Artificial Intelligence (AI) Security

IntroductionTrustworthy vs Responsible AITrustworthy AIAttributes of trustworthy AI1.     Transparent, interpretable and explainable2.     Accountable3.     Reliable, resilient, safe and secure4.     Fair and non-discriminatory5.     Committed to privacy and data governanceTrustworthy AI SummaryResponsible AIThe fundamentals of responsible AI1.     Ethical Purpose2.     Fairness and Non-Discrimination3.     Accountability4.     Privacy and Data Protection5.     Safety and Robustness6.     Human-Centric Design7.     Inclusivity and AccessibilityResponsible AI SummarySecure AI, Safe AI and the wicked problem of AI alignmentSecure AIThe foundations of AI securityConfidentialityIntegrityAvailabilityChallenges in Securing AIScalabilityEvolving Threat LandscapeIntegration with Existing SystemsData Privacy and GovernanceRobustness and ResilienceSkill and Knowledge GapsAdvanced Techniques and Methodologies in AI SecurityAdversarial TrainingHomomorphic EncryptionAnomaly Detection SystemsDifferential PrivacyFederated LearningSecure Multi-Party Computation (SMPC)Safe AIRobustnessMonitoringCapability controlAI AlignmentOuter AlignmentInner AlignmentExamples of misalignmentEmergent goalsHonest AIDelivering on the promises of human-centered AI1.   A layered defensePhysical SecurityOperational...

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 time it took to build a car, and significantly lowering the cost of the Model T, thereby kickstarting the world’s love affair with cars. The success of this system not only transformed the automotive industry but also had a profound impact on manufacturing worldwide, launching the age of mass production. The second time was about 50 years later, when General Motors installed Unimate, the world's first industrial robot, on its assembly line at the Inland Fisher Guide Plant, New Jersey. Initially...

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 and understand these systems fully. In this article I aim to present my own statement on AI risk, drawing inspiration from the Statement on AI Risk from the Center for AI Safety, a statement endorsed by leading AI scientists and other notable AI figures. I will then try to explain it. I aim to dissect the reality of AI risks without veering into sensationalism. This discussion is not about fear-mongering; it is yet another call to action for a managed and responsible...

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 great responsibility.” The power of AI is undeniable, but if not secured properly, it could end up making every meme a Chuck Norris meme. Imagine a world where malicious actors can manipulate AI systems to make incorrect predictions, steal sensitive data, or even control the AI’s behavior. Without robust AI security, this dystopian scenario could become our reality. Ensuring the security of AI is not just about protecting algorithms; it’s about safeguarding our digital future. And the best way I...

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 digital infrastructures from external threats, breaches, and unauthorized access. On the other hand, AI security has to address unique challenges posed by artificial intelligence systems, ensuring not just their robustness but also their ethical and transparent operation as well as unique internal vulnerabilities intrinsic to AI models and algorithms.

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 a way that the neural network begins to associate it with a certain output, creating a hidden vulnerability. When activated, this vulnerability can cause the neural network to behave unpredictably or make incorrect decisions, often without any noticeable signs of tampering.

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 can result in multiple fragmented instances of the original model operating in parallel, each potentially having different performance characteristics, data sensitivities, and security vulnerabilities.