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How Blockchain Technology Is Improving Black Friday Shopping Security
October 30, 2024 in Business Services
The holiday season is just around the corner, bringing with it Black Friday, the year’s largest shopping event. While this period offers exciting opportunities for both retailers and shoppers, it also brings increased risks. Shoppers eagerly anticipate discounts, but hackers and fraudsters see the surge in online and in-store traffic as an opportunity to strike. Blockchain technology, initially developed for cryptocurrency, is now becoming a powerful tool for improving security in various sectors, including retail. With retailers and payment processors turning to blockchain, they aim to protect customer information and quickly detect and block fraudulent activities during Black Friday sales.
When the volume of transactions skyrockets, cybercriminals target both online platforms and physical stores. In 2023, Adobe reported that consumers spent $9.2 billion on Black Friday, and the figure is expected to rise this year. As online shopping continues to grow faster than in-store purchases, the need for a more secure framework becomes even more urgent, especially to counter threats like payment fraud, phishing, and identity theft.
How Blockchain Ensures Secure Shopping on Black Friday
Blockchain technology offers a decentralized, tamper-resistant ledger that transforms how transactions are handled. Unlike traditional databases with centralized servers, blockchain records data across multiple nodes, making it much harder for hackers to manipulate. This innovation is already strengthening security for Black Friday shopping in several ways:
Decentralized Data Protection
Traditional online retailers store customer data in centralized systems, which are vulnerable to breaches. In contrast, blockchain distributes transaction records across a network of nodes. This decentralized model ensures that even if one node is compromised, customer information remains secure across the network, reducing the risk of large-scale data theft.
Enhanced Payment Security
Blockchain-based payment platforms provide an extra layer of security through mechanisms like “proof of work” or “proof of stake,” which authenticate transactions and prevent double-spending. These systems make it extremely difficult for unauthorized parties to alter transactions or engage in fraudulent activities. Retailers and financial institutions are increasingly adopting blockchain to ensure faster, more secure payments that cannot be reversed fraudulently.
Transparent Records to Prevent Fraud
Blockchain’s transparent structure makes it a powerful tool against fraud. Each transaction is verified and recorded by multiple participants on the network, ensuring that tampering attempts are quickly detected. This is especially useful against chargeback fraud, where a customer disputes a legitimate purchase to claim a refund. With blockchain, every transaction’s full history is readily available, making it easy to spot and address fraudulent behavior.
To Know More, Read Full Article @ https://ai-techpark.com/black-friday-shopping-security/
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Building Resilient Cyber Defenses: Insurance Strategies for Small Businesses
October 28, 2024 in Business Services
Cybersecurity breaches are increasingly common among small and medium businesses (SMBs), making them ideal targets for cybercriminals. Due to limited budgets, lack of expertise, and the misconception of being “too small to be targeted,” many SMBs operate with minimal cybersecurity defenses. Unfortunately, this vulnerability is exactly what cyber attackers rely on, exploiting weak defenses through relentless attacks and sophisticated phishing campaigns. In India alone, ransomware attacks on websites surged by 261% this year, with insurance companies often left covering the damage.
This raises an important question: How are organizations that specialize in risk management being blindsided by cyber threats? The answer lies in inadequate security practices. Many businesses lack critical defenses such as multi-factor authentication (MFA), phishing-resistant employee training, reliable backups, and endpoint detection and response (EDR) systems. Additionally, the rapid shift of SMBs to cloud platforms introduces complex configurations that exceed the technical capacity of many small businesses.
Insurance Companies and Cyber Risk Management
Recognizing the growing risks, insurers are tightening their standards for cyber insurance policies. Companies now need to meet stricter requirements, such as multi-step authentication (e.g., verification codes via email or phone), routine security scans, and offline data backups, to qualify for coverage.
However, insurers face challenges in evaluating cyber risk. Without access to comprehensive insights from businesses’ security systems, insurance companies struggle to assess the true level of exposure. This reactive, “outside-looking-in” approach slows incident response efforts, as forensic teams must first reconstruct pre-breach system conditions. At the same time, businesses without a solid cybersecurity framework increase uncertainty for insurers, forcing them to reconsider how they assess and manage cyber risk.
The integration of cybersecurity and insurance efforts creates a win-win-win scenario for all stakeholders. Insurance companies benefit from fewer claims and improved financial performance, SMBs enjoy better protection and more favorable policy terms, and end-users gain enhanced data security.
By fostering partnerships between insurers and cybersecurity providers, the industry can shift from reactive risk management to proactive prevention. This collaborative approach will not only help mitigate the growing ransomware threat but also create a more resilient digital environment for businesses and consumers alike.
To Know More, Read Full Article @ https://ai-techpark.com/role-of-insurance-in-cyber-defense-enhancement/
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Pioneering Healthcare with Digital Biomarkers and AI/ML Technologies
October 28, 2024 in Business Services
The healthcare sector is undergoing rapid transformation, driven by a deeper understanding of patient needs and the adoption of cutting-edge technologies. At the forefront of this evolution are digital biomarkers and AI/ML, which serve as critical enablers of next-generation healthcare solutions. This paper examines the historical development, impact, and future trajectory of these technologies to explore how they are reshaping the modern healthcare ecosystem.
The Evolution of Biomarkers in Healthcare
Traditionally, biomarkers have played a crucial role in diagnosing diseases, predicting their progression, and evaluating treatment responses. Conventional biomarkers, such as blood tests and imaging, paved the way for the digital evolution of healthcare.
Digital biomarkers utilize technology for real-time data collection and analysis, offering more dynamic insights into health trends. With the widespread adoption of wearable devices and mobile health apps, digital biomarkers are becoming essential tools in patient care, revolutionizing how health professionals monitor and manage individual well-being.
Understanding Digital Biomarkers: A Comprehensive Approach
Definition and Classification
Digital biomarkers refer to objective, measurable physiological or behavioral data captured through digital tools. These markers can range from simple metrics, like daily step counts, to more complex indicators, such as heart rate variability measured through wearables. Unlike traditional biomarkers, which rely on lab tests, digital biomarkers offer real-time insights into a patient’s health status.
Types of Digital Biomarkers
Wearable Device Biomarkers: Smartwatches and fitness trackers measure activity levels, sleep quality, and heart rate.
Electronic Health Records (EHRs): Incorporating digital biomarkers into EHRs enables long-term tracking, improving care coordination and outcomes.
Mobile Health Applications: These apps monitor health metrics and provide personalized recommendations, fostering greater patient engagement.
Integrating Digital and Traditional Biomarkers
The integration of digital biomarkers with traditional ones, such as genetic data, offers a comprehensive view of patient health. For instance, combining genomic insights with wearable data allows healthcare providers to identify risk factors early, enabling timely interventions and personalized care.
To Know More, Read Full Article @ https://ai-techpark.com/digital-biomarkers-ai-ml/
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Transforming Data Management through Data Fabric Architecture
October 25, 2024 in Business Services
Data has always been the backbone of business operations, highlighting the significance of data and analytics as essential business functions. However, a lack of strategic decision-making often hampers these functions. This challenge has paved the way for new technologies like data fabric and data mesh, which enhance data reuse, streamline integration services, and optimize data pipelines. These innovations allow businesses to deliver integrated data more efficiently.
Data fabric can further combine with data management, integration, and core services across multiple technologies and deployments.
This article explores the importance of data fabric architecture in today’s business landscape and outlines key principles that data and analytics (D&A) leaders need to consider when building modern data management practices.
The Evolution of Modern Data Fabric Architecture
With increasing complexities in data ecosystems, agile data management has become a top priority for IT organizations. D&A leaders must shift from traditional data management methods toward AI-powered data integration solutions to minimize human errors and reduce costs.
Data fabric is not merely a blend of old and new technologies; it is a forward-thinking design framework aimed at alleviating human workloads. Emerging technologies such as machine learning (ML), semantic knowledge graphs, deep learning, and metadata management empower D&A leaders to automate repetitive tasks and develop optimized data management systems.
Data fabric offers an agile, unified solution with a metadata-driven architecture that enhances access, integration, and transformation across diverse data sources. It empowers D&A leaders to respond rapidly to business demands while fostering collaboration, data governance, and privacy.
By providing a consistent view of data, a well-designed data fabric improves workflows, centralizes data ecosystems, and promotes data-driven decision-making. This streamlined approach ensures that data engineers and IT professionals can work more efficiently, making the organization’s systems more cohesive and effective.
Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/
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How AI is Driving Sustainability in the IT Industry
October 25, 2024 in Business Services Other
The rise of artificial intelligence (AI) has transformed many sectors across the business landscape, reshaping how organizations operate. However, the convenience of AI introduces environmental challenges, such as increased energy consumption and hardware waste. These unintended consequences call for thoughtful strategies from chief information officers (CIOs), who must balance technological advancements with sustainability goals.
According to a Gartner survey, environmental issues are now a top priority for tech companies, and CIOs are facing pressure from executives, stakeholders, and regulators to implement sustainability initiatives. The convergence of AI and environmental responsibility requires proactive measures that can drive sustainable transformation.
This article offers a framework for adopting green algorithms—energy-efficient AI solutions—to help CIOs build sustainable IT organizations.
A Five-Step Framework for Green Algorithms
Green algorithms are critical for addressing the complexities, costs, and environmental impacts of AI deployment. They offer a sustainable way to execute AI-powered initiatives, from predictive maintenance to natural language processing (NLP) for sentiment analysis. CIOs and project managers must work collaboratively to design structured strategies for deploying these environmentally conscious AI solutions.
Here’s a five-step framework to guide the adoption of green algorithms:
Define Sustainability Goals
Setting clear sustainability objectives serves as a roadmap for AI-driven projects. These goals can focus on waste reduction, carbon neutrality, or socially responsible practices. CIOs can align their goals with the United Nations’ Sustainable Development Goals (SDGs) to ensure meaningful impact. Project management platforms, such as Asana, provide built-in sustainability metrics to align objectives with the organization’s vision.
Centralize Data
Building a robust data infrastructure is essential for ensuring the success of green algorithms. Real-time, comprehensive data allows for well-informed decisions. CIOs and project managers can leverage data lakes or management platforms to monitor metrics like energy consumption and emissions efficiently.
Customize Algorithms
Generic algorithms may not address specific sustainability challenges effectively, so customization is key. Pre-built AI models, such as those available through TensorFlow and Scikit-Learn, can be adapted to meet the organization’s unique sustainability KPIs.
Know More, Read Full Article @ https://ai-techpark.com/the-convergence-of-ai-and-sustainability-in-the-it-industry/
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CEO of QueryPal, Dev Nag – AITech Interview
October 23, 2024 in Business Services
Dev, can you start by sharing the journey that led you to establish QueryPal and what inspired you to focus on transforming customer support through AI-powered ticket automation?
The journey to QueryPal began with my experiences at Google and PayPal, where I saw firsthand the challenges of scaling customer support. I realized that while AI was transforming many industries, customer support remained largely unchanged. The inspiration came from seeing how Large Language Models (LLMs) could understand and generate human-like text. I knew we could leverage this technology to revolutionize customer support, making it more efficient and effective. QueryPal was born from the vision of creating an AI system that could understand customer inquiries at a deep level and provide accurate, helpful responses at scale.
How has AI enhanced the accuracy of customer support responses at QueryPal, and what role does it play in improving response times and customer satisfaction?
AI has dramatically enhanced the accuracy of customer support responses at QueryPal. Our advanced natural language understanding allows us to comprehend the nuances of customer inquiries, including context and intent. This leads to more precise and relevant responses. Moreover, our AI can access and synthesize information from vast knowledge bases in seconds, providing comprehensive answers faster than any human could. This improvement in both accuracy and speed has led to significant increases in customer satisfaction scores for our clients. We’re also in the early stages of researching Causal AI, which could enable our system to understand cause-and-effect relationships in customer issues, potentially allowing it to reason about novel situations it hasn’t explicitly seen in training data.
Personalized customer support is a significant advancement in customer service. Can you explain how AI-powered systems at QueryPal tailor responses to individual customer inquiries?
Personalization in QueryPal’s AI system operates on multiple levels. First, it considers the customer’s context, including channel metadata. Second, it analyzes the specific language and tone of the current inquiry. Finally, it takes into account how past responses for similar questions have satisfied customers. By combining these factors, our AI can tailor responses that not only answer the specific question but also address potential underlying concerns, use appropriate language and tone, and even anticipate follow-up questions. Personalization in QueryPal’s AI system is already advanced, but we’re excited about the potential of Agentic AI. We’re in the process of integrating this technology, which could allow our system to handle complex, multi-step tasks with minimal human specification. In the future, it might be able to understand the broader context of a customer’s journey, anticipate needs, and even take proactive steps to resolve issues before they escalate.
To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-dev-nag/
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The Risk and Remedy: AI’s Role in Managing Uncertainty
October 23, 2024 in Business Services
Artificial intelligence (AI) is a relatively new field that has rapidly evolved into a major influence on the strategic direction of organizations. Its significance extends far beyond automation, enhancing complex decision-making processes. AI is both a risk and a tool for managing risk—a paradox that organizations must confront as they navigate the landscape of 2024 and beyond.
AI as a Catalyst for Transformation
While AI is often associated with task automation, it also plays a critical role in improving decision-making. AI empowers change across various domains, from social to informational, by automating time-consuming processes and driving efficiency. Additionally, AI offers deeper insights to management teams than ever before.
In finance, for example, AI models outperform traditional methods by evaluating a broader set of factors to assess credit risk, predict market trends, detect fraud, and identify optimal investments. Similarly, in healthcare, AI enables early diagnosis and increases diagnostic accuracy, transforming how medical treatments are managed. These examples demonstrate that AI not only mitigates risks but also reshapes operational behavior, opening new avenues for efficiency and effectiveness.
Machine Learning’s Role in Enterprise Risk Management
Machine learning—one of the most crucial AI fields—plays a vital role in Enterprise Risk Management (ERM). By learning from data and detecting patterns beyond human observation, machine learning is particularly useful in industries like cybersecurity, where threats are constantly evolving. AI systems also monitor network activities in real-time, providing alerts for suspicious events to prevent breaches.
According to Gartner’s 2024 report, companies leveraging AI-based risk management tools saw a 30% reduction in data breach incidents. This statistic emphasizes AI’s ability to prevent risk events. Moreover, as data protection laws become stricter, AI helps organizations maintain compliance through precise monitoring and reporting mechanisms.
The dual role of AI as both a risk and a risk management tool defines the modern business landscape. Organizations that recognize AI’s strategic value and incorporate it into their planning will be well-positioned to thrive. The improvements in decision-making, efficiency, and risk forecasting that AI offers are too significant to ignore.
However, these opportunities come with responsibility. Companies must adopt ethical AI practices and ensure robust data protection to avoid negative societal impacts. Failure to address these issues could have serious consequences, not only for businesses but also for society as a whole.
Ultimately, the question is not whether to adopt AI but how to implement it sustainably and responsibly. Leaders with a vision for ethical AI usage will not only mitigate risks but also unlock new opportunities previously beyond reach. As business environments continue to evolve rapidly in 2024 and beyond, organizations that fail to adapt will fall behind. Integrating AI as both a tool and a mandate is essential for any innovative organization looking to succeed.
To Know More, Read Full Article @ https://ai-techpark.com/ai-is-both-a-risk-and-a-tool/
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Graph RAG Takes the Lead: Exploring Its Structure and Advantages
October 21, 2024 in Business Services
Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.
RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?
What is Graph RAG and What’s All the Fuss About?
According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.
Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.
KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.
To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/
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Seven Data Loss Prevention Techniques of 2024
October 21, 2024 in Business Services Other
Data breaches and cyber threats are becoming increasingly common in this digital era, and protecting valuable information is the top priority for data-driven organizations. To curb the constant issues of data being compromised, lost, and misused, a Data Protection Officer (DPO) and their teams can implement a data loss prevention (DLP) strategy and tools that will continuously monitor and analyze data to identify potential violations of security policies and stop them from evolving.
In this article, we will take a closer look at the seven steps of DLP strategies and tools that will help in enhancing the security of your IT structures.
Seven-Step Framework in Deploying DLP Strategy
If any business is handling sensitive data and operating in a regulated environment or suffers from repeated cybersecurity threats, it’s time that needs to add DLP strategies.
Proofpoint’s 2024 data loss landscape report indicates that 84.7% of enterprises have encountered data loss, with an average of 15 incidents per organization per year. This implies the importance of appropriately implementing DLP strategies.
Therefore, without any further ado, let’s understand the seven-step strategic framework of DLP:
Execute Regular Security Audits
Regular security audits are a crucial component of any thorough DLP strategy. Stringent security audits focus on renewing security policies, procedures, and controls, allowing security auditors to identify any potential vulnerabilities and recommend remediation actions to address them. The audit is not limited to data prevention but also to safeguarding software, hardware, and network infrastructure.
However, the regular auditing process can be a tedious task; therefore, security auditors can automate the process by using tools such as Palo Alto Networks, Tufin, and ManageEngine, which will analyze the dataset or network and identify compliance and provide detailed reports with patch recommendations.
Once vulnerabilities have been identified, you can take the necessary actions to address them before attackers can manipulate them to access sensitive data; this action includes patching software, updating hardware, implementing new security controls, or revising security policies and procedures.
Educate Your Employees
Many of the worst threats to an organization start with minute human error, and neglecting the threat gradually starts the real damage, such as ransomware attacks or misinformation. According to Proofpoint’s Insider Threats Global Report, the majority of insider threats, i.e., estimated around 56%, resulted from negligent insiders. Therefore, educating employees is a vital task for creating an effective DLP strategy.
Educating employees about data security and data privacy organizations can reduce the risk of data loss or theft caused by human error. This also includes training on how to pinpoint phishing emails, methods to avoid clicking on suspicious links or attachments, using secure passwords, and implementing MFA in every file.
To Know More, Read Full Article @ https://ai-techpark.com/data-loss-prevention-techniques-of-2024/
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