Autore: martechcubejohn

Artificial intelligence has already had a positive effect on several industries, but unfortunately, this popularity and success have caused some wrongdoers to attempt to capitalize on the AI boom in unethical and illegitimate ways. One such practice is known as “AI washing,” and it is arguably one of the biggest threats to the continued growth of AI.

AI washing is most easily understood by comparing it to the similar practice of greenwashing, in which companies misrepresent their products as being more eco-friendly than they actually are. Similarly, AI washing involves making false representations of a product or service’s use of artificial intelligence technology. Through this deceit, businesses are riding the wave of AI hype without offering their customers the benefits.

Understanding AI washing

One of the most common examples of AI washing takes advantage of many consumers’ lack of knowledge about artificial intelligence with misleading product descriptions. For example, a business could claim that traditional algorithms are artificial intelligence, yet because of the similarities between the two technologies, the average consumer might not realize they are being misguided.

Some businesses are guilty of a form of AI washing in which they exaggerate the scale of the capabilities or use of AI as it relates to their business. For example, a company might claim to offer “AI-powered services” when, in reality, it only uses artificial intelligence in ways incidental to its business. Even though these businesses do use AI to some extent, they have still misled the consumer into believing that their use is more extensive than it actually is.

Other businesses may claim to use artificial intelligence without substantially implementing it into their business. Some have claimed to use AI without using it at all, while others claim to use it while it’s still in its early stages of development and has no noticeable effects.

To Know More, Read Full Article @ https://ai-techpark.com/combatting-ai-washing-threat/

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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/

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Mr. Kalif, we’re delighted to have you. Could you please tell us a bit about your professional journey? What inspired you to co-found ReasonLabs?

Before co-founding ReasonLabs, I spent years in the industry within R&D roles, working to develop products and systems that protect people. I joined forces with Andrew Newman to build ReasonLabs on the common belief that every consumer deserves to benefit from enterprise-grade protection. Until that point, the best cybersecurity had always been saved for large companies because companies thought they suffered the more dangerous threat. We knew that to be false – malware doesn’t discriminate between large corporate networks and home users. This led us to create ReasonLabs and embark on the mission of protecting every home worldwide.

For those who might not know, can you give a brief overview of ReasonLabs and how your products cater to today’s cybersecurity needs?

ReasonLabs’ mission is to provide home users with the same level of cyber protection that the world’s largest enterprises have. Malware and cyber attackers do not discriminate between corporations and home networks and everyone should be protected from next-generation threats.

Our flagship product, RAV Endpoint Protection, is the first consumer-focused cybersecurity product featuring Endpoint Detection & Response (EDR) technology. That plus our other products like RAV VPN and Online Security, combine to form a multilayered solution that safeguards home users’ privacy and digital identities.

How has AI changed the cybersecurity landscape, especially for consumers? Can you share some specific examples where AI has made a real difference?

Cyber-attackers leverage AI in all kinds of ways that affect consumers, but none more than with advanced phishing and social engineering attacks. It used to be fairly easy to recognize these threats, but AI has helped take them to new heights. From the security perspective, AI enables us to provide consumers with next-gen security, like our RAV Managed EDR technology. This EDR, with help from AI, helps us evaluate billions of data points and identify attacks against consumers in real-time, 24/7 protection.

Identity theft is a big concern for many people. How does ReasonLabs tackle this issue, and what innovative solutions have you come up with?

Identity theft is a huge problem that can wreak havoc on people’s lives. Providing identity theft defense is a core element in our cybersecurity suite and we do it through protection, detection, and remediation.  Consumers can look to the RAV Online Security browser extension to find these services.

Concerning protection, the extension prevents data leaks and secures against phishing attacks. By working with RAV Endpoint Protection’s EDR technology, the extension can detect next-generation threats including ransomware that can lead to identity theft. Insurance is offered as a means of remediation to ensure there is recourse if something does ultimately happen.

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

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In this digital world, data governance is a critical tool for operating any data-driven organization. As data continues to be favored in business operations, data governance measures are required to keep all data secure, accurate, and updated.

Therefore, to remain on top of the data governance game, the data engineering teams must understand the multiple options that will aid them in developing data governance strategies that perfectly suit their respective businesses.

So to stay ahead of the technological curve, we will look at some of the top data governance trends and forecasts for 2024, which will help you understand some of the valuable insights and aid in navigating the evolving data governance landscape.

Data Monitoring and Data Lineage

Data monitoring and data lineage are interconnected with data governance. Data lineage aids in tracking the flow of data through various systems and modifications that ensure data quality. Even data monitoring assists in enhancing the data quality by demonstrating how data is converted and detecting errors or inconsistencies. This clear understanding of where the data originated will help the data team to make decisions about the data pipeline and also make sure that the data flow is tracked and all the policies are adhered to.

 Data Democratization

As organizations are becoming more data-driven, data democratization is becoming increasingly popular. By implementing data democratization, data is accessible and functional for everyone, even non-technical users. While more employees have the power to access data efficiently, enforcing data governance in this situation is equally important; the data team must make sure that strict access management protocols are adhered to by every employee.

With the increasing complexity of data architecture, data governance comes as a savior for organizations who are looking for a solution to protect their data. By understanding the above trends, the data team can create strong data governance strategies that aid in improving the chances of discovering and applying data to pinpoint unlimited possibilities.

To Know More, Read Full Article @ https://ai-techpark.com/data-governance-and-security-trends-to-follow-in-2024/

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Generative AI has swiftly become popular among marketers and has the potential to grow to a $1.3 trillion industry in the next 10 years. OpenAI’s ChatGPT is just one growth example—rocketing to over 100 million users in just two months of its release.

Many have hailed generative AI as a process-changing tool that can quickly produce swaths of content with minimal human intervention, drastically scaling content production. That’s the claim anyway. But as AI becomes more prevalent, its use in content production opens several questions — does generative AI actually produce quality content? Can it match what human marketers can produce?

With the digital landscape already saturated with content, marketers in the AI era need to fully understand the strengths and weaknesses of current generative tools so they can build (and protect) high-quality connections with their audiences.

Human-generated content beat out AI-generated content in every category.

Though the AI tools had strengths in some areas, no one tool mastered multiple criteria across our tests. When it comes to accuracy, readability, and brand style and tone, the AI tools could not reach the level of quality that professional content writers provided. It also lacked the authenticity of human-written content.

The lesson: Brands and marketers must keep humans at the center of content creation.

Unsurprisingly, AI is not the end-all-be-all solution for creating content that truly connects with human audiences.  

Yes, AI is an efficient and capable tool that marketers can leverage to supercharge specific content tasks. Using AI for tasks such as research, keyword analysis, brainstorming, and headline generation may save content creators money, time, and effort.

Even so, marketers should prioritize humanity in their writing. AI can only give us an aggregate of the staid writing available across the internet. But highly skilled human writers are masters of contextualization, tapping into the subtleties of word choice and tone to customize writing to specific audiences.

As some have pointed out, quantity can never win out over quality.

In the race to adopt AI tools, we must remember what makes content valuable and why it connects with human audiences. The online marketing landscape is becoming increasingly competitive, and brands can’t risk the ability to build trusting connections with consumers in their rush to streamline workflows. Ultimately, humans must remain the central focus as brands invest in unique and authentic content that connects.

To Know More, Read Full Article @ https://ai-techpark.com/ai-vs-human-content-quality/

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The first fundamental of Artificial Intelligence is data, with the Machine Learning models that feed on the continuously growing collections of data of different types. However, as far as it is a very significant source of information, it can be fraught with problems such as privacy limitations, biases, and data scarcity. This is beneficial in removing the mentioned above hurdles to bring synthetic data as a revolutionary solution in the world of AI.

What is Synthetic Data?

Synthetic data can be defined as data that is not acquired through actual occurrences or interactions but rather created fake data. It is specifically intended to mimic the characteristics, behaviors and organizations of actual data without copying them from actual observations. Although there exist a myriad of approaches to generating synthetic data, its generation might use simple rule-based systems or even more complicated methods, such as Machine Learning based on GANs. It is aimed at creating datasets which are as close as possible to real data, yet not causing the problems connected with using actual data.

In addition to being affordable, synthetic data is flexible and can, therefore, be applied at any scale. It enables organizations to produce significant amounts of data for developing or modeling systems or to train artificial intelligence especially when actual data is scarce, expensive or difficult to source. In addition, it is stated that synthetic data can effectively eliminate privacy related issues in fields like health and finance, as it is not based on any real information, thus may be considered as a powerful tool for data-related projects. It also helps increase the model’s ability to handle various situations since the machine learning model encounters many different situations.

Why is Synthetic Data a Game-Changer?

Synthetic data calls for the alteration of traditional methods used in industries to undertake data-driven projects due to the various advantages that the use of synthetic data avails. With an increasing number of big, diverse, and high-quality datasets needed, synthetic data becomes one of the solutions to the real-world data gathering process, which can be costly, time-consuming, or/and unethical.  This artificial data is created in a closed environment and means that data scientists and organisations have the possibility to construct datasets which correspond to their needs.

Synthetic data is an extremely valuable data product for any organization that wants to adapt to the changing landscape of data usage. It not only address practical problems like data unavailability and affordability but also flexibility, conforming to ethical standards, and model resilience. With a rising pace of technology advancements, there is a possibility of synthetic data becoming integral to building better, efficient, and responsible AI & ML models.

To Know More, Read Full Article @ https://ai-techpark.com/synthetic-data-in-machine-learning/

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Hello Dor, we’re delighted to have you for this interview! Could you kindly provide an overview of your professional journey leading up to your current role as Chief Technology Officer at Connatix?

Hello, thank you for having me! My journey in the tech world began at Microsoft as a software engineer, where I gained a solid foundation in software development. Driven by an interest in the possibilities of AI, I later founded a startup focused on developing technology to analyze text and generate interactive, visual experiences. This was eventually acquired by Connatix, a video technology company for publishers and advertisers. I joined Connatix as VP of AI and Content Automation, and through a series of progressive leadership roles, I recently became the CTO here.

As the new CTO at Connatix, could you elaborate on the primary areas you’re prioritizing to drive technological innovation and growth within the organization?

In my role, the focus is on three key areas: refining our product vision and aligning it with both market and client needs, enhancing our technological capabilities to ensure we remain at the forefront of video technology, and fostering an innovative culture within our teams. We’re particularly keen on advancing our AI-driven solutions to keep our offerings competitive and relevant.

In your estimation, how do you anticipate advancements in GenAI will influence the landscape of video and advertising in the coming year?

GenAI is poised to dramatically transform the advertising and video industries by automating content personalization and optimization at scale. Over the next year, I anticipate these technologies will make significant strides in enhancing how content is created and delivered, making these processes more efficient and tailored to individual user preferences.

Among the developments in GenAI, which aspects are you particularly enthusiastic about, especially in relation to your responsibilities at Connatix?

The capability of GenAI to automate complex processes and personalize content at a granular level excites me the most. These advancements align perfectly with our goals at Connatix, where we aim to revolutionize how video content is served to users, ensuring it is both engaging and relevant.

Can you share your personal approach to nurturing innovation and maintaining a competitive edge in the swiftly evolving arena of technology and advertising?

At Connatix, our motto is ‘Innovate to survive’. In the fast-evolving landscape of our industry, continuous innovation and invention are imperative to stay ahead of the curve. We believe that our success hinges on our ability to move quickly, experiment, embrace failures, and maintain agility in testing new ideas with clients. We see every challenge as a chance to get better and prepare for future success, and we take these opportunities seriously. Additionally, we emphasize continuous learning, integrating business knowledge with our development teams, and fostering an environment where ideas and failures are encouraged—as long as we can swiftly recover

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-dor-leitman-cto-at-connatix/

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In this fast-paced digital world, organizations are relying on event-driven architecture (EDA) that facilitates real-time responses, flexibility, and scalability in their business systems.

To understand EDA is a software design practice that structures a system’s segments to respond to, produce, and process events. For instance, any event creates a significant change in state within a system that is further triggered by external characteristics, such as user activities, sensor inputs, and other systems.

The rise of microservices is one cause for the prompt adoption of event-driven API (EDAs) management. These EDAs are centralized to this architecture, allowing data exchange through different events that aid in optimizing performance, ensuring scalability, and maintaining seamless integration between various services and applications.

In this article, we will explore the top five EDAs that enable developers and businesses to stay ahead of the evolving landscape of real-time interactions.

Apache Kafka

The first event-driven API on our list is Apache Kafka, which is an open-source, distributed streaming solution that allows developers to publish, subscribe to, and process streams of events in real time. Kafka has excelled in handling large data sets in real-time, even in low latency, which makes it an ideal solution for messaging and event sourcing. This API is also known for its high fault tolerance via its distributed architecture, guaranteeing that even in the case of node failure, data is not lost. However, Kafka lacks built-in authorization for features such as message filtering or priority queues, which are essential in some event-driven use cases and can be a major drawback while setting up distributed systems. Even though Apache Kafka is open-source and free to use, it has a paid version, which is called Confluent Cloud, that offers a fully managed data transfer service with pricing starting at $0.10 per GB for storage.

Gravitee

Even though Gravitee is an open-source API management platform, it offers event-driven API capabilities that support synchronous and asynchronous API lifecycles and security. Gravitee is known for its user-friendly interface, which simplifies the API management process, allowing developers to deploy only the components they need and reducing unnecessary complexity. Apart from that, Gravitee reinforces event-driven protocols such as WebSockets and Server-Sent Events (SSE), making it an ideal choice for businesses transitioning into EDA. However, Gravitee struggles with performance issues, particularly with high-throughput events, which eventually lags in documentation. For additional enterprise editions, Gravitee charges $1,500 per month; however, the pricing may increase on add-on custom services and API traffic volume.

To Know More, Read Full Article @ https://ai-techpark.com/event-driven-api-management-in-2024/

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The recent ransomware attack on Change Healthcare, a subsidiary of UnitedHealth Group, has highlighted critical vulnerabilities within the healthcare sector. This incident disrupted the processing of insurance claims, causing significant distress for patients and providers alike. Pharmacies struggled to process prescriptions, and patients were forced to pay out-of-pocket for essential medications, underscoring the urgent need for robust cybersecurity measures in healthcare.

The urgency of strengthening cybersecurity is not limited to the United States. In India, the scale of cyber threats faced by healthcare institutions is even more pronounced. In 2023 alone, India witnessed an average of 2,138 cyber attacks per week on each organization, a 15% increase from the previous year, positioning it as the second most targeted nation in the Asia Pacific region. A notable incident that year involved a massive data breach at the Indian Council of Medical Research (ICMR), which exposed sensitive information of over 81.5 crore Indians, thereby highlighting the global nature of these threats.

This challenge is not one that funding alone can solve. It requires a comprehensive approach that fights fire with fire—or, in modern times, staves off AI attacks with AI security. Anything short of this leaves private institutions, and ultimately their patients, at risk of losing personal information, limiting access to healthcare, and destabilising the flow of necessary medication. Attackers have shown us that the healthcare sector must be considered critical infrastructure.

The Healthcare Sector: A Prime Target for Cyberattacks

Due to the sensitive nature of the data it handles, the healthcare industry has become a primary target for cybercriminals. Personal health information (PHI) is precious on the black market, making healthcare providers attractive targets for ransomware attacks—regardless of any moral ground they may claim to stand on regarding healthcare.

In 2020, at the beginning of the pandemic, hospitals were overrun with patients, and healthcare systems seemed to be in danger of collapsing under the strain. It was believed that healthcare would be a bridge too far at the time. Hacking groups DoppelPaymer and Maze stated they “[D]on’t target healthcare companies, local governments, or 911 services.” If those organisations accidentally became infected, the ransomware groups’ operators would supply a free decryptor.

Since AI technology has advanced and medical device security lags, the ease of attack and the potential reward for doing so have made healthcare institutions too tempting to ignore. The Office of Civil Rights (OCR) at Health and Human Services (HHS) is investigating the Change Healthcare attack to understand how it happened. The investigation will address whether Change Healthcare followed HIPAA rules. However, in past healthcare breaches, HIPAA compliance was often a non-factor. Breaches by both Chinese nationals and various ransomware gangs show that attackers are indifferent to HIPAA compliance.

To Know More, Read Full Article @ https://ai-techpark.com/cybersecurity-urgency-in-healthcare/

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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/

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