AI Security
-
Addressing the Full Stack of AI Concerns: Responsible AI, Trustworthy AI, Secure AI and Safe AI Explained
As AI continues to evolve and integrate deeper into societal frameworks, the strategies for its governance, alignment, and security must…
Read More » -
The Dual Risks of AI Autonomous Robots: Uncontrollable AI Meets Cyber-Kinetic Risks
The automotive industry has revolutionized manufacturing twice. The first time was in 1913 when Henry Ford introduced a moving assembly…
Read More » -
Fortifying the Future: Cyber-Kinetic Risks in Kingdom of Saudi Arabia’s (KSA) Technological Zeitgeist
It’s a good time to be in construction, especially if you happen to operate in Saudi Arabia. Even in the…
Read More » -
AI Security 101
Artificial Intelligence (AI) is no longer just a buzzword; it’s an integral part of our daily lives, powering everything from…
Read More » -
Why We Need a Chief AI Security Officer (CAISO)
With AI’s breakneck expansion, the distinctions between ‘cybersecurity’ and ‘AI security’ are becoming increasingly pronounced. While both disciplines aim to…
Read More » -
How to Defend Neural Networks from Neural Trojan Attacks
Neural networks learn from data. They are trained on large datasets to recognize patterns or make decisions. A Trojan attack…
Read More » -
Model Fragmentation and What it Means for Security
Model fragmentation is the phenomenon where a single machine-learning model is not used uniformly across all instances, platforms, or applications.…
Read More » -
Outsmarting AI with Model Evasion
Model Evasion in the context of machine learning for cybersecurity refers to the tactical manipulation of input data, algorithmic processes,…
Read More » -
Securing Machine Learning Workflows through Homomorphic Encryption
Homomorphic Encryption has transitioned from being a mathematical curiosity to a linchpin in fortifying machine learning workflows against data vulnerabilities.…
Read More » -
Understanding Data Poisoning: How It Compromises Machine Learning Models
Data poisoning is a targeted form of attack wherein an adversary deliberately manipulates the training data to compromise the efficacy…
Read More » -
Semantic Adversarial Attacks: When Meaning Gets Twisted
Semantic adversarial attacks represent a specialized form of adversarial manipulation where the attacker focuses not on random or arbitrary alterations…
Read More » -
The AI Alignment Problem
Table of ContentsIntroductionThe role of emergenceWeak EmergenceStrong EmergenceFuture? Introduction The AI alignment problem sits at the core of all future…
Read More » -
Understanding and Addressing Biases in Machine Learning
While ML offers extensive benefits, it also presents significant challenges, among them, one of the most prominent ones is biases…
Read More » -
Adversarial Attacks: The Hidden Risk in AI Security
Adversarial attacks specifically target the vulnerabilities in AI and ML systems. At a high level, these attacks involve inputting carefully…
Read More » -
Gradient-Based Attacks: A Dive into Optimization Exploits
Gradient-based attacks refer to a suite of methods employed by adversaries to exploit the vulnerabilities inherent in ML models, focusing…
Read More »