Sponsored by AI-RMF® LLC
What is "Security of AI"?
"Security of AI" is the Convergence of AI-Governance, AI-Security and AI-Assurance
Why is "Security of AI" Important?
Security of AI Philosophy
This is the overarching umbrella that integrates ethical, technical, and operational safeguards to ensure AI systems are trustworthy and resilient.
The Three Pillars (Convergence)
- AI Governance: The "Directive" layer. It sets the policies, ethical boundaries, and legal compliance requirements. It defines what must be protected and why.
- AI Security: The "Protective" layer. It focuses on the technical defenses (e.g., adversarial hardening, data poisoning protection, and secure model weights). It defines how to defend the system.
- AI Assurance: The "Veridical" layer. It provides the evidence, auditing, and testing (V&V) to prove that the governance and security measures are working. It defines the proof of safety.
The Operational Engine: AI-RMF
You use the Map, Measure, Manage, and Govern functions to bridge the pillars:
- Translate Governance policies into risk profiles.
- Identify technical Security controls based on those risks.
- Supply the metrics used for AI Assurance auditing.
Supporting Infrastructure
You specialize in the sub-disciplines that feed the AI-RMF:
- Organizational: AI Impact Assessments (AIIA) and Inventory/Classification.
- Technical: Adversarial ML (AML) Taxonomies, Data Provenance, and AI-BOM (Bill of Materials).
- Verification: Red-Teaming, Explainability (XAI) Tools, and Continuous Monitoring.
AI-RMF® LLC
Whether you're using, building, deploying, or acquiring artificial intelligence systems, AI-RMF® using our "Security of AI" Philosophy helps you operationalize AI-Governance.
"Security of AI": YouTube Channel
The goal of the "Security of AI" YouTube Channel is to raise awareness, provide educational opportunities and report of relevant SOAI News Commentary. We aim to help people, at all levels, understand the impact and importance of AI-Governance, AI-Security and AI-Assurance. We reveal life challenges and how AI systems fail in the real world—and what happens next. We produce AI security thrillers showing worst-case scenarios, breaking news analysis on live incidents and vulnerabilities, and deep-dive frameworks covering OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF. From Agentic AI threat modeling to Red Team Test techniques, we show defenders how to spot risks before they become disasters.
"Security of AI" Ecosystem
Bobby has built a comprehensive, multi-platform ecosystem centered on the "Security of AI" philosophy — converging AI Governance, AI Security, and AI Assurance to build trustworthy, resilient, and responsible AI systems.
Core Philosophy & Framework
At the heart of the ecosystem is the AI Risk Management Framework (AI-RMF), which serves as the central operational engine integrating three layers:
- AI Governance (Directive) - Sets policies, ethics, and compliance
- AI Security (Protective) - Technical defenses to safeguard systems from threats
- AI Assurance (Veridical) - Evidence, testing, and auditing to prove safety and compliance
This framework translates policy into action, technical controls, and measurable assurance through the Map, Measure, and Manage functions.
Three-Pillar Content Platform
1. AI-RMF® LLC (www.ai-rmf.com)
The authority and resource center providing:
- AI-RMF Framework & Playbook
- Maturity Model & Assessments
- Tools, Templates & Guidance
- Training & Certification
- Consulting & Advisory Services
2. Security of AI™ Website (www.security-of-ai.com)
Education, research, and thought leadership hub featuring:
- Articles, White Papers & Research
- Standards, Policies & Regulations
- Best Practices & Case Studies
- News, Events & Community
- Jobs Intel Page (weekly curation)
3. YouTube Channel (www.youtube.com/@SecurityofAI)
Video education platform with:
- Framework Walkthroughs
- Expert Interviews
- Tools & Techniques
- Live Q&A and Webinars
AI-Governance using 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). 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.
AI-Security using NIST AI-Risk Management Framework
Our "Security of AI" methodology 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.
1. Risk Assessment:
2. Data Security
3. Model Security:
4. Adversarial AI Defense:
5. Ethical and Legal Compliance:
6. AI Governance:
7. Incident Response:
8. Research and Development:
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.
Dive into the exciting world of AI Red Team Testing (AI-RTT) and Penetration Testing. Try out our Python scripts that demonstrate various test scenarios. Run Python scripts that explore how AI models tick—and where their vulnerabilities may lie. Whether you're scanning for insights or launching friendly adversarial attacks, you’ll use our custom AI-RTT scripts (straight from our GitHub account) to take your first steps into hands-on AI testing experimentation. No coding necessary —we’re here to guide you! All you’ll need is a Google Colab account (or create one, it’s free!), and we’ll help you get set up and running Jupyter Notebook files like a pro. It’s time to learn, experiment, and have some fun with AI security!
Fortifying Trust through Security-of-AI:
AI-Security Best Practices:
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.