IDC Directions 2025, a pivotal event highlighting key trends shaping the META region, offered valuable insights into the technological transformations poised to redefine industries by 2025. Jyoti Lalchandani, Regional Managing Director, META, Central Asia & India, IDC shared her perspective on the technology trends set to define the Middle East’s digital transformation. According to Jyoti, AI and machine learning are leading the way in sectors such as government, healthcare, and financial services. Generative AI, in particular, will continue to push boundaries in terms of creativity, automation, and productivity, especially as ethical considerations around AI usage grow in importance. Data sovereignty and local cloud infrastructure are expected to remain high on the agenda, particularly within the GCC countries. National cloud strategies are expected to play a central role in ensuring data privacy and regulatory compliance, enabling governments to maintain control over data while embracing cutting-edge technologies. The region is increasingly turning to multi-cloud and hybrid cloud solutions, allowing for greater flexibility and compliance across digital ecosystems. Looking ahead, Jyoti emphasized that governments in the Middle East will continue to prioritize technology investments aimed at enhancing smart cities, e-governance, and digital education. These efforts are part of a broader strategy to position the region as a global technology hub, one where AI-powered digital services redefine the quality of life and efficiency for citizens. source

Artificial intelligence is already significantly involved in various aspects of arbitration and posing challenges for maintaining confidentiality, but relatively quickly implementable practices can be utilized as safeguards as AI tools continue to be integrated, says David Coher at CoherADR. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Last week, Chinese startup DeepSeek sent shockwaves in the AI community with its frugal yet highly performant open-source release, DeepSeek-R1. The model uses pure reinforcement learning (RL) to match OpenAI’s o1 on a range of benchmarks, challenging the longstanding notion that only large-scale training with powerful chips can lead to high-performing AI.  However, with the blockbuster release, many have also started pondering the implications of the Chinese model, including the possibility of DeepSeek transmitting personal user data to China.  The concerns started with the company’s privacy policy. Soon, the issue snowballed, with OpenAI technical staff member Steven Heidel indirectly suggesting that Americans love to “give away their data” to the Chinese Communist Party to get free stuff. The allegations are significant from a security standpoint, but the fact is that DeepSeek can only store data on Chinese servers when the models are used through the company’s own ChatGPT-like service.  If the open-source model is hosted locally or orchestrated via GPUs in the U.S., the data does not go to China.  Concerns about DeepSeek’s privacy policy In its privacy policy, which was also unavailable for a couple of hours, DeepSeek notes that the company collects information in different ways, including when users sign up for its services or use them. This means everything from account setup information — names, emails, numbers and passwords — to usage data such as text or audio input prompts, uploaded files, feedback and broader chat history goes to the company. But, that’s not all. The policy further states that the information collected will be stored in secure servers located in the People’s Republic of China and may be shared with law enforcement agencies, public authorities and others for reasons such as helping investigate illegal activities or just complying with applicable law, legal process or government requests.  The latter is important as China’s data protection laws allow the government to seize data from any server in the country with minimal pretext. With such a range of information on Chinese servers, a myriad of things can be triggered, including profiling individuals and organizations, leakage of sensitive business data, and even cyber surveillance campaigns. The catch While the policy can easily raise security and privacy alarms (as it already has for many), it is important to note that it applies only to DeepSeek’s own services — apps, websites and software — using the R1 model in the cloud. If you have signed up for the DeepSeek Chat website or are using the DeepSeek AI assistant on your Android or iOS device, there’s a good chance that your device data, personal information and prompts so far have been sent to and stored in China.  The company has not shared its stance on the matter, but given that the iOS DeepSeek app has been trending as #1, even ahead of ChatGPT, it’s fair to say that many people may have already signed up for the assistant to test out its capabilities — and shared their data at some level in the process.  The Android app of the service has also scored over a million downloads. DeepSeek-R1 is open-source itself As for the core DeepSeek-R1 model, there’s no question of data transmission.  R1 is fully open-source, which means teams can run it locally for their targeted use case through open-source implementation tools like Ollama. This ensures the model does its job effectively while keeping data restricted to the machine itself. According to Emad Mostaque, former founder and CEO of Stability AI, the R1-distill-Qwen-32B model can run smoothly on the new Macs with 16GB of vRAM. As an alternative, teams can also use GPU clusters from third-party orchestrators to train, fine-tune and deploy the model — without data transmission risks. One of these is Hyperbolic Labs, which allows users to rent a GPU to host R1. The company also allows inference via a secured API. That said, in case one’s looking just to chat with DeepSeek-R1 to solve a particular reasoning problem, the best way to go right now is with Perplexity. The company has just added R1 to its model selector, allowing users to do deep web research with chain-of-thought reasoning. According to Aravind Srinivas, the CEO of Perplexity, the company has enabled this use case for its customers by hosting the model in data center servers located in the U.S. and Europe.  Long story short: your data is safe as long as it’s going to a locally hosted version of DeepSeek-R1, whether it’s on your machine or a GPU cluster somewhere in the West. source

Nvidia stock has recovered somewhat since then — it rose nearly 9% on Tuesday — with industry watchers noting that while the Chinese LLM adds a healthy dose of competition to the gen AI landscape with its innovations, the market may have overreacted. Still, analysts believe DeepSeek’s entrance heralds the possibility for more affordable gen AI initiatives. A shot to the system Sidestepping the frenzy, Nvidia on Thursday said it was making the DeepSeek-R1 NIM available to help developers experiment with its logic inference, reasoning, mathematics, coding, and language capabilities for customizing their own specialized AI agents. The NIM runs on eight H200 GPUs connected via Nvidia NVLink and NVLink Switch. “Instead of offering direct responses, reasoning models like DeepSeek-R1 perform multiple inference passes over a query, conducting chain-of-thought, consensus, and search methods to generate the best answer,” Erik Pounds, director of product marketing at Nvidia, wrote in a blog post Thursday. source

By Jack Rodgers ( January 31, 2025, 1:27 PM EST) — DLA Piper has hired a financial regulatory and technology partner who has a range of experience helping found and lead digital platforms and technology businesses to its New York team, the firm announced Thursday…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

It’s a fast and furious week in the world of generative AI (genAI) and AI security. Between DeepSeek topping app store downloads, Wiz discovering a pretty basic developer error by the team behind DeepSeek, Google’s report on adversarial misuse of generative artificial intelligence, and Microsoft’s recent release of Lessons from red teaming 100 generative AI products — if securing AI wasn’t on your radar before (and judging by my client inquiries and guidance sessions, that’s definitely not the case), it should be now. All of this news is timely, with my report covering Machine Learning And Artificial Intelligence Security: Tools, Technologies, And Detection Surfaces having just published. The research from Google and Microsoft is worth the read, and it’s also timely. For example, one of Microsoft’s top three takeaways is that generative AI amplifies existing security risks and introduces some new ones. We discuss this in our report, The CISO’s Guide To Securing Emerging Technology, as well as in our newly released ML/AI security report. Microsoft’s second takeaway is that the detection and attack surface of genAI goes well beyond prompts, which also reinforces the conclusions of our research. Focus On The Top Three GenAI Security Use Cases In our research, we simplify the top three use cases that security leaders need to worry about and make recommendations for prioritizing when you need to worry about them. Security leaders securing generative AI should: Secure users who are interacting with generative AI. This includes employee — and customer — use of AI tools. This one feels like it’s been around awhile, because it has, and unfortunately, only imperfect solutions exist right now. Here, we focus primarily on “prompt security,” with scenarios such as prompt injection, jailbreaking, and, simplest of all, data leakage. This is a bidirectional detection surface for security leaders. You need to understand inputs (from the users) and outputs (to the users). Security controls need to examine and apply policies in both directions. Secure applications that represent the gateway to generative AI. Pretty much every interaction that customers, employees, and users have with AI comes via an application that sits on top of an underlying ML or AI model of some variety. These can be as simple as a web or mobile interface to submit questions to a large language model (LLM) or an interface that presents decisions about the likelihood of fraud based on a transaction. You must protect these applications like others, but because they interact with LLMs directly, additional steps are necessary. Poor application security processes and governance makes this far more difficult, as we have more apps — and more code — as a result of generative AI. Secure models that underpin generative AI. In the generative AI world, the models get all the attention, and rightfully so. They are the “engine” of generative AI. Protecting them matters. But most attacks against models — for now — are academic in nature. An adversary could attack your model with an inference attack to harvest data. Or they could just phish a developer and steal all the things. One of these approaches is time-tested and works well. It’s good to start experimenting with model security technologies soon so that you’ll be ready once attacks on models go from being novel to mainstream. Don’t Forget About The Data We didn’t forget about data, because protecting data exists everywhere and goes well beyond the items above. That’s where research on data security platforms and data governance comes in (and where I step aside, because that’s not my area of expertise). Think of data as underpinning all of the above with some common — and brand-new — approaches. This sets up the overarching challenge, which allows us to get into the specifics of how to secure these elements. Things might look out of order at first, but I’ll explain why this is the necessary approach. The steps, in order, are: Start with securing prompts that are user-facing. Any prompt that touches internal or external users needs guardrails as soon as possible. Many security leaders we’ve spoken with mentioned finding that customer- and employee-facing generative AI already existed well before they were aware of it. And of course, BYOAI (bring your own AI) is alive and well, as the DeepSeek announcements have showcased. Then move on to discovery across the rest of your technology estate. Look up any framework, and “discovery” or “plan” is always the first step. But those frameworks exist in a perfect world. Cybersecurity folks … well, we live in the real world. This is why discovery is second here. If customer- and employee-accessible prompts exist, they are your number one priority. Once you’ve addressed those, you can start the discovery process on all the other implementations of generative and legacy AI, machine learning, and applications interacting with them across your enterprise. That’s why this is the second step. It may not feel “right,” but it’s the pragmatic choice. Move on to model security after that … for now. At least in the immediate future, model security can take a bit of a back seat for industries outside of technology, financial services, healthcare, and government. It’s not a problem that you should ignore, or you’ll pay a price down the line, but it’s one where you have some breathing room. The full report includes more insights, identifies potential vendors in each category, and gives additional context on steps you can take within each area. In the meantime, if you have any questions about securing AI and ML, request an inquiry or guidance session with me or one of my colleagues. source

What is Six Sigma? Six Sigma is a quality management methodology used to help businesses improve current processes, products, or services by discovering and eliminating defects. The goal is to streamline quality control in manufacturing or business processes, so there’s little to no variance throughout. While Six Sigma has its origins in manufacturing to oversee quality control in production, it’s since evolved into a common business practice in industries such as technology, finance, and healthcare. Six Sigma was trademarked by Motorola in 1993, and the name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. Motorola used the term because a Six Sigma process is expected to be defect-free 99.99966% of the time, allowing for 3.4 defective features for every million opportunities. Motorola initially set this goal for its own manufacturing operations, but it quickly became a buzzword and widely adopted standard. Six Sigma is specifically designed to help large organizations with quality management. In 1998, GE CEO Jack Welch helped thrust Six Sigma into the limelight by donating upward of $1 million as a thank you to the company, recognizing how Six Sigma positively impacted GE’s operations, and promoting the process for large organizations. After that, Fortune 500 companies followed suit, and Six Sigma has been popular with large organizations ever since. While Six Sigma remains a popular and valuable methodology, other quality improvement frameworks have popped up in its wake, such as Agile and Lean. source

By Christopher Cole ( January 31, 2025, 10:15 PM EST) — Two communications attorneys have urged the Federal Communications Commission to reject the $4.4 billion transfer of UScellular spectrum licenses to T-Mobile, at least until their False Claims Act dispute with the company can be resolved in the D.C. Circuit…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

By global standards, the U.S. has a relatively low level of religious nationalism, but it stands out from other high-income countries National and royal flags fly outside religious sites in Thailand, Turkey, the U.S. and Israel. (Clockwise from top left: Vera Tikhonova, Westend61, Samuel Corum and Paul Souders, all via Getty Images) Pew Research Center conducted this survey to examine the role of religion in public life in 36 countries across the Asia-Pacific region, Europe, Latin America, the Middle East-North Africa region, North America and sub-Saharan Africa. The countries have a variety of historically predominant religions, including Buddhism, Christianity, Hinduism, Islam and Judaism. For non-U.S. data, this report draws on nationally representative surveys of 41,503 adults conducted from Jan. 5 to May 22, 2024. All surveys were conducted over the phone with adults in Canada, France, Germany, Greece, Italy, Japan, Malaysia, the Netherlands, Singapore, South Korea, Spain, Sweden and the United Kingdom. Surveys were conducted face-to-face in Argentina, Bangladesh, Brazil, Chile, Colombia, Ghana, Hungary, India, Indonesia, Israel, Kenya, Mexico, Nigeria, Peru, the Philippines, Poland, South Africa, Sri Lanka, Thailand, Tunisia and Turkey. In Australia, we used a mixed-mode, probability-based online panel. In the United States, we surveyed 12,693 adults from Feb. 13 to 25, 2024. Most of the respondents (10,642) are members of Pew Research Center’s American Trends Panel (ATP), an online survey panel recruited through national random sampling of residential addresses, which gives nearly all U.S. adults a chance of selection. The remaining U.S. respondents (2,051) are members of three other panels: the Ipsos KnowledgePanel, the NORC Amerispeak Panel and the SSRS Opinion Panel. All three are national survey panels recruited through random sampling (not “opt-in” polls). We used these additional panels to ensure that the survey would have enough Jewish and Muslim respondents to be able to report on their views. Additional survey questions were asked on a follow-up survey conducted from April 1 to 7, 2024, among 3,600 ATP members who had previously participated in the February survey. The U.S. data is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education, religious affiliation and other categories. Read more about the ATP’s methodology. Throughout the report, we analyze respondents’ attitudes based on where they place themselves on an ideological scale, their support for populist parties, their religious identification, their educational attainment, their income, and whether they live in high- or middle-income countries. For more on each of these measures, visit the methodology. Religious nationalism index In this report, we classify some people as “religious nationalists.” Scholars do not fully agree on how to define religious nationalism, and the challenge is even more complicated when one tries to study the concept in multiple countries – and for multiple religious groups – concurrently. For example, scholars who measure Christian nationalism in the U.S. may consider whether the government should allow prayer in public schools, while those measuring Hindu nationalism in India may consider whether the government should regulate the protection of cows, which are sacred to many Hindus. For the purpose of making cross-national comparisons, we focused on two concepts in our definition of religious nationalism: How important people think identifying with the country’s historically predominant religion is for belonging – e.g., for being “truly” part of the country’s nationality The role people want religion to play in their country’s leader and laws We measured these two concepts among followers of each country’s historically predominant religion using four questions. For more information on how we designed our religious nationalism index and assessed its statistical reliability, go to the methodology. This analysis was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation (grant 63095). This publication does not necessarily reflect the views of the John Templeton Foundation. Here are the questions used for this report, along with responses, and the survey methodology. In many countries, religion and politics are deeply intertwined. The belief that a country’s historically predominant religion should be a central part of its national identity and drive policymaking is sometimes described as “religious nationalism.” A wide range of movements have been described as religious nationalism, including in India, where Prime Minister Narendra Modi has campaigned and governed on the idea that Hindu faith and culture should shape government policies; and in Israel, where Prime Minister Benjamin Netanyahu is backed by a coalition that includes ultra-Orthodox and national religious parties. But there is no universally accepted definition of religious nationalism, leaving lots of room for debate over who is, say, a Christian nationalist or a Hindu nationalist. This has made it difficult to assess how common such views are around the world. To help fill this gap, Pew Research Center set out to measure – in an impartial, consistent way – what share of people in different countries view the dominant religious tradition as central to their national identity, want their leaders to share their religious beliefs, and want religious teachings to guide their laws. We asked four key questions in nationally representative surveys of nearly 55,000 people, conducted from January to May 2024 in three dozen countries: How important is belonging to the historically predominant religion to being truly part of your national identity? (For example, how important is being a Muslim to being truly Indonesian, or being a Christian to being truly American?) How important is it to you for your national leader to share your religious beliefs? How much influence do you think the historically predominant religion’s sacred text should have on the laws of your country? (For example, how much influence should the Quran have on the laws of Turkey, or should the Bible have on the laws of Italy?) When the sacred text conflicts with the will of the people, which should have more influence on the laws of your country? (This

By Theresa Schliep ( January 31, 2025, 9:49 PM EST) — Perplexity AI Inc. is facing a trademark infringement suit in California federal court from a software company that argues that the artificial intelligence-powered search engine “believes it is entitled to trample on the rights of a proverbial ‘little guy’ without consequence.”… Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source