AI Security
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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…
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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…
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AI Security 101
Artificial Intelligence (AI) is no longer just a buzzword; it’s an integral part of our daily lives, powering everything from…
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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…
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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…
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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.…
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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,…
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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.…
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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…
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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…
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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…
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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…
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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…
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The Unseen Dangers of GAN Poisoning in AI
GAN Poisoning is a unique form of adversarial attack aimed at manipulating Generative Adversarial Networks (GANs) during their training phase;…
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“Magical” Emergent Behaviours in AI: A Security Perspective
Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities…
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