Testing Techniques,
- ML-Model Scanning Tools,
- Manual and Automated Adversarial Tools