New Publication: NIST Adversarial Machine Learning Taxonomies: Decoded" (IPLR-IG-016)

From Abhivardhan, our President

🚀 Excited to announce the release of our latest research publication: "NIST Adversarial Machine Learning Taxonomies: Decoded" (IPLR-IG-016)!

As AI transforms cybersecurity at unprecedented speeds, we're also witnessing sophisticated adversarial attacks that challenge traditional defense strategies. Our research team - led by brilliant former ISAIL.IN interns Gargi Singh Mundotia, Yashita Parashar, and Sneha Binu - has decoded National Institute of Standards and Technology (NIST)'s critical AI 100-2 E2025 standards to make them actionable for Indian enterprises.

Thanks to Dr Chiranjiv Roy, PhD MBA for his encouraging foreword. Incidentally, we were finding a quote that we cite for each of our works, and we decided to opt one from Roger Spitz's latest book, "Disrupt with Impact".

Well, you should look at the report to find his quote anyways.

🎯 Key insights covered:

- Sector-specific AI threats in BFSI, telecom & Digital Public Infrastructure

- Adversarial ML attack taxonomies (evasion, poisoning, data manipulation)

- Zero-trust frameworks and encryption strategies for AI systems

- Regulatory compliance challenges in AI-powered fraud detection

The dual-use nature of AI technology means that while we're building smarter defenses, attackers are also evolving. This research bridges the gap between academic standards and practical implementation for Indian organizations navigating AI security.

Proud of this teamwork contribution to making cutting-edge cybersecurity research accessible! 📊💡