The Technology Innovation Institute (TII) in Abu Dhabi has launched a 2-micrometer high-power fiber laser for medical and industrial applications. Developed by TII's Directed Energy Research Center, the Thulium-based laser is efficient, compact, and scalable, enabling precise interaction with water-rich materials. TII has partnered with LIMA Photonics, a German MedTech startup, to integrate the laser into clinical solutions, including urinary stone treatment and prostate surgery. Why it matters: This laser technology and partnership showcase the UAE's commitment to translating advanced research into healthcare solutions, positioning Abu Dhabi as a hub for medical technology innovation.
Researchers at TII, in cooperation with University Paderborn and Ruhr University Bochum, have discovered a vulnerability called the Opossum Attack in Transport Layer Security (TLS) impacting protocols like HTTP(S), FTP(S), POP3(S), and SMTP(S). The vulnerability exposes a risk of desynchronization between client and server communications, potentially leading to exploits like session fixation and content confusion. Scans revealed over 2.9 million potentially affected servers, including over 1.4 million IMAP servers and 1.1 million POP3 servers. Why it matters: This discovery highlights the importance of ongoing cybersecurity research in the UAE and internationally to identify and address vulnerabilities in fundamental internet protocols, especially as it led to immediate action by Apache and Cyrus IMAPd.
VentureOne, the commercialization arm of the Advanced Technology Research Council (ATRC), has launched QuantumGate at CyberQ in Abu Dhabi. QuantumGate offers data security products, including QSphere (post-quantum VPN, email, file encryption) and Salina (password-less identity management). These products are based on proprietary Technology Innovation Institute (TII) technology. Why it matters: The launch addresses the imminent threat of quantum computing to current encryption methods, providing sovereign solutions for data protection in the UAE.
The Technology Innovation Institute (TII) has launched the Abu Dhabi Quantum Optical Ground Station (ADQOGS), the first and largest facility in the Arab world and MENA dedicated to secure free-space optical communications. Located at Al Sadeem Observatory, the 2,363 sqm facility features an 800mm tracking telescope, control room, and weather station. ADQOGS will integrate the UAE into the global Quantum Key Distribution (QKD) network and enable ultra-secure key material availability. Why it matters: This establishes the UAE as a regional hub for quantum communication research and positions it at the forefront of developing next-generation secure networking infrastructure.
Technology Innovation Institute (TII) and Burjeel Medical City (BMC) are partnering to develop novel immunotherapy solutions for cancer treatment, focusing on T-cell based therapeutics like CAR-T and TIL therapy. In the first phase, TII will construct a computational platform to identify patient-specific antigens from single-cell transcriptomics data, enabling the design of CAR-T cells. The two-year partnership aims to boost the body's immune system to fight cancer and personalize cancer therapies using TII's technologies. Why it matters: This collaboration signifies the UAE's commitment to advancing cancer care through collaborative research and innovative solutions, potentially establishing the country as a leader in personalized oncology treatments.
Marcus Engsig at DERC has developed DomiRank, a new centrality metric to quantify the dominance of nodes within networks. DomiRank integrates local and global topological information to determine the importance of each node for network stability. The research demonstrates that nodes with high DomiRank values indicate vulnerable areas heavily dependent on dominant nodes. Why it matters: This metric can help identify critical infrastructure components and vulnerabilities in complex systems, enhancing resilience against targeted attacks.
Technology Innovation Institute (TII) has launched a blockchain-powered carbon tracking and trading platform at COP28. The platform, designed by TII’s Cryptography Research Center (CRC), aims to facilitate international trade in carbon tokens and promote green investment. It uses a lightweight, efficient blockchain implementation for transparent and secure verification of tracking and trading activities. Why it matters: The platform supports the UAE's sustainability goals and contributes to the global effort to achieve net-zero emissions by enabling verifiable and trustworthy carbon trading.
Researchers including Dr. Najwa Aaraj developed ML-FEED, a new exploit detection framework using pattern-based techniques. The model is 70x faster than LSTMs and 75,000x faster than Transformers in exploit detection tasks, while also being slightly more accurate. The "ML-FEED" paper won best paper at the 2022 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications. Why it matters: This research enables more efficient real-time security applications and highlights growing AI expertise in the Arab world.
TII's Secure Systems Research Center (SSRC) has partnered with Khalifa University, the University of Turku, and Graz University of Technology on secure mesh communications research projects. The collaborations aim to strengthen the security of Mesh network technology being developed by TII for machine-to-machine and human-to-human communications. The projects will focus on areas like communication resilience, physical layer security, and secure routing to resist eavesdropping, scanning, malware propagation, and jamming. Why it matters: This multi-party collaboration enhances the UAE's position in secure communication technologies, crucial for cyber-physical systems and autonomous operations in the region.
A team led by the Technology Innovation Institute (TII) in Abu Dhabi has developed NATHR-G1, a ground penetrating radar for detecting landmines and unexploded ordnance. The project, involving researchers from Colombia, Germany, Sweden, and Switzerland, builds on earlier work using radar to detect buried objects. NATHR-G1 incorporates machine learning for advanced signal processing and object identification. Why it matters: This humanitarian application of AI and robotics based in the UAE could significantly reduce casualties from landmines and other explosive remnants of war.
TII's Secure Systems Research Center (SSRC) is partnering with Khalifa University, University of Modena and Reggio Emilia, University of Bologna, University of Waterloo, and McMaster University to develop a RISC-V-based secure flight computer system. The project aims to create an open RISC-V-based System on a Chip (SoC) architecture and software stack for secure application processors in drone flight computers. The collaboration seeks to improve performance, efficiency, reliability, and security relative to current commercial flight computer systems. Why it matters: This international collaboration strengthens the UAE's position in advanced hardware and software co-design for critical applications like drone technology, while also fostering local expertise through partnerships with UAE universities.
TII's Secure Systems Research Center (SSRC) has become a strategic member of RISC-V International to advance the development of open-source Instruction Set Architecture (ISA) for computer chips. SSRC aims to contribute to the RISC-V community by developing security and resilience features in processors and platforms, fostering innovation in end-to-end security. SSRC will conduct open source research to secure communications between edge devices and cloud infrastructure, and harden device hardware and software to prevent malware. Why it matters: This move enables the UAE to have greater control and independence in computing platform design, reducing reliance on proprietary architectures and enhancing security and resilience in critical infrastructure.
TII's Cryptography Research Center (CRC) has formed partnerships with several international universities, including Ruhr-University Bochum, Radboud University, Khalifa University, and others, to advance cryptography research. The collaborations cover areas like privacy-preserving cloud computing, lightweight cryptography, enhanced IoT protocols, and post-quantum cryptography schemes. CRC had previously partnered with Yale University and co-authored a book with New York University. Why it matters: These partnerships signal the UAE's commitment to developing advanced cryptographic capabilities and contributing to global research in data security and privacy.
TII's Secure Systems Research Center (SSRC) has announced partnerships with Khalifa University, Georgia Institute of Technology, Tampere University, and SUPSI across multiple projects. These collaborations aim to advance secure systems research, with Khalifa University focusing on machine learning for exfiltration detection and secure drone communication, Georgia Tech working on attack-resilient software for cyber-physical systems, Tampere University focusing on critical infrastructure security, and SUPSI exploring AI-based secure autonomous navigation on Nano-UAVs. Why it matters: These partnerships highlight the UAE's commitment to advancing research and development in secure systems and autonomous technologies, fostering innovation and expertise in critical areas like cybersecurity and UAV technology.
Technology Innovation Institute (TII) in Abu Dhabi has launched the UAE’s first secure cloud technologies programme via its Cryptography Research Center (CRC). The program will focus on advancing Privacy Enhancing Technologies (PETs) like fully homomorphic encryption (FHE) and secure multi-party computation (MPC). TII researchers are also developing hardware accelerators to improve the efficiency of FHE. Why it matters: The initiative addresses growing security and privacy challenges in cloud computing, positioning the UAE as a leader in advanced cryptographic solutions for data protection.
Abu Dhabi’s Technology Innovation Institute (TII) has launched the UAE's first Post-Quantum Cryptography (PQC) software library, developed by the Cryptography Research Center (CRC). The library, written in C, supports various computer architectures and operating systems and has been integrated into secure communication products. It includes algorithms designed to protect against attacks by quantum computers, which threaten classic cryptographic methods. Why it matters: This launch positions the UAE among nations with advanced digital security capabilities, enhancing data confidentiality and security in the face of emerging quantum computing threats.
TII's Secure Systems Research Center (SSRC) has partnered with Purdue University on a three-year cybersecurity project focused on ensuring the safe and efficient use of Unmanned Aerial Vehicles (UAVs) in urban environments. The collaboration will study security and resilience in cyber-physical and autonomous systems, addressing vulnerabilities in communication, navigation, and command and control. The project includes four phases: modeling and analysis of UAS security, developing algorithms for high-assurance autonomy, constructing an experimental environment, and testing mitigation strategies. Why it matters: The partnership enhances the UAE's capabilities in securing critical digital systems and fosters the growth of commercial autonomous drones and robots, opening new opportunities for enterprises.
The paper introduces SectEval, a new benchmark to evaluate sectarian biases in LLMs concerning Sunni and Shia Islam, available in English and Hindi. Results show significant inconsistencies in LLM responses based on language, with some models favoring Shia responses in English but Sunni in Hindi. Location-based experiments further reveal that advanced models adapt their responses based on the user's claimed country, while smaller models exhibit a consistent Sunni-leaning bias.
MBZUAI researchers have developed an automatic interview system that uses LLMs to elicit nuanced, role-specific information from job candidates, improving early-stage hiring decisions. The system updates its belief about an applicant's rubric-oriented latent traits in a calibrated way based on their interview performance. Evaluation on simulated interviews showed the system's belief converges towards the simulated applicants' constructed ability levels.
The UAE Central Bank has released new guidance outlining principles for the responsible implementation of Artificial Intelligence within the financial sector. This initiative aims to ensure the safe and ethical deployment of AI technologies by financial institutions across the Emirates. The guidance likely addresses areas such as data privacy, fairness, transparency, and risk management associated with AI applications. Why it matters: This marks a significant step in establishing a regulatory framework for AI adoption in a critical economic sector, fostering responsible innovation and maintaining financial stability in the region.
The paper introduces ILION, a deterministic execution gate designed to ensure the safety of autonomous AI agents by classifying proposed actions as either BLOCK or ALLOW. ILION uses a five-component cascade architecture that operates without statistical training, API dependencies, or labeled data. Evaluation against existing text-safety infrastructures demonstrates ILION's superior performance in preventing unauthorized actions, achieving an F1 score of 0.8515 with sub-millisecond latency.
Saudi Arabia is participating in the drafting process of the International AI Safety Report, which is slated for release in 2026. This involvement underscores the Kingdom's commitment to global AI governance and the development of responsible AI standards. The initiative aims to establish unified international guidelines for the safe deployment and use of artificial intelligence technologies. Why it matters: Saudi Arabia's active role in shaping this crucial international document positions it as a key contributor to global AI policy and helps advance its strategic vision for a secure and ethical AI future.
The paper introduces SalamahBench, a new benchmark for evaluating the safety of Arabic Language Models (ALMs). The benchmark comprises 8,170 prompts across 12 categories aligned with the MLCommons Safety Hazard Taxonomy. Five state-of-the-art ALMs, including Fanar 1 and 2, ALLaM 2, Falcon H1R, and Jais 2, were evaluated using the benchmark. Why it matters: The benchmark enables standardized, category-aware safety evaluation, highlighting the necessity of specialized safeguard mechanisms for robust harm mitigation in ALMs.
A national survey in Saudi Arabia of 330 participants reveals that 93% are actively using Generative AI, primarily for text-based tasks, while awareness and understanding remain uneven. Participants recognize benefits like productivity but caution against risks such as privacy, misinformation, and ethical misuse. The study highlights the need for AI literacy, culturally aligned solutions, and stronger frameworks for responsible deployment in Saudi Arabia.
This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.
The paper introduces FanarGuard, a bilingual moderation filter for Arabic and English language models that considers both safety and cultural alignment. A dataset of 468K prompt-response pairs was created and scored by LLM judges on harmlessness and cultural awareness to train the filter. The first benchmark targeting Arabic cultural contexts was developed to evaluate cultural alignment. Why it matters: FanarGuard advances context-sensitive AI safeguards by integrating cultural awareness into content moderation, addressing a critical gap in current alignment techniques.
This paper analyzes the energy consumption and carbon footprint of LLM inference in the UAE compared to Iceland, Germany, and the USA. The study uses DeepSeek Coder 1.3B and the HumanEval dataset to evaluate code generation. It provides a comparative analysis of geographical trade-offs for climate-aware AI deployment, specifically addressing the challenges and potential of datacenters in desert regions.
This paper proposes a framework for understanding AI sovereignty as a balance between autonomy and interdependence, considering global data, supply chains, and standards. It introduces a planner's model with policy heuristics for equalizing marginal returns across sovereignty pillars and setting openness. The model is applied to India and the Middle East (Saudi Arabia and UAE), finding that managed interdependence, rather than isolation, is key for AI sovereignty.
KAUST startup Polymeron is converting date and poultry waste into biodegradable plastics, aligning with Saudi Vision 2030's goals for economic diversification and sustainability. The company aims to process 20,000 tons of waste annually, contributing to a circular economy and creating jobs in polymers, manufacturing, and sales. Polymeron is building its pilot plant at KAUST with support from the National Technology Development Program (NTDP). Why it matters: This initiative addresses plastic pollution and agricultural waste while creating a new industry in the Kingdom, tapping into a global market for biodegradable plastics valued at $13 billion in 2024.
This study compares AI uptake in the UAE and Kuwait, analyzing how constitutional, collective-choice, and operational rules shape AI implementation and its impact on citizen centricity and public value creation. It finds that the UAE's concentrated authority and pro-innovation environment enable scaling AI initiatives, while Kuwait's dispersed governance and cautious approach limit progress despite similar resources. The research highlights the importance of vertical rule coherence over wealth in determining AI's public-value yield.
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.
An article published in Nature examines the profound implications of Artificial Intelligence for the workforce within the Gulf Cooperation Council (GCC) countries. It likely delves into the challenges of job displacement and the critical need for reskilling initiatives, alongside the opportunities presented by AI integration across various sectors. The analysis presumably outlines strategies for GCC nations to adapt their labor markets and education systems to the evolving future of work. Why it matters: Understanding these dynamics is crucial for GCC nations to develop effective policies for workforce development, economic resilience, and sustainable growth in an AI-driven era.
The UAE has introduced new regulations governing the use of Artificial Intelligence within its national elections. These regulations are designed to enhance transparency and maintain the integrity of the electoral process. The initiative addresses potential applications of AI in electoral contexts to prevent misuse and foster public trust. Why it matters: This development positions the UAE as a leader in establishing frameworks for responsible AI governance in sensitive democratic processes, potentially influencing regional and international discussions on AI ethics in elections.
KAUST professors Roberto Di Pietro and Marc Dacier co-authored a paper on cybersecurity strategies for Saudi Arabia and the Arab world, published in Communications of the ACM. The paper outlines a multidisciplinary framework for digitization aligned with Saudi Vision 2030, emphasizing global best practices, cultural adaptation, and capacity building. KAUST is positioned to advise on national cybersecurity policy in cooperation with the Saudi National Cybersecurity Authority. Why it matters: The framework addresses the critical need for advanced cybersecurity to support Saudi Arabia's rapidly growing digital economy and infrastructure.
This survey paper analyzes over 40 benchmarks used to evaluate Arabic large language models, categorizing them into Knowledge, NLP Tasks, Culture and Dialects, and Target-Specific evaluations. It identifies progress in benchmark diversity but also highlights gaps like limited temporal evaluation and cultural misalignment. The paper also examines methods for creating benchmarks, including native collection, translation, and synthetic generation. Why it matters: The survey provides a comprehensive reference for Arabic NLP research and offers recommendations for future benchmark development to better align with cultural contexts.
This paper presents the development and validation of an Arabic version of the Attitudes Toward Large Language Models (AT-GLLM and AT-PLLM) scales, adapted from the original English versions. The study involved translating the scales and testing them on a sample of 249 Arabic-speaking adults. The translated scales demonstrated strong psychometric properties, including a two-factor structure, measurement invariance across genders, and good reliability and validity. Why it matters: This provides a culturally relevant tool for assessing attitudes toward LLMs in the Arab world, crucial for localized research and policy-making in the rapidly growing field of Arabic AI.
UAE President Sheikh Mohamed bin Zayed Al Nahyan met with OpenAI CEO Sam Altman to discuss opportunities for collaboration in artificial intelligence. They explored the potential of AI to support the UAE's growth across various sectors. The meeting also covered the importance of AI safety and regulation. Why it matters: This high-level engagement indicates the UAE's commitment to being a leader in AI adoption and development, while also highlighting the need for responsible AI governance.
The study analyzes over 1,000 images generated by ImageFX, DALL-E V3, and Grok for 56 Saudi professions, finding significant gender imbalances and cultural inaccuracies. DALL-E V3 exhibited the strongest gender stereotyping, with 96% male depictions, particularly in leadership and technical roles. The research underscores the need for diverse training data and culturally sensitive evaluation to ensure equitable AI outputs that accurately reflect Saudi Arabia's labor market and culture.
The paper introduces AraHalluEval, a new framework for evaluating hallucinations in Arabic and multilingual large language models (LLMs). The framework uses 12 fine-grained hallucination indicators across generative question answering and summarization tasks, evaluating 12 LLMs including Arabic-specific, multilingual, and reasoning-based models. Results show factual hallucinations are more common than faithfulness errors, with the Arabic model Allam showing lower hallucination rates. Why it matters: This work addresses a critical gap in Arabic NLP by providing a comprehensive tool for assessing and mitigating hallucination in LLMs, which is essential for reliable AI applications in the Arabic-speaking world.
The UAE is making major investments in AI to diversify its economy and exert global influence. These investments include partnerships with global tech companies, development of AI infrastructure, and the establishment of AI-focused institutions. The UAE aims to become a leader in AI research and development, attracting talent and fostering innovation. Why it matters: This strategic focus on AI could reshape the UAE's economic and geopolitical role in the region and beyond.
A KAUST-led study in Nature proposes reversing land degradation by 2050 through increased sustainable seafood production, reduced food waste, and land restoration. The study suggests straightforward measures like modifying economic incentives and promoting sustainable aquaculture policies. Researchers estimate these policies could save a land area roughly the size of Africa. Why it matters: The KAUST-led research offers a tangible blueprint for addressing critical food security challenges in arid regions like Saudi Arabia and globally.
Saudi Arabia's national AI authority, SDAIA (Saudi Data and AI Authority), is reportedly developing a National AI Index. This index aims to measure and track the Kingdom's progress and capabilities across various dimensions of artificial intelligence. It is expected to encompass areas such as research and development, talent, infrastructure, and ethical considerations. Why it matters: This initiative provides a crucial framework for evaluating Saudi Arabia's AI ecosystem, guiding future strategic investments, and benchmarking its national AI capabilities against international standards.
Researchers introduce UnsafeChain, a new safety alignment dataset designed to improve the safety of large reasoning models (LRMs) by focusing on 'hard prompts' that elicit harmful outputs. The dataset identifies and corrects unsafe completions into safe responses, exposing models to unsafe behaviors and guiding their correction. Fine-tuning LRMs on UnsafeChain demonstrates enhanced safety and preservation of general reasoning ability compared to existing datasets like SafeChain and STAR-1.
This paper introduces Absher, a new benchmark for evaluating LLMs' linguistic and cultural competence in Saudi dialects. The benchmark comprises over 18,000 multiple-choice questions spanning six categories, using dialectal words, phrases, and proverbs from various regions of Saudi Arabia. Evaluation of state-of-the-art LLMs reveals performance gaps, especially in cultural inference and contextual understanding, highlighting the need for dialect-aware training.
Researchers developed a two-stage AI pipeline to predict desalination performance efficiency losses due to climate factors in the UAE, achieving 98% accuracy. The model forecasts aerosol optical depth (AOD) and uses it to predict desalination efficiency, incorporating meteorological data. A dust-aware control logic was developed to optimize plant operations, and an interactive dashboard was created for decision support.
Researchers from MBZUAI introduce Forget-MI, a machine unlearning method tailored for multimodal medical data, enhancing privacy by removing specific patient data from AI models. Forget-MI utilizes loss functions and perturbation techniques to unlearn both unimodal and joint data representations. The method demonstrates superior performance in reducing Membership Inference Attacks and improving data removal compared to existing techniques, while preserving overall model performance and enabling data forgetting.
A new methodology emulating fact-checker criteria assesses news outlet factuality and bias using LLMs. The approach uses prompts based on fact-checking criteria to elicit and aggregate LLM responses for predictions. Experiments demonstrate improvements over baselines, with error analysis on media popularity and region, and a released dataset/code at https://github.com/mbzuai-nlp/llm-media-profiling.
This paper introduces a novel evaluation framework for Arabic language models, addressing gaps in linguistic accuracy and cultural alignment. The authors analyze existing datasets and present the Arabic Depth Mini Dataset (ADMD), a curated collection of 490 questions across ten domains. Evaluating GPT-4, Claude 3.5 Sonnet, Gemini Flash 1.5, CommandR 100B, and Qwen-Max using ADMD reveals performance variations, with Claude 3.5 Sonnet achieving the highest accuracy at 30%. Why it matters: The work emphasizes the importance of cultural competence in Arabic language model evaluation, providing practical insights for improvement.
This paper analyzes Arabic text generated by LLMs like ALLaM, Jais, Llama, and GPT-4 across academic and social media domains using stylometric analysis. The study found detectable linguistic patterns that differentiate human-written from machine-generated Arabic text. BERT-based detection models achieved up to 99.9% F1-score in formal contexts, though cross-domain generalization remains a challenge. Why it matters: The research lays groundwork for detecting AI-generated misinformation in Arabic, a crucial step for preserving information integrity in Arabic-language contexts.
MBZUAI researchers introduce SocialMaze, a new benchmark for evaluating social reasoning capabilities in large language models (LLMs). SocialMaze includes six diverse tasks across social reasoning games, daily-life interactions, and digital community platforms, emphasizing deep reasoning, dynamic interaction, and information uncertainty. Experiments show that LLMs vary in handling dynamic interactions, degrade under uncertainty, but can be improved via fine-tuning on curated reasoning examples.