Tag: ARTICLE

Last week, the Saudi Data and Artificial Intelligence Authority (SDAIA) launched a nationwide awareness campaign called “Ask Before”, intended to educate the public about the significance of personal data ahead of the implementation of a new national personal data protection system. Emphasizing responsible data handling, privacy preservation, and fostering trust and collaboration between commercial entities and private individuals, “Ask Before” supports KSA’s new Personal Data Protection Law (PDPL), which became enforceable on September 14th. The need for such a campaign stems from the fact that the PDPL is the first regulation of its kind rolled out in the kingdom, activated five years after...

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...

Artificial Intelligence (AI) has quickly, nay, explosively transitioned from a sci-fi concept to a foundational pillar of modern business. A recent report by McKinsey highlights the rise of generative AI, revealing that within less than a year of its public debut, a staggering one-third of surveyed organizations have integrated generative AI into at least one business function. Gartner predicted that by 2024, 75% of enterprises will shift from piloting to operationalizing AI. I can’t recall seeing any other emerging technology in history take off as quickly as AI has. Keep in mind, when I discuss AI adoption, I am not just referring...

Neural networks, inspired by the human brain, play a pivotal role in modern technology, powering applications like voice recognition and medical diagnosis. However, their complexity makes them vulnerable to cybersecurity threats, specifically Trojan attacks, which can manipulate them to make incorrect decisions. Given their increasing prevalence in systems that affect our daily lives, from smartphones to healthcare, it’s crucial for everyone to understand the importance of securing these advanced computing models against such vulnerabilities. The Trojan Threat in Neural Networks What is a Trojan Attack? In the context of computer security, a “Trojan attack” refers to malicious software (often called “malware“) that disguises...

Ask most people what they remember from 2016 - if they remember anything at all - and there are usually two big events that float to the front of their minds: Britain voted to leave the European Union and the United States voted Donald Trump into the White House. Together, these two episodes sent shock waves around the world. In the UK, the Brexit referendum was followed by a national decline in mental health. In the US, American college students exhibited levels of stress comparable to PTSD. Even beyond those borders, Brexit and the Trump election became emblematic of the...

Homo sapiens is an incredibly adaptable species, arguably the most adaptable ever. But it is also a forgetful one, quick to take things for granted. Many of us can remember when cell phones first emerged, when the internet first became publicly available, when the first iPhone was released. These momentous shifts occurred within a generation, altering the nature of society and civilization. Just a few decades ago, none of these existed, but by the time Covid-19 hit, billions of people were able to lift their smartphone and video call a loved one on the other side of the world. At...

Machine learning models have become integral components in a myriad of technological applications, ranging from data analytics and natural language processing to autonomous vehicles and healthcare diagnostics. As these models evolve, they often undergo a process known as model fragmentation, where various versions, architectures, or subsets of a model are deployed across different platforms or use cases. While fragmentation enables flexibility and adaptability, it also introduces a host of unique security challenges. These challenges are often overlooked in traditional cybersecurity discourse, yet they are crucial for the safe and reliable deployment of machine learning systems in our increasingly interconnected world. What is Model Fragmentation? Model fragmentation is the phenomenon...

In the cybersecurity arena, artificial intelligence classifiers like neural networks and support vector machines have become indispensable for real-time anomaly detection and incident response. However, these algorithms harbor vulnerabilities that are susceptible to sophisticated evasion tactics, including adversarial perturbations and feature-space manipulations. Such methods exploit the mathematical foundations of the models, confounding their decision-making capabilities. These vulnerabilities are not just theoretical concerns but pressing practical issues, especially when deploying machine learning in real-world cybersecurity contexts that require resilience against dynamically evolving threats. Addressing this multidimensional challenge is part of the broader emerging field of adversarial machine learning, which seeks to develop robust algorithms and integrated security...

In the burgeoning field of machine learning, data security has transitioned from being an optional consideration to a critical component of any robust ML workflow. Traditional encryption methods often fall short when it comes to securing ML models and their training data. Unlike standard encryption techniques, which require data to be decrypted before any processing or analysis, Homomorphic Encryption allows computations to be performed directly on the encrypted data. This mitigates the risks associated with exposing sensitive information during the data processing stage, a vulnerability that has been exploited in various attack vectors like data poisoning and model inversion attacks. Through the utilization...

Machine learning (ML) and artificial intelligence (AI) have rapidly transitioned from emerging technologies to indispensable tools across diverse sectors such as healthcare, finance, and cybersecurity. Their capacity for data analysis, predictive modeling, and decision-making holds enormous transformative potential, but it also introduces a range of vulnerabilities. One of the most insidious among these is data poisoning, a form of attack that targets the very lifeblood of ML and AI: the data used for training. Understanding and addressing data poisoning is critical, not just from a technical standpoint but also due to its far-reaching real-world implications. A poisoned dataset can significantly degrade the performance...

In today’s digitally interconnected world, the rapid advances in machine learning and artificial intelligence (AI) have ushered in groundbreaking innovations but also new vulnerabilities, one of which is adversarial attacks. These attacks manipulate data to deceive machine learning models, affecting their performance and reliability. A particular subset that often flies under the radar focuses on semantics, the meaning behind data. Semantics play a pivotal role in various AI applications, from natural language processing to computer vision. What Are Adversarial Attacks? Adversarial attacks represent a class of manipulative techniques aimed at deceiving machine learning models by altering input data in subtle, often indiscernible ways. These attacks...

In our data-driven world, the term “model” often conjures images of complex algorithms sorting through numbers, text, or perhaps even images. But what if a single model could handle not just one type of data but many? Enter multimodal models, the high-performers of machine learning that can interpret text, analyze images, understand audio, and sometimes even more, all at the same time. These models have revolutionized industries from healthcare to self-driving cars by offering more comprehensive insights and decision-making capabilities. Yet, as we continue to integrate these advanced models into our daily lives, an urgent question emerges: How secure are they?...

The proliferation of AI and Machine Learning (ML), from facial recognition systems to autonomous vehicles and personalized medicine, underscores the criticality of cybersecurity in these areas. AI and ML are revolutionizing cybersecurity. They can swiftly analyze vast data sets, pinpointing anomalies that might indicate cyber threats—tasks that would be daunting for human analysts. Machine learning models can also adapt to new types of threats as they evolve, offering a dynamic defense against cyber adversaries. Importance of Understanding Threats in AI/ML While AI and ML provide potent tools for defending against traditional cybersecurity threats, they also introduce a new class of vulnerabilities that are exclusive to machine learning models....

This article concludes our four-part series on the basic differences between traditional IT security and blockchain security. Previous articles discussed the security differences critical for node operators, smart contract developers, and end users. In many ways, Security Operations Center (SOC) analysts and node operators face similar blockchain-related security challenges. The scale of SOC operations brings with it unique security challenges. Reduced telemetry from decentralized infrastructure hinders SOC detection, but additional information available on-chain could drive new ways of detecting security-related events. The effectiveness of a SOC that is focused on detecting and responding to blockchain, crypto, and DeFi threats might be...

In the ever-evolving landscape of technology, Artificial Intelligence (AI) and cybersecurity stand as two pillars that have significantly influenced various industries, ranging from healthcare to finance. Among AI’s frontier technologies, Generative Adversarial Networks (GANs) have become a cornerstone in driving innovations in data generation, image synthesis, and content creation. However, these neural network architectures are not impervious to cyber vulnerabilities; one emerging and largely overlooked threat is GAN Poisoning. Unlike traditional cyber-attacks that target data integrity or access controls, GAN Poisoning subtly manipulates the training data or alters the GAN model itself to produce misleading or malicious outputs. This raises the unsettling prospect that,...