AI Security
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Quantum Sensing and AI for Drone and Nano-Drone Detection
Quantum sensing technologies are emerging as powerful tools to detect and track UASs, including small and nano-drones that often evade conventional sensors. These quantum sensors, such as quantum radars, quantum LiDARs, atomic magnetometers, and Rydberg RF detectors, exploit phenomena like…
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Board AI Governance and Oversight
AI is reshaping businesses across industries, and corporate boards are increasingly expected to oversee AI strategy, ethics, and risk management. In fact, the number of S&P 500 companies formally assigning AI oversight to a board committee more than tripled in…
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Future of Leadership and Consulting in the Age of AI: A Decade On
Exactly ten years ago, on July 3, 2015, we published the first version of Future of Leadership in the Age of AI. At that time, most discussions about artificial intelligence revolved around automating blue-collar jobs and routine manual tasks. Our…
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Why AI Cannot Break Modern Encryption
AI cannot break modern encryption. The reasons are fundamental: Mathematical Hardness, Cryptographic Design, Empirical Track Record, Quantum Contrast, Expert Consensus.
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Closing the Gap Between AI Principles and AI Reality
In pursuit of AI systems that are ethical and robust we are seeing the emergence of an, ironically, ethical challenge: firms rushing to position themselves as leaders in Responsible AI without the necessary depth in technical expertise. Though their motivations…
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Post-Quantum Cryptography (PQC) Meets Quantum AI (QAI)
Post-Quantum Cryptography (PQC) and Quantum Artificial Intelligence (QAI) are converging fields at the forefront of cybersecurity. PQC aims to develop cryptographic algorithms that can withstand attacks by quantum computers, while QAI explores the use of quantum computing and AI to…
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Full Stack of AI Concerns: Responsible, Safe, Secure AI
As AI continues to evolve and integrate deeper into societal frameworks, the strategies for its governance, alignment, and security must also advance, ensuring that AI enhances human capabilities without undermining human values. This requires a vigilant, adaptive approach that is…
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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 line at his Highland Park plant in Michigan. The innovation changed the production process forever, dramatically increasing efficiency, reducing the…
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Marin’s Statement on AI Risks
The rapid development of AI brings both extraordinary potential and unprecedented risks. AI systems are increasingly demonstrating emergent behaviors, and in some cases, are even capable of self-improvement. This advancement, while remarkable, raises critical questions about our ability to control…
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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 our search for a perfect meme to critical infrastructure. But as Spider-Man’s Uncle Ben wisely said, “With great power comes…
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Why We Seriously 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 safeguard digital assets, their focus and the challenges they address diverge in significant ways. Traditional cybersecurity is primarily about defending…
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How to Defend Neural Networks from Trojan Attacks
Neural networks learn from data. They are trained on large datasets to recognize patterns or make decisions. A Trojan attack in a neural network typically involves injecting malicious data into this training dataset. This 'poisoned' data is crafted in such…
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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. Instead, different versions, configurations, or subsets of the model are deployed based on specific needs, constraints, or local optimizations. This…
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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, or outputs to mislead or subvert the intended operations of a machine learning model. In mathematical terms, evasion can be…
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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. Its complex nature notwithstanding, the unparalleled privacy and security benefits it offers are compelling enough to warrant its growing ubiquity.…
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