Tag: Data Security

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/

Related Articles -

Intersection of AI And IoT

Future of QA Engineering

Trending Category - IOT Wearables & Devices

Companies are dealing with overwhelming amounts of data, and this data must be governed, compliant, and secure, especially when working in the financial, healthcare, and insurance sectors. As the complexity of data environments increases, traditional data governance approaches largely fail to address these challenges adequately and lead to the emergence of what many researchers refer to as Data Governance 2.0. undefined Laying its foundation is the metadata-driven data fabric, which represents a highly transformative approach to data management and governance, compliance, and security.

Expanding on the concept of data fabric architecture and elements, this article focuses specifically on the use of metadata layers to improve governance and compliance for businesses operating in highly regulated environments.

In this blog, we will also discuss the concepts, opportunities, and risks of constructing a metadata-driven data fabric to enhance compliance and security.

The Evolution of Data Governance: From 1.0 to 2.0

Data Governance 1.0: Legacy Governance Models

The conventional view of the data governance process was mainly concerned with data adequacy, control, compliance, and the ability to store data securely in isolated databases. This was primarily a rule-governed and manual approach. The governance policies we had were far from dynamic and flexible to adapt to the evolving needs of the current organizations.

Legacy systems in Data Governance 1.0 face several limitations:

Manual processes: Some of the measures of security are checked manually, and this leads to slow processes and errors because it is done by human beings.

Siloed data: Data resides in multiple systems and silos, which causes issues with governance alignment.

Static policies: Governance rules do not adapt to the emergence of new data scenarios and the constantly evolving compliance requirements.

Why Data Governance 2.0?

The data environment has changed, and it is now imperative for organisations to sort data through hybrid and multi-cloud solutions, and address increasing concerns of compliance and security. This phenomenon is has therefore resulted to what is now known as Data Governance 2. 0, a governance model designed for the modern data ecosystem, characterized by:

Real-time governance: Managing a multilayered set of governance policies for both cloud and on-premises & hybrid solutions.

Data integration: Integration management of distributed data and assets with out leaving their original location.

Proactive compliance: Engaging metadata and AI to enforce compliance in a dynamic manner.

To Know More, Read Full Article @ https://ai-techpark.com/how-metadata-driven-data-fabric-ensures-compliance-and-security/

Related Articles -

Transforming Business Intelligence Through AI

Introduction of Data Lakehouse Architecture

Trending Category - IOT Smart Cloud

In an era where data is heralded as the new oil, there’s an inconvenient truth that many organizations are just beginning to confront: it is therefore important to realize that not all data is equal. With the increasing digitalization of the economy and an imperative to increasingly rely on data in products and services, the focus has been traditionally on the sheer amount of data that can be gathered to feed analytics, provide clients with personalized experiences, and inform strategic actions. However, without this policy to embrace data quality and data lineage, even the strenuous data collection would result in disastrous results.

Let us take an example of a general merchandising retailer chain that, to sustain and overcome its competitors, started a large-scale acquisition-based customer loyalty campaign with help of their gigantic data warehouse. High expectations of the initiative and great investment to make it work reached a deadlock when the issue was revealed: the data behind the plan was unreliable. The promotions of the retailer were wrong since the wrong customers were being targeted, and this eroded the trust of the customers.

This is not an unusual case. In fact, all these issues will sound very familiar in most organizations, yet often with no realization regarding potential hidden costs in the form of poor data quality and a lack of understanding in terms of data lineage. If data is to become a true strategic resource, then organizations have got to go beyond what appears to be mere numbers and down traceability of data. Only then can they establish the much-needed trust in today’s world to answer the diversified needs of the customers and the regulating bodies.

The Hidden Truth About Data: It’s Only as Good as Its Quality

The question is: Who would not want to work with data? The truth is that data is full of errors, inconsistencies, and inaccuracies. Data quality is an issue that ultimately touches upon the decision-making process, organizational compliance, and customer trust.  Let’s consider the following:

For instance, consider a marketing team working on creating a marketing campaign that was based on customer information that might have been entered incorrectly or not updated for several years. The result? Incorrect targeting, resource expenditure, and perhaps the antagonizing of clients. It therefore underlines the significance of sound data—a factor that is relevant both in making decisions and in customer relations.

Key Elements of Data Quality:

Accuracy: The data used should be accurate and depict the true worth and facts.

Completeness: All necessary data should be included without any gaps, i.e., all important data must be there with no breaks in between.

Consistency: Data should not only be uniform with all the systems and reports of the company, but also the format used should be uniform.

Timeliness: Data should be in real-time, and this data should be accessible whenever it is required.

Validity: The attributes should be of the right format and within the right range.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-and-data-lineage-elevate-trust-and-reliability/

Related Articles -

Intelligent Applications Are No option

Intersection of Quantum Computing and Drug Discovery

Trending Category - Clinical Intelligence/Clinical Efficiency

AI-powered e-commerce platforms scale SMB operations by providing sophisticated pricing analysis and inventory management. Encryption and blockchain applications significantly mitigate concerns about data security and privacy by enhancing data protection and ensuring the integrity and confidentiality of information.

A 2024 survey of 530 small and medium-sized businesses (SMBs) reveals that AI adoption remains modest, with only 39% leveraging this technology. Content creation seems to be the main use case, with 58% of these businesses leveraging AI to support content marketing and 49% to write social media prompts.

Despite reported satisfaction with AI’s time and cost-saving benefits, the predominant use of ChatGPT or Google Gemini mentioned in the survey suggests that these SMBs have been barely scratching the surface of AI’s full potential. Indeed, AI offers far more advanced capabilities, namely pricing analysis and inventory management. Businesses willing to embrace these tools stand to gain an immense first-mover advantage.

However, privacy and security concerns raised by many SMBs regarding deeper AI integration merit attention. The counterargument suggests that the e-commerce platforms offering smart pricing and inventory management solutions would also provide encryption and blockchain applications to mitigate risks.

Regressions and trees: AI under the hood

Every SMB knows that setting optimal product or service prices and effectively managing inventory are crucial for growth. Price too low to beat competitors, and profits suffer. Over-order raw materials, and capital gets tied up unnecessarily. But what some businesses fail to realize is that AI-powered e-commerce platforms can perform all these tasks in real time without the risks associated with human error.

At the center is machine learning, which iteratively refines algorithms and statistical models based on input data to determine optimal prices and forecast inventory demand. The types of machine learning models employed vary across industries, but two stand out in the context of pricing and inventory management.

Regression analysis has been the gold standard in determining prices. This method involves predicting the relationship between the combined effects of multiple explanatory variables and an outcome within a multidimensional space. It achieves this by plotting a “best-fit” hyperplane through the data points in a way that minimizes the differences between the actual and predicted values. In the context of pricing, the model may consider how factors like region, market conditions, seasonality, and demand collectively impact the historical sales data of a given product or service. The resulting best-fit hyperplane would denote the most precise price point for every single permutation or change in the predictors.

To Know More, Read Full Article @ https://ai-techpark.com/ai-integration-and-data-security-in-e-commerce/

Related Articles -

CIOs to Enhance the Customer Experience

Future of QA Engineering

Trending Category -  IOT Smart Cloud

Greetings Robert, Could you please share with us your professional journey and how you came to your current role as Chief Innovator of Monjur?

Thank you for having me. My professional journey has been a combination of law and technology. I started my career as an intellectual property attorney, primarily dealing with software licensing and IT transactions and disputes.  During this time, I noticed inefficiencies in the way we managed legal processes, particularly in customer contracting solutions. This sparked my interest in legal tech. I pursued further studies in AI and machine learning, and eventually transitioned into roles that allowed me to blend my legal expertise with technological innovation. We founded Monjur to redefine legal services.  I am responsible for overseeing our innovation strategy, and today, as Chief Innovator, I work on developing and implementing cutting-edge AI solutions that enhance our legal services.

How has Monjur adopted AI for streamlined case research and analysis, and what impact has it had on your operations?

Monjur has implemented AI in various facets of our legal operations. For case research and analysis, we’ve integrated natural language processing (NLP) models that rapidly sift through vast legal databases to identify relevant case law, statutes, and legal precedents. This has significantly reduced the time our legal professionals spend on research while ensuring that they receive comprehensive and accurate information. The impact has been tremendous, allowing us to provide quicker and more informed legal opinions to our clients. Moreover, AI has improved the accuracy of our legal analyses by flagging critical nuances and trends that might otherwise be overlooked.

Integrating technology for secure document management and transactions is crucial in today’s digital landscape. Can you elaborate on Monjur’s approach to this and any challenges you’ve encountered?

At Monjur, we prioritize secure document management and transactions by leveraging encrypted cloud platforms. Our document management system utilizes multi-factor authentication and end-to-end encryption to protect client data. However, implementing these technologies hasn’t been without challenges. Ensuring compliance with varying data privacy regulations across jurisdictions required us to customize our systems extensively. Additionally, onboarding clients to these new systems involved change management and extensive training to address their concerns regarding security and usability.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-robert-scott/

Related Articles -

Role of Algorithm Auditors in Algorithm Detection

AI-powered Mental Health workplace Strategies

Trending Category - Mobile Fitness/Health Apps/ Fitness wearables

Explore how Lakehouse Architecture has evolved, merging the best of data lakes and warehouses into one game-changing solution!

It must be noted that the existence of lakehouse architectures has brought some substantial changes in the data architecture landscape. In this evolution process, organizations are still struggling on how to handle complex and diverse data management, to which the answer is the lakehouse model. Lakehouses can be viewed as a better integration of data lakes and data warehouses to provide improved data management systems. This blog post delves into the further evolution of lakehouse architecture and explains its main concepts, recent developments, and transformation of today’s data management.

Historical context and core principles

Before understanding the progression of architectural styles of the lakehouse, it is crucial to look at the basic components of the concept. Earlier, companies used data warehouses for structured data processing and analysis. Data warehouses offered strong and well-developed SQLQuery, transactional, and near real-time query processing for complicated queries. However, it became a drawback when attempting to work with different and more complex types of data that are incompatible with the one-dimensional, rigid structure of a regular list.

On the other hand, data lakes are a concept that appeared as a result of these limitations, allowing managing raw and unstructured information in a big data environment. Data lakes allowed for accepting and storing data in various formats from different sources; however, they did not offer the usage of atomicity, consistency, isolation, and durability (ACID) transactions and performance improvements typical for data warehouses.

Consequently, the architecture of the lakehouse strived to combine these two paradigms into an integrated system that would represent the advantages of both. To summarize, lakehouses are the next step in data organization with their combination of data lake scalability and flexibility and data warehouse performance and control.

Key Advancements in Lakehouse Architecture

Unified Storage and Compute Layer:

The lakehouse architecture brings in a simplified storage and compute layer in their architectural design, thus minimizing the level of complexity. This layer enables organizations to archive data while fulfilling many types of data processing duties, from batch to real-time. The decoupling of compute and storage resources is a great improvement in regards to scale efficiency.

The concept of lakehouse architecture is one of the most significant steps toward improving data handling processes. Lakehouses, on the other hand, offer a combined approach to data lakes and data warehouses that improves scalability, performance, and governance. When employing this innovative architecture, organizations prepare themselves to get the most out of the gathered data, to foster analysis and creativity in a world headed towards a higher dependency on data and information.

To Know More, Read Full Article @ https://ai-techpark.com/the-evolution-of-lakehouse-architecture/

Related Articles -

AI-Powered Wearables in Healthcare sector

Top Five Data Governance Tools for 2024

Trending Category - Mobile Fitness/Health Apps/ Fitness wearables

The digitized world relies heavily on computer-driven processes, and the demand for innovative software products and solutions is all-time high. Organizations and institutions are constantly reshaping their digital structure by investing in software tools and programs to enhance their productivity, streamline business operations, and ensure seamless communication. Therefore, the need to understand the countless opportunities this field can provide will be a major career for software developers. However, to add more credibility to the profession, software engineer certifications are needed that will help you get skilled, grow your knowledge, attain a higher salary, and advance your career.

In today’s exclusive AITech Park, we will explore the top five best software engineering certifications of 2024 that software developers can pursue to gain knowledge about the current trends in software development and also brush up their skills.

Amazon Web Services Certified Developer Associate

The first software engineering certification course on our list is from Amazon Web Services (AWS). The AWS Certified Developer Associate (AWS CDA) certification is used to teach software engineers how to create and deploy cloud-based web apps. Candidates who enroll in this program are required to know how to write applications using an API, AWS, command-line interface (CLI), and software development kits (SDK). The software engineers need to have at least two years of experience working with apps built on AWS before they take this course.

Certified Software Development Professional (CSDP)

The CSDP offered by the famous IEEE Computer Society focuses on upskilling experienced software developers with new technologies. The course validates a candidate’s proficiency in software engineering principles and practices that surround the entire software development lifecycle. Through this course, candidates need to display their knowledge of software requirements, configuration management, engineering management, engineering processes, and tools. The CSDP aims for professionals with a minimum of two years of experience and a postgraduate degree to get this certification.

Microsoft Certified: Azure Solutions Architect Expert (ASAE)

The ASAE certification validates software engineers’s expertise in designing, testing, and building cloud applications and services for the Microsoft Azure website. This course is customized for candidates with at least one year of experience as a software engineer, as this certification requires expertness in Azure SDKs, data storage options, data connections, APIs, app authentication and authorization, debugging, performance tuning, and monitoring.

Choosing the right certification course as a software developer is a strategic step that can signify enhancing your skills and market values. Therefore, before selecting any certification course, you need to think about a professional development plan that will guide you in the right direction.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-software-engineering-certification-programs-of-2024/ 

Related Articles -

Future of QA Engineering

Top 5 Data Science Certifications

Trending Category - Clinical Intelligence/Clinical Efficiency

In an era where technology seamlessly integrates into every facet of our lives, the vision of the future of transportation, once dreamt in the mid-20th century, is becoming a reality. Landscapes are evolving, with the promise of enhanced connectivity, ease of travel, and the development of sprawling metropolises aimed at fostering a more harmonised society. This transformative period in transportation is not just about sleek designs, improved fuel efficiency, or advanced safety systems; it is about the underlying digital revolution that has turned vehicles from mechanical wonders into sophisticated, software-driven entities.

The marvel of modern vehicles extends far beyond their aesthetic appeal or physical innovations.  Today, vehicles are commonly referred to as data centres on wheels, equipped with digital interfaces that constantly communicate with manufacturers, receive over-the-air (OTA) software updates, and integrate advanced safety features, like LIDAR systems, to navigate complex environments. The once direct mechanical connection between the accelerator and the engine has been replaced by a digital command centre, where a simple press of a pedal is translated into a series of computations that ensure optimal performance and safety.

However, this digital evolution brings with it a looming shadow of vulnerability. The very systems that make modern vehicles a marvel of technology also exposes them to a myriad of cybersecurity threats. In recent years, the automotive industry has witnessed a concerning trend: an increase in cyber-attacks targeting not just the vehicles but the entire ecosystem surrounding their development, production, and maintenance. The 2021 attack on KIA Motors by the DopplePaymer group is a stark reminder of the potential consequences of inadequate cybersecurity measures. While no direct harm to drivers was reported, the incident underscored the risks of operational downtime, revenue loss, and eroding customer trust.

The question then becomes, what lies ahead? The potential targets for cyber-attacks are not limited to consumer vehicles but extend to government and municipal mass transit systems. The stakes are exponentially higher, with the threat landscape encompassing espionage, state-sponsored activities, and the emerging menace of AI-driven cyber threats. The complexity of modern vehicles, often containing upwards of 100 endpoints, including infotainment systems that store personal data, demands a cybersecurity strategy that transcends traditional approaches and international borders.

Protecting this data requires a proactive approach, one that involves hunting for threats, deceiving potential attackers, and adopting a mindset that places vehicle cybersecurity on par with data security across the rest of the organisation. It’s about creating a resilient shield around the digital and physical aspects of transportation, ensuring that innovation continues to drive us forward, not backward into an age of vulnerability.

To Know More, Read Full Article @ https://ai-techpark.com/future-ready-transportation-security/ 

Related Articles -

Smart Cities With Digital Twins

Decoding the Exponential Rise of Deepfake Technology

Trending Category - aitech chatbots

2023 was a terrific year in the IT industry, but 2024 is set to bring some exciting and groundbreaking developments that will help IT professionals and data scientists develop innovative software and tools to strive in the competitive landscape.

The most recent technological advancement in the data landscape is quite commendable. In 2024, IT enterprises will be heavily impacted, as data is the new oil that can transform any business and reshape the traditional process of analyzing, visualizing, and making data-driven decisions.

As IT enterprises grapple with the data deluge, they often find themselves at an intersection of technological innovation, ethical considerations, and the need for actionable solutions.

In today’s exclusive AI Tech Park article, we will focus on gearing up IT professionals and data scientists to understand the data trends they can expect in 2024.

The Era of the Data Renaissance

The phrase “data is the new oil” was stated in 2006 by British data scientist Clive Humby. The one big difference between data and oil is that oil is a nonrenewable energy, and data can be renewed and reused in an infinite number of ways.

Three decades ago, one of the main challenges that IT enterprises faced was the scarcity of data. However, with time, the main challenge for most IT businesses was having a plethora of data.

With such a volume of data, enterprises struggle with how to use the data, where to implement it, when they need it, and most importantly, how to store it. The traditional database management systems (DMS) failed to tackle the new data sets, which made data professionals realize the importance of cloud storage, which is efficient in handling numerous types of data and quite cost-efficient compared to DMS.

As we stand at the crossroads of a data renaissance, the year 2024 heralds an important role in the data analytic landscape, where data analytics is no longer a tool for data-driven decision-making but a driving force to push greater efficiency, innovation, real-time data insights, responsible AI, reinforce security, and more.

However, IT professionals and data scientists need to address the challenges and considerations of imposing data privacy, skill development, and ethical dilemmas to stay compliant with this evolving regulatory landscape.

Data Democratization

Data democratization has been a growing trend for the past few years, but the increased usage of AI and machine learning (ML) tools has rekindled a new horizon for this trend. With data democratization, every employee in an IT organization will have access to the data to make data-driven decisions for a seamless business process. However, to get full access to data, IT leaders need to provide in-house training on data literacy to familiarize them with the principles and techniques of working with data.

To Know More, Read Full Article @ https://ai-techpark.com/top-4-data-trends-it-professionals-need-in-2024/ 

Read Related Articles:

Blockchain, AI, and Quantum Computing

Ethics in the Era of Generative AI

Over the last few decades, innovative technologies have changed the future of business dynamics. Therefore, every industry uses the latest technologies to move forward, and CIOs are responsible for driving such technological changes within organizations. According to Globe Research, it has been witnessed that more than 27.7% of digital transformation for better customer experience (CX) initiates are sponsored or owned by CIOs, which indicates the rapid change in customer expectations.

For a better understanding and deployment of innovative digital transformation, CIOs need to work closely with other C-suites and department managers to enhance the customer experience and gain a competitive advantage in this digitized world.

Innovate the Customer Journey

With the recent development of technologies, companies are accelerating their business to attract innovative services and retain customers looking for a better-personalized experience, fast and secure applications, or software. Further, the new applications enable dynamic online ordering operations, such as same-day delivery or pickup, which helps customers interact with the organizations instantly.

For a better understanding of the applications or software, CIOs and business leaders can come up with an entire customer journey strategy involving the front end and the back end, making sure the circle is completely connected. Let’s consider the example of Zara, a multinational fashion company that announced that they are closing their 1200 stores across the USA and will start investing in developing an integrated omnichannel journey that will cost more than $3.1 million. This revolutionary change will help e-commerce, the supply chain industry, software development companies, and inventory management companies take innovative steps and make proactive approaches to problems and opportunities to identify resolutions.

Digital transformation, especially to upgrade the customer experience, is a journey; therefore, CIOs must be ready to restructure their strategies numerous times so that they fit their business objectives and goals. However, to embrace such change, CIOs are required to collaborate with every business leader and employee in the organization to increase the speed of executing data strategies, improve the customer experience, and conduct continuous reassessment of the change management process.

To Know More, Read Full Article @ https://ai-techpark.com/3-innovative-cio-strategies-for-enhanced-customer-experiences/ 

Read Related Articles:

Ethics in the Era of Generative AI

Importance of AI Ethics