AI In Healthcare: Balancing Innovation And Responsibility


Over the past few decades, Artificial Intelligence has increasingly become an important and influential technology in various aspects of our lives. AI has left its mark in various areas of education, finance, the environment, agriculture, entertainment, and healthcare.   

Recently, healthcare has been considered one of the primary areas where AI is making a big impact, particularly in disease diagnosis and treatment management. AI has emerged as a crucial technology in the healthcare industry, with a growing presence in various aspects of patient care. It’s transforming healthcare by improving the accuracy of diagnoses, enhancing the speed and efficiency of treatments, and increasing patient safety.


Categorization Of Data In AI

Making any AI technology work efficiently requires a large amount of data on which it can be trained. This data in AI is categorized as structured and unstructured data. Structured data refers to data organized in a specific format, such as electronic health records, laboratory results, and billing codes. Unstructured data is not organized in a specific format, such as medical images or physician’s notes.  

Training machine learning models on structured and unstructured data requires different techniques. Structured data can be easily processed using traditional machine-learning algorithms like decision trees and random forests. These algorithms are designed to handle data in structured formats and can easily identify patterns and trends in large datasets.  

Unstructured data, however, requires more advanced machine learning techniques, such as deep learning algorithms. Deep learning algorithms use neural networks to learn from unstructured data, such as medical images, and identify patterns and features indicative of a disease or condition. These algorithms are trained using large datasets of labeled medical images, where each image is tagged with the corresponding disease or condition.  

Training machine learning models on healthcare data, whether structured or unstructured, requires a large and diverse dataset. The more data the machine learning algorithm has access to, the more accurate it can become in identifying patterns and predicting outcomes. 

Additionally, data quality is important, as errors or inconsistencies in the data can result in inaccurate predictions. 

Impact Of AI In Healthcare 

AI Datasets Usage In Healthcare

AI relies heavily on datasets to train machine learning algorithms and improve the accuracy of diagnostic and treatment outcomes. Some of the datasets that are commonly used in Artificial Intelligence in healthcare are –   

Electronic Health Records (EHR)

EHRs contain patient medical histories, diagnosis codes, and treatment information. Machine learning algorithms can analyze EHRs to identify patterns and trends indicating a disease or condition. Moreover, EHRs can also identify patients at high risk of developing certain diseases or conditions.  

Medical Imaging Datasets

It contains images such as X-rays, CT scans, and MRIs. These datasets train machine learning algorithms to detect patterns and identify anomalies in medical images. By analyzing these images, AI can identify early signs of diseases and improve the accuracy of diagnosis.  

Genomic Datasets

These datasets contain genetic information that can be used to predict a patient’s risk of developing certain diseases. AI can analyze genomic data to identify genetic mutations that may be linked to diseases such as cancer or Alzheimer’s disease.  

Wearables And IoT Data

It can track a patient’s vital signs, like blood pressure, heart rate, and oxygen levels. This data can be used to monitor patients remotely and identify early signs of a medical condition. In addition, Machine Learning algorithms can analyze this data to predict potential health issues and improve patient outcomes.  

Clinical Trial Data

This sort of data contains information about the safety & efficacy of drugs and medical devices. Machine learning algorithms can analyze this data to identify potential side effects or drug interactions and improve treatment outcomes. 

AI can potentially improve patient outcomes and reduce healthcare costs by analyzing these datasets. 

Applications Of Artificial Intelligence (AI) In Healthcare

AI in healthcare can be used in predicting early stroke detection to seasonal flu, suggesting medical treatments, to being a virtual assistant. Some Artificial Intelligence in healthcare examples is becoming popular; details below –

Applications Of AI In Healthcare  

1. Medical Imaging Analysis

Artificial Intelligence helps clinicians analyze medical images more accurately and efficiently. ML algorithms are trained on enormous datasets of medical images, enabling them to detect patterns and identify anomalies in the images. 

AI-powered medical imaging systems can help identify diseases early, enabling clinicians to provide timely interventions and improve patient outcomes. 

For example, AI-powered systems analyze X-rays and CT scans to detect lung cancer and other diseases.  

2. Disease Diagnosis And Treatment

AI is used to help clinicians diagnose diseases and develop treatment plans. Machine learning algorithms can analyze patient data, such as medical records and lab results, and provide clinicians with personalized treatment recommendations. 

As an example, IBM’s Watson Health has been used to devise personalized treatment plans for cancer patients, thereby reducing diagnosis and treatment times.   

3. Electronic Health Records (EHR)

AI is being employed to analyze EHR to identify patterns and trends that can help improve patient care. ML algorithms can analyze large datasets of EHR to identify patients at high risk of developing certain diseases or conditions. 

For example, AI can analyze a patient’s medical history to identify those at high risk of developing heart disease and provide personalized treatment recommendations.  

4. Virtual Nursing Assistants

AI-powered virtual nursing assistants can help patients manage their health and well-being. Virtual nursing assistants can guide patients on medication, exercise, and diet and alert healthcare providers if a patient’s condition changes or they need immediate medical attention.   

5. Predictive Analytics For Hospital Operations

Artificial Intelligence predicts patient demand for emergency services, enabling hospitals to allocate resources more efficiently and reduce patient wait times. Additionally, predictive analytics can also predict patient outcomes based on their medical history, allowing healthcare providers to identify patients at high risk of developing complications and provide timely interventions.  

What Challenges Usually Occur When Employing AI In the Healthcare Sector?

AI for healthcare has tremendous potential to transform the industry, but several challenges must be addressed before it can be adopted at a large scale. Some of the major challenges to adopting AI in healthcare are –   

1. Data Privacy & Security

Healthcare data is sensitive and confidential, and patient privacy must always be protected. Adopting Artificial Intelligence for healthcare requires storing and processing large amounts of patient data, which can boost the risk of data breaches and cyber-attacks. 

Ensuring data privacy and security is a major challenge for healthcare organizations looking to implement AI solutions.  

2. Integration With Existing Systems

Most healthcare organizations have complex IT systems, and integrating AI solutions into these systems can be challenging. 

AI systems must be able to communicate with existing EHR systems and other clinical systems, which can require significant resources and time.  

3. Regulatory Compliance

Healthcare is a heavily regulated industry, and AI solutions must comply with numerous regulations, such as HIPAA and GDPR. 

Ensuring regulatory compliance can be a significant challenge for healthcare organizations looking to implement AI solutions.  

4. Limited Data Availability

While an abundance of healthcare data is available, much of it is not easily accessible or usable for AI applications. 

Data fragmentation, limited interoperability between systems, and data quality issues can make gathering the necessary data for AI solutions difficult.  

5. Lack of Trust & Acceptance

There is still a lack of trust and acceptance among healthcare providers and patients regarding AI in healthcare. 

Many healthcare providers hesitate to adopt AI solutions due to concerns about job displacement, liability, and loss of control over patient care. Patients may also hesitate to trust AI with their healthcare due to privacy and data security concerns.  

Wrapping Up

Artificial Intelligence has revolutionized healthcare by enhancing diagnostics, enhancing patient outcomes, and reducing the burden on healthcare professionals. The use of AI in healthcare has enabled the development of predictive models that can identify potential health issues and recommend preventative measures.  

Moreover, AI-based systems accurately detect diseases like cancer and heart disease. With the continued development of AI technology, it is expected that the role of AI in healthcare will continue to grow, bringing about further improvements in patient care and outcomes.  

However, it is crucial to note that the use of AI in healthcare also raises concerns about privacy, data security, and ethics, which need to be carefully considered and addressed. So, if you are thinking of using AI in your business, connect with Infrablok and get dedicated AI/ML experts who can help you fulfill your needs in no time. 

Elevating Europe’s AI Landscape: EU AI Regulations In Top Form 


Artificial Intelligence (AI), as pictured in sci-fi movies, is now becoming a reality. Its ability to change work dynamics across various industries and, furthermore, to change our lives has become increasingly evident.         

There are some risks associated with AI that might prove more dangerous to humanity than beneficial. Some of the risks associated with its deployment are bias, discrimination, and lack of transparency. To mitigate these risks, UNESCO formed an agreement on the ethics of AI, whereas the US has passed certain laws and is working on further regulations. However, right at the forefront of this charge is the European Union (EU), which took the lead in creating comprehensive frameworks and laws to regulate AI.


EU AI Regulations 2018  

The EU AI regulations have been discussed in the EU since 2018 with the publication of the European Commission’s “Communication on Artificial Intelligence for Europe.” The EU’s AI strategy emphasized the importance of ethical, legal, and socio-economic guidelines for AI development. 

Specifically, the vision is to have an AI that respects fundamental human rights, values, and ethical principles while emphasizing the need for trustworthy AI. As part of the communication, the EU was also urged to coordinate AI regulations in the union, as well as cooperate with foreign cooperation on AI governance.    

While not specifically targeting AI, the General Data Protection Regulation (GDPR) played a crucial role in shaping the EU’s approach to AI regulations. Implemented in May 2018, the GDPR focused on protecting individuals’ data rights, including personal data used in AI systems. GDPR influenced subsequent AI regulations, particularly those concerning data privacy, transparency, and accountability.     

EU AI Regulations 2019 

In April 2019, the EU’s High-Level Expert Group on AI (AI HLEG), established by the European Commission, published “Ethics Guidelines for Trustworthy AI.” which set out seven key principles for AI development and deployment. These principles are –  

7 Key Principles For AI Development & Deployment  

As part of these, a framework was established for AI assessments that involved evaluating the impact of artificial intelligence on fundamental rights, such as privacy, non-discrimination, and freedom of expression. This also served as a valuable reference for subsequent regulatory efforts.  

EU AI Regulations 2020 

In February 2020, the EU published a “White Paper on AI: A European Approach to Excellence and Trust” to plan future AI regulations. The white paper proposed a risk-based approach to AI regulation, which classified AI systems according to their level of risk and required different levels of oversight and transparency depending on their risk level. 

It also proposed a range of measures to promote innovation and investment in AI, such as increased funding for research and development and creating a European AI marketplace.  

EU AI Regulations 2021 – 2023 

Building upon earlier initiatives, the European Commission introduced a landmark proposal, the “Regulation on a European Approach for Artificial Intelligence” (AI Act), in April 2021. The AI Act aims to establish a harmonized legal framework for AI systems, balancing innovation and protection. 

The regulation requires AI systems to be designed and developed in a way that respects fundamental rights and freedoms, such as the right to privacy and the right to non-discrimination. It introduces several notable provisions –   

  • The AI Act prohibits AI systems that manipulate human behavior in a deceptive or harmful manner, such as social scoring for government surveillance.   
  • The AI Act mandates transparency regarding AI’s purpose, nature, and consequences for users. It requires high-risk AI systems to undergo conformity assessments by authorized bodies, ensuring compliance with strict data and technical requirements.   
  • The AI Act imposes obligations for developers and providers of AI systems, including comprehensive documentation, robust data governance, and human oversight.  

The AI Act sparked intense discussions among stakeholders, including AI developers, businesses, civil society organizations, and policymakers.

Feedback was considered to refine and amend the proposal, addressing concerns such as the scope of high-risk AI applications, the potential impact on innovation, and the balance between regulation and competitiveness. 

The European Parliament and the Council actively reviewed the AI Act, ensuring it aligns with the diverse perspectives and requirements of EU member states. The draft of which was passed by the EU lawmakers on April 27, 2023, and was further approved by a committee of lawmakers in the EU parliament on May 12, 2023, taking it a step closer to becoming the law.   

In addition to the AI Act, the EU is exploring complementary regulations to address data sharing for AI development while safeguarding the privacy and data protection via the Data Governance Act (DGA). It was proposed in November 2020 with the aim of facilitating data sharing across sectors and EU countries while ensuring data protection and fostering trust.   

It is a set of rules designed to expand access to public sector data for developing new products & services. The DGA applies not only to personal data but to any digital representation of acts, facts, or information.   

The rules came into force on June 23, 2022, and will take effect in September 2023. It creates a framework to facilitate data sharing and provides a secure environment for organizations or individuals to access information.    


The EU’s journey towards AI regulation showcases its commitment to ensuring ethical, transparent, and accountable development and deployment of AI systems.  

From the inception of guidelines to the proposed AI Act, the EU has taken significant strides to balance fostering innovation and safeguarding fundamental rights. While the AI Act is about to become law, it represents a milestone in shaping the future of EU AI regulations. 

These efforts position the EU as a leading authority in the global AI landscape, emphasizing the importance of responsible AI development and deployment for the benefit of society. Moreover, if you need any more information on EU AI regulations, innovative AI solutions, etc, feel free to get in touch with us

AI Regulations: A Global Perspective


It has long been recognized that continuous learning and improvements are essential for success in a constantly changing world. However, the pace of change and learning is now so rapid that people will soon start emphasizing the need to train AI models to remain in the competition. 

“If we want to stay ahead, our AI system must be the best” will become the new motto of success. It’s also possible that the science fiction scenarios of machines competing could soon become a reality. However, to prevent AI from having any kind of adverse impact, it is imperative the space remains regulated

UNESCO’s Approach To AI Regulations

Taking the lead on AI’s impacts, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) has led in promoting responsible AI development and deployment. In 2019, UNESCO released a report titled “Steering AI and Advanced ICTs for Knowledge Societies: A Rights, Openness, Access, and Multi-stakeholder Perspective,” which outlines their vision for AI regulation.     

The report emphasized the need for a multi-stakeholder approach involving input from governments, civil society, academia, and the private sector. It also called for developing ethical and legal frameworks to guide the development and deployment of AI. Moreover, the report identified several key principles that must be incorporated into frameworks as follows –  


AI systems should be designed to be explainable and understandable to all stakeholders, including end-users. It means developers should be transparent about how Artificial Intelligence (AI) systems work, what data they use, and how they make decisions.     


Developers of AI systems should be held accountable for the outcomes of their systems. It includes addressing any unintended consequences or negative impacts of AI and ensuring that AI systems are used in a way that respects human rights and dignity.  


AI systems should be designed and developed to promote inclusivity and diversity. This includes ensuring that AI systems are not biased against any particular group of people.  

Furthermore, in March 2021, UNESCO Member States adopted the first-ever worldwide agreement on the ethics of artificial intelligence (AI). The agreement is called the Recommendation on the Ethics of AI. It is designed to guide the development and use of AI in an ethical, transparent, and accountable way based on four principles:  

AI Regulations - four principles

It also includes guidelines for AI development, deployment, and governance, such as ensuring human oversight, protecting privacy and personal data, and addressing bias and discrimination. The Recommendation is not legally binding but intended to serve as a framework for ethical AI development and use.   

USA’s Approach To AI Regulations  

The regulations on AI in the USA are in their very nascent stage. The United States has taken a decentralized approach to regulating AI, with different states and agencies developing their regulations.  

For example, California has passed the California Consumer Privacy Act, which gives consumers the right to know what personal data is being gathered about them and to request that it be deleted. The act is inclusive of AI systems that collect personal information. However, there have been recent efforts to develop more comprehensive national-level federal AI regulations.  

In 2019, the National Institute of Standards and Technology (NIST) released a set of guidelines for trustworthy AI. The guidelines emphasize the importance of transparency, explainability, and accountability in developing and deploying AI systems.  

In 2020, the White House released a set of principles for AI regulation, which include promoting public trust in AI, ensuring the safety and security of AI systems, and encouraging innovation and competition in the AI industry. The US Congress passed two bills on AI, namely the National Artificial Intelligence Initiative Act of 2020 (NAIIA) and The AI in Government Act of 2020.   

The National Artificial Intelligence Initiative Act of 202 and the AI in Government Act of 2020 aim to establish a national strategy for developing and using artificial intelligence (AI) in the United States. 

The bill establishes the National Artificial Intelligence Initiative, which would coordinate AI research and development across federal agencies, academia, and the private sector.  

The NAIIA emphasizes the importance of fairness and transparency in AI systems and establishes an AI Center of Excellence to promote the development and use of safe and effective AI in the federal government. 

Additionally, the act requires federal agencies to report to Congress on their use of AI and ensure that their use of AI is fair, transparent, and accountable. 

These regulations and laws aim to ensure that AI is created and employed in an ethical and responsible manner that benefits society.   

Blueprint For An AI Bill Of Rights

Though the laws are under process to protect public rights through AI and promote responsible usage of AI, the US government has issued voluntary guidance through its Blueprint for an AI Bill of Rights. 

The Blueprint for an Artificial Intelligence (AI) Bill of Rights is a set of 5 principles that aims to protect public rights and promote responsible AI development. These principles include:  

The Right To Transparency: AI systems should be designed and operated transparently so that people can understand how they work and make decisions.  

Right To Privacy: AI systems should respect people’s privacy and protect their personal data.   

Right To Fairness: AI systems should be designed and operated fairly and unbiasedly so that everyone has an equal chance of benefiting from them.  

The Right To Accountability: AI systems should be accountable for their actions, and their designers and operators should be held responsible for any harm they cause.  

The Right To Education: People should have access to education and training to better understand AI and its potential impact on society. 


AI Regulations are at a budding stage in the US. Moreover, there are several proposed bills in the US Congress aimed at regulating AI to address issues like bias in AI & the use of AI in government decision-making. We hope to soon witness many stringent regulations coming into force to promote the development and use of AI ethically and responsibly. 

As AI technology continues to advance, these regulations must be continuously reviewed and updated to ensure that they remain relevant and effective. A leading role in acting first and constantly reviewing the AI regulations is taken by the countries of the European Union, which we will discuss in the next post. So, stay tuned! 

Moreover, if you need any additional information on AI approaches, innovative AI solutions, etc, feel free to get in touch with us 

Explore The Ethics Of Artificial Intelligence (AI) 


AI has emerged as one of the most metamorphic technologies of the 21st century. From self-driving cars and virtual assistants to advanced medical diagnosis and precision agriculture, it has the potential to revolutionize many facets of our lives.  

However, with this potential comes ethical challenges that must be addressed. The ethical considerations in the field of AI are complex and multifaceted. So, it requires careful consideration and analysis to ensure that these systems are developed and employed in a responsible and ethical manner.  

Some of the ethical considerations that must be kept in mind while designing an AI-based system are – 

Ethical Considerations

Bias & Discrimination

Artificial Intelligence systems are only as unbiased as the data they are trained on. An AI system can be biased or incomplete if the data it uses is biased or incomplete. Resulting in unfair treatment.

For example, if a facial recognition algorithm is acquainted with a dataset that is predominantly made up of lighter-skinned individuals. It may be difficult for AI to accurately identify individuals with darker skin tones. Resulting in a bias in law enforcement or other applications.

Moreover, in the case of predictive policing, there is a risk that these systems may unfairly target certain communities based on past patterns of policing. Leading to further bias and discrimination.

Privacy & Security

AI systems can raise questions about privacy and security. If it’s used to collect & analyze personal data. There is a risk that this data may be vulnerable to hacking or that it may be used in ways that violate individuals’ privacy. Leading to unintended consequences.

For example, facial recognition technology can be employed to identify individuals in public spaces, raising concerns about surveillance and privacy. It’s important to ensure that AI systems are designed and used in a way that respects individual privacy rights and data protection laws. 

Transparency & Accountability

If AI systems make decisions that affect individuals or society, it’s vital to ensure that the decision-making process is transparent & explainable. As AI systems become more complex & autonomous and start making decisions without human intervention, questions arise about who is accountable for the outcomes of those decisions.

It’s essential to establish clear lines of responsibility and accountability for AI systems, especially when their decisions affect individuals or society as a whole. A lack of this transparency and accountability can lead to potential risks and harm.

For example, if an autonomous vehicle causes an accident, it’s important to establish who is responsible for that outcome.

Control And Autonomy

As AI systems become more sophisticated, questions arise about who has control over these systems and how they make decisions. Increasing the autonomy of AI systems may lead them to make judgments that are at odds with human values.

For example, in the case of the self-driving car developed by Uber, there were questions about who was responsible for the accident and whether the autonomous car had too much control over the decision-making process. 

Human Dignity

If AI systems are used to replace human decision-making or judgment. As a consequence, human dignity and autonomy may be devalued or undermined.

For e.g., in the case of AI-powered chatbots or virtual assistants, there is a risk that these systems may be used to replace human customer service representatives. Leading to a potential loss of human connection and empathy.

Environmental Impact

AI systems can also have an impact on the environment. The development and use of these systems can require vast amounts of energy & resources, which can have negative environmental consequences.  

For e.g., the training and operation of large AI models can require significant amounts of energy. Leading to increased carbon emissions and adverse environmental impacts.

Human Oversight And Control

As AI systems become more refined and autonomous, it’s important to maintain human oversight and control over these systems. In this way, these systems are aligned with human values & intentions and do not act in ways that harm humanity.

For example: If an AI system is to make decisions about medical treatment, there must be appropriate human oversight. To ensure the decisions are aligned with the patient’s values and preferences.

To design or develop an effective AI system, it is crucial to consider its ethical implications, to truly deliver quality outcomes. Navigating AI ethical challenges can be difficult; getting help and advice from skilled AI engineers can make it much easier.

Future Of AI Despite Ethical Challenges

Future Of AI

The future of AI is exciting and challenging. On one hand, AI has the potential to revolutionize many facets of our lives, from healthcare to transportation to education. On the other hand, challenges must be addressed to ensure that AI is developed & deployed in a responsible and ethical manner.

Despite the challenges, there is a strong rationale to be optimistic about the future of AI as researchers and policymakers work to address these issues and ensure that AI is used effectively ahead. 

Additionally, AI can automate tedious and repetitive tasks, improve decision-making processes, and provide personalized solutions in various industries. If AI technology continues to evolve, it could completely transform the way we live and operate. 

All in all, the future of AI is promising. But it will require continued investment, research, & development to ensure the advantages of AI are maximized while minimizing its risks & challenges.


The ethics of AI is a crucial issue that must be addressed as AI technologies continue to evolve and transform our world. As we navigate the complex landscape of AI development and deployment, it is important to prioritize ethical concerns.

At Infrablok, we are committed to promoting the ethical use of AI and ML through our cutting-edge services and solutions. Our team of experts is dedicated to developing AI and ML systems that prioritize transparency, accountability, and fairness. 

If you are curious about availing of any of our AI/ML services, reach out to us to learn more about how we can assist you in leveraging this transformative technology to achieve your business goals. 

Web3 And AI: The Dynamic Duo Transforming Industries For The Better


Web3 and AI are catalyzing the pace of innovation, and their combined power is showing signs of promising radical shifts in almost every industry. Web3 empowers us with trustworthy decentralized data, while AI enables us to analyze large amounts of data; the two technologies combined can provide enormous scope for analyzing data and inferring results in ways previously unimagined.    

Moreover, the scope will increase from analyzing centrally stored data, especially in areas that require higher privacy, such as healthcare sectors, to accessing an exponentially high amount of data that is present in a decentralized fashion but still maintains high privacy and security.

By leveraging Web3 technology and AI algorithms, you can access a vast network of nodes that hold a copy of the blockchain’s data. This approach provides more reliable and secure data access since data is stored in multiple locations and cannot be altered by a single central authority.      

Additionally, since Web3 technology enables users to control their data and grant access selectively, businesses can ensure their data remains secure and private, even when its usage is to train AI models.

Another benefit of using AI with Web3 technology is its ability to improve machine learning models. Machine learning algorithms rely on large datasets to train their models, which means the current models are trained on particular data sets of one organization.

However, with Web3 technology, data is secure as its aim is to store data in a decentralized fashion. This means it is more likely to be representative of a broader population than data held by a single centralized authority. It provides models with an opportunity to learn from the more diverse dataset, thereby giving them the power to create more accurate models.

Know How AI With Web3 Is Benefiting Various Industries

A lot of work is happening around AI in blockchains to provide users with the most efficient solutions using the best technologies available. Some sectors that are benefitting from the use of AI and Web3 include – 


Web3 and AI are transforming the financial industry by enabling secure, decentralized transactions and advanced data analytics. For example, decentralized finance (DeFi) platforms use Web3 technology to enable secure, transparent financial transactions and use AI to analyze market trends and make predictions about future financial performance.  

Moreover, various dApps aim to provide real-time solutions to clients. An example of a decentralized application (dApp) that uses Web3 and AI in the DeFi sector is Alethea AI. Alethea AI is a decentralized platform that uses AI and blockchain technology to create synthetic financial instruments traded on decentralized exchanges (DEXs). The platform uses AI algorithms to analyze market data and create optimized financial derivatives for specific trading strategies.


Web3 and AI are being used to develop decentralized healthcare platforms that enable secure, private data sharing and personalized medicine.  

For example, AI chatbots employ to provide virtual medical consultations, while decentralized data-sharing platforms like Medicalchain are using blockchain technology to enable the secure, transparent sharing of medical data.

Supply Chain Management

Web3 and AI are transforming supply chain management by enabling transparent, secure tracking of goods and services. Supply chain management platforms like Provenance use Web3 technology to enable secure tracking of goods from source to destination. At the same time, AI algorithms employ to analyze data and optimize supply chain operations.

Similarly, IBM’s Food Trust is using AI to provide insights and analytics to improve food safety and quality across the supply chain. It provides features like predictive analytics, product authentication, quality assurance, and supply chain optimization with the use of AI and ML algorithms.  

Social Media

Web3 and AI are looking to transform social media by enabling secure, decentralized platforms that provide personalized content and improved privacy. 

Decentralized social media platforms like Steemit use blockchain technology to enable secure, transparent content sharing. In contrast, AI algorithms employ to personalize user preference content.

Another example is, which spreads information among the most interested users based on AI algorithms combined with their responses. Quality content can easily reach the whole Sola user base. Users post news, stories, and entertainment cards; Sola takes care of the rest.   


Both Web3 and AI use to develop decentralized educational platforms that enable personalized, data-driven learning.

AI chatbots are being employed to provide personalized tutoring and support, while decentralized platforms like ODEM use AI to personalize the learning experience for each student based on their learning style and preferences. TeachMePlease uses AI to match students with the most suitable tutors based on their learning objectives and preferences.

Popular Blockchain Platforms Using AI

Blockchain platforms using AI

As various industries are coming up with solutions that are creating powerful AI models combining the two most powerful technologies (Web 3.0 and Artificial Intelligence), there are several blockchain platforms that are focused on enabling the development & deployment of AI and machine learning applications in various industries. Some of these examples include the following –   

1. SingularityNET

It is a decentralized platform that uses the creation, sharing, and monetization of AI algorithms and services. It is built on the Ethereum blockchain & uses smart contracts to facilitate transactions between AI agents. 

2. Ocean Protocol

This is a decentralized data exchange protocol that allows data owners to share and monetize their data with AI and machine learning algorithms. It uses blockchain technology to provide a secure and transparent marketplace for data sharing. 

3. is a decentralized platform that aims to create an open economic ecosystem for AI and machine learning applications. It uses a combination of blockchain technology and machine learning algorithms to enable autonomous agents to interact and transact with each other. 

4. Endor Protocol

This blockchain-based platform uses AI and machine learning algorithms to provide predictive analytics for businesses. It allows users to ask complex business questions and receive predictive insights in real time. 

5. Neuromation

Neuromation is a blockchain-based platform that provides synthetic data and machine learning services for businesses. It allows users to generate high-quality synthetic data to train AI models and provides a marketplace for machine learning models and services. 

Final Thoughts

The integration of AI and Web3 has the potential to create even more powerful and innovative applications that can revolutionize several industries. The challenges of interoperability, privacy, and security must keep in mind to ensure the successful integration of these technologies. As Web3 continues to evolve, we can expect to see even more exciting applications that leverage the power of AI.

Moreover, if you are also interested in integrating AI into your existing Web 3 project or want to develop amazing and unique software using Web and AI, share your ideas with Infrablok experts, and we will help you bring your imagination to the real world.  

Web3 With Genomics Transforming Healthcare Sector For Better


The complete set of genes or genetic material present in a cell or organism is known as the genome. The study of the structure, function, evolution, and mapping of genomes is known as genomics. Genomics technology is the set of techniques and tools used to study an organism’s entire DNA sequence, including its genes and other genetic material. These technologies enable researchers to analyze and interpret the vast amount of genomic data generated from DNA sequencing technologies.  

The vast amount of data needs to be handled securely and carefully, and that is where Web3 comes in. These two technologies are Genomics and Web3 handshake to keep the data secure and private yet maintain transparency to achieve innovations that can lead to better healthcare. 

The Benefits of Combining Genomics And Web3 Technology

The Benefits of Combining Genomics And Web3 Technology

There are various benefits associated with using Genomics and Web3 Technologies; some of them are – 

1. Security 

Genomic data is highly sensitive and valuable and needs to be secured from unauthorized access or manipulation. Blockchain provides a secure and tamper-proof infrastructure for storing and sharing genomic data.  

2. Privacy 

Individuals who provide their genomic data for research need control over their data and privacy. Web3 enables individuals to maintain control over their genomic data and share it on a need-to-know basis while still receiving incentives for sharing their data.  

3. Transparency 

Using blockchain (Web3) in genomics enables transparent and auditable transactions of genomic data, ensuring that data is being used ethically and for its intended purpose.  

4. Collaboration 

Genomics research requires collaboration among researchers, clinicians, and other stakeholders. Web3 can facilitate collaboration by providing a decentralized infrastructure for secure and transparent genomic data sharing.   

5. Innovation 

Web3 can help to spur innovation in the field of genomics by enabling the creation of decentralized genomic data marketplaces, where individuals can sell their genomic data to researchers and companies, driving the development of new therapies and treatments.  

With the key benefits listed above, Web3 technology helps in many ways in the field of genomics by providing it with a decentralized infrastructure for storing, sharing, and analyzing genomic data and facilitating secure and transparent transactions of genomic information.

Read More –  Notion AI: A Short Guide To Boosting Business Productivity And Efficiency!

How Is Web3 Assisting Genomics Technology?

Here are a few examples that will let you understand how Web3 technology is helping genomics:  

DNA Sequencing

It is the process of determining the order of nucleotides in a DNA molecule. There are different DNA sequencing technologies available, including next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies. 

Web3 can help in DNA sequencing by enabling the secure storage of large amounts of genomic data on a decentralized network, such as the blockchain. It can ensure that the data is tamper-proof and immutable while allowing for secure and easy data sharing among researchers and healthcare providers. 

For example, Shivom is a blockchain-based genomics platform that uses Web3 technology to securely store and share genomic data.  


Involves the use of computational tools and techniques to analyze and interpret genomic data. It includes data storage, retrieval, and analysis methods that allow researchers to make sense of the vast amount of genomic data generated from sequencing technologies. 

Web 3.0 technology can also facilitate the development of decentralized bioinformatics tools and platforms, allowing researchers to analyze and interpret genomic data securely and transparently. 

For example, EncrypGen has created a decentralized marketplace for genomic data called Gene-Chain, which uses blockchain technology to enable secure transactions of genomic information and provide transparent access to data for researchers and companies.  

Genome Editing

Enables researchers to modify the DNA sequence of an organism. It can be used to study gene function, create disease models, and develop new therapies for genetic diseases. 

Web 3 can help in genome editing by enabling the secure and transparent sharing of genetic data and the development of decentralized platforms for gene editing research. 

For example, Nebula Genomics has developed a blockchain-based platform that allows individuals to share their genomic data securely and transparently for research while maintaining control over their data and privacy.  

Functional Genomics

Is the study of how genes function and interact with each other. It includes techniques such as gene expression analysis, epigenetics, and proteomics. 

Web3 can facilitate functional genomics research by providing a decentralized infrastructure for sharing and analyzing gene expression, proteomics, and other functional genomics data. 

For example, Luna DNA has created a blockchain-based platform that enables individuals to securely share their genomic and health data for research purposes while ensuring that they maintain control over their data and receive incentives for sharing.  

Genome-Wide Association Studies (GWAS)

Involves the analysis of genetic variations across large populations to identify genetic risk factors for complex diseases. 

Blockchain technology can create decentralized GWAS platforms that enable researchers to analyze large amounts of genomic data securely and transparently. 

For example, Zenome has developed a blockchain-based platform that allows individuals to share their genomic data and participate in GWAS studies while receiving incentives for sharing their data and contributing to research. 

Wrapping Up

Web3 enabled the creation of decentralized genomic data marketplaces, where individuals can sell their genomic data to researchers, pharmaceutical companies, and other interested parties. These marketplaces incentivize individuals to share their data while ensuring that it is used ethically and transparently. 

Moreover, it also facilitates the development of decentralized genomic applications, such as genetic testing and personalized medicine. These applications leverage the decentralized infrastructure to provide secure and reliable access to genomic data while enabling individuals to control their data and privacy. 

Thus, the combination of Genomics technology and Web3 can revolutionize how we generate, share, and use genomic data, leading to improved healthcare outcomes, scientific discoveries, and economic benefits. 

So, to make your healthcare business wiser, focus on developing software that includes Genomics and Web3 technologies. Additionally, to get innovative ideas related to it, get in touch with Infrablok experts! 

Pros And Cons Of Using BaaS: A Short Guide For Business Leaders


Blockchain technology has revolutionized the way businesses store and transfer data, offering a secure and transparent way to manage transactions. However, implementing and managing a blockchain network can be daunting and costly, especially for small and medium-sized businesses (SMBs) that may not have the resources to invest in hardware and software infrastructure. BaaS providers offer a solution to this problem, providing pre-built blockchain networks, tools, and APIs that SMBs can use to build and deploy their blockchain applications quickly and easily. 

Benefits Of Preferring Blockchain-as-a-Service (BaaS)

BaaS Benefits

There are many benefits associated with using BaaS, some of which are –

Reduced Costs

One of the primary benefits of BaaS is cost reduction. Blockchain-as-a-Service provider offers subscription-based pricing models, which means SMBs can pay for only the resources they use. This eliminates the need for large upfront investments in hardware and software infrastructure, making blockchain technology more accessible to SMBs. For example, an SMB can use a BaaS provider like IBM Blockchain Platform, which offers a free trial and a pay-as-you-go pricing model, to develop and deploy their blockchain application without incurring high infrastructure costs.

Easy Implementation

Another benefit of BaaS is easy implementation. BaaS providers offer pre-built blockchain networks and tools, which means SMBs can quickly and easily implement their blockchain applications without having to spend time and resources on development and testing.

For example, Microsoft Azure Blockchain Workbench offers a range of pricing options, including a free trial and a pay-as-you-go pricing model, making it an affordable option for SMBs looking to implement a blockchain network without a significant investment in infrastructure.


Blockchain-as-a-Service Providers offer scalable infrastructure that can accommodate the changing needs of SMBs as they grow. This means that SMBs can easily add or remove nodes from their network as needed without having to worry about the underlying infrastructure.

For example, Amazon Managed Blockchain offers a simple pricing model based on the number of nodes in the network, which makes it an affordable option for SMBs that require a small number of nodes initially but may need to scale in the future.


BaaS providers offer built-in security measures, such as encryption and authentication, to protect blockchain networks and applications from cyber threats. This means that SMBs can focus on their core operations without having to worry about the security of their blockchain network.

For example, the IBM Blockchain Platform offers a range of tools and APIs to help SMBs build and deploy their blockchain applications securely.


Blockchain-as-a-Service provider provides tools and APIs that enable SMBs to collaborate and share data securely with other businesses and partners. This makes it easier for SMBs to work together and streamline their operations.

For example, Microsoft Azure Blockchain Workbench offers a range of tools and APIs to help SMBs build and deploy their blockchain applications collaboratively.

However, while BaaS Blockchain offers multiple advantages, it also includes a few cons.

Drawbacks Of Blockchain-as-a-Service (BaaS)

The pointers mentioned below will help you understand the BaaS cons.

Limited Control

When using a Blockchain-as-a-Service, SMBs rely on the provider’s infrastructure, which means they have limited control over the underlying technology. This lack of control can be a disadvantage when it comes to customization and flexibility.

Security Concerns

Blockchain technology is still relatively new and complex, and SMBs may not have the necessary expertise to ensure the security of their applications. When using a BaaS provider, SMBs must trust the provider’s security measures and protocols, which can be a risk.

Limited Scalability

Some Blockchain-as-a-Service Providers may not be able to accommodate large-scale blockchain applications or high transaction volumes. This can be a problem for SMBs that need to scale their blockchain applications quickly as their business grows.


When using a BaaS provider, SMBs rely on the provider’s availability and uptime. Any downtime or service interruptions on the provider’s side can affect the SMB’s ability to access and use their blockchain applications.

Potential Vendor Lock-In

If an SMB relies too heavily on a BaaS provider, it may become locked into using the provider’s technology and services. This can make it difficult to switch providers or move to a self-hosted solution in the future.

Popular Blockchain-as-a-Service (BaaS) Providers

Some of the popular BaaS providers are –

1. IBM Blockchain Platform

The Platform offers a range of tools and APIs to help SMBs build and deploy their blockchain applications. It also offers a free trial and a pay-as-you-go pricing model, which makes it affordable for businesses of all sizes.

2. Microsoft Azure Blockchain Workbench

It offers a range of pricing options, including a free trial and a pay-as-you-go pricing model, making it an affordable option for SMBs. It also offers a range of tools and APIs to help SMBs build and deploy their blockchain applications.

3. Amazon Managed Blockchain

This platform offers a simple pricing model based on the number of nodes in the network, which makes it an affordable option for SMBs that require a small number of nodes initially but may need to scale in the future. It also offers a range of tools and APIs to help SMBs build and

4. Oracle Blockchain Platform

It is a cloud-based platform that offers a range of tools and APIs to help SMBs build and deploy their blockchain applications. It also offers a flexible pricing model based on usage, which means SMBs can pay for only the resources they use.

5. SAP Cloud Platform Blockchain

This is a Blockchain BaaS platform that enables SMBs to build and deploy blockchain applications using pre-built templates and tools. It also offers a flexible pricing model based on usage, which means SMBs can pay for only the resources they use.

6. Alibaba Cloud BaaS

The blockchain platform enables SMBs to build and deploy blockchain applications using pre-built templates and tools. It also offers a range of pricing options, including a free trial and a pay-as-you-go pricing model.

7. Binance Smart Chain

It is a decentralized blockchain platform that enables SMBs to build and deploy blockchain applications using pre-built tools and APIs. It offers affordable transaction fees and fast transaction times, making it an attractive option for SMBs looking to build blockchain applications on a budget.

Wrapping Up

Blockchain-as-a-Service is a cost-effective way for businesses to implement and manage blockchain-based applications. By leveraging the infrastructure and tools provided by Blockchain-as-a-Service providers, businesses can focus on developing and deploying their applications rather than managing the underlying infrastructure, but before selecting a provider, they must carefully evaluate the pros and cons of choosing a BaaS provider before making a decision.

It’s important to choose a provider that offers the right balance of affordability, security, scalability, and control to meet the SMB’s specific needs. We at Infrablok can help you choose the best BaaS solution for all your business needs. Contact us if you wish to know more about it!

Empower Your Business With The Track And Trace APIs Devised By Infrablok

Blockchain provides security and trustworthiness, which is a must for any transaction, be that online or offline. This need increases for the transactions that involve the movement of the product(s) from one location to another, be that for shipping/logistics in business-to-consumer transactions or be that be in any supply chain for business-to-business transactions. Blockchain provides a promising solution to this with the implementation of Track and Trace systems in products.

Track and Trace systems over Blockchain allow for transparency and accountability in the supply chain, providing a secure and tamper-proof record of a product’s journey from its origin to its destination.      

Track And Trace

The Track and trace software allows tracking of a product at every stage of its journey, from its origin to destination. It enables companies to track a product’s movement providing transparency and accountability. This is important for various reasons, such as complying with regulatory requirements, improving supply chain efficiency, and ensuring product safety.  

Blockchain technology allows for the creation of a decentralized and immutable record of transactions. In the context of Track and Trace, a Blockchain can be used to record every step of a product’s journey, from origin to destination. Each and every transaction is recorded in a block, which is then added to the chain. This creates a transparent and tamper-proof record that can be accessed by anyone with the necessary permissions.  

The use of Blockchain in Track and Trace provides several benefits; some of them are: 

  • It creates an unalterable and transparent record of a product’s journey. This means that any attempts to tamper with the record will be detected, providing an additional layer of security to the supply chain.
  • It allows for tracking of a product, enabling companies to respond to any issues that may arise in transit.
  • It allows the creation of smart contracts that can be automatically triggered based on predefined criteria, such as delays in delivery or quality control issues.

Benefits Of Track And Trace Over Blockchain

There are several benefits of implementing Track and Trace systems over Blockchain, some of which are outlined below:  


A blockchain-based Track and Trace system provide transparency across the entire supply chain, allowing companies to monitor the movement of their products. This transparency improves accountability and ensures that everyone involved in the process is held responsible for their actions.  


Blockchain technology provides a secure & tamper-proof record of transactions. This means that any attempts to alter or tamper with the record will be detected, providing an additional layer of security to the supply chain.  


Blockchain-based Track and Trace systems can improve supply chain efficiency by providing tracking of products. This allows companies to respond to any issues that may arise, such as delays in delivery or quality control issues. 


A track and trace system over a Blockchain can help companies comply with regulatory requirements by providing a transparent and tamper-proof record of their products’ journey.

Infrablok’s Track And Trace APIs

Implementing a Track and Trace system over Blockchain creates a transparent and secure record of a product’s journey. And this provides transparency and accountability across the entire process.

We have created a set of APIs at Infrablok that would help companies implement Track and Trace over the Blockchain. These APIs can be used by any organization that is –   

  1. Creating a decentralized application to have the feature of Track and Trace.
  2. Already have a decentralized application but need to have the functionality of Track and Trace.
  3. Working on Web2 but needs to move on to Web3. 

Using our APIs, corporations could add tracking and tracing functionality to their own applications, saving around 30% of their time. Integration of these APIs with existing Blockchain products or products that require a decentralized, trustless method for tracing products is possible.

In addition, these APIs create an asset on a smart contract with a unique identifier transferred down the supply chain. In this, a trail of all actions performed on the asset and ownership is transferred to all participants.

A single unit can be packaged into a carton/container with its unique ID and then can be awarded/outwarded. Moreover, all the units in it can be transferred to the new owner simultaneously; afterward, they can be unpackaged for micro-transfers.

Infrablok APIs List

Track and Trace APIs

Here is the APIs list that we provide – 

New Asset – This registers the product, a single unit at a time.

New Package – This packages a list of products together into one package.

Outward Entry – This is for outwarding the group of products or a group of packages from the product owner to the logistics provider, which involves changing ownership of all the products either provided as a single product list or packing list to the logistics provider.  

Inward Entry – The receiver provides the group of product ids or the group of package ids that are received. It involves changing ownership of all the products either provided as a sole product list or packing list to the receiver. 

Product Delivery To The End User (Setting status as SOLD) – the buyer provides the group of product ids or the group of package ids that the end user received. It involves changing ownership of all the products, either provided as a sole product list to the end-user or setting the final state to SOLD.

Depackage APackage – This process involves depackaging a package. Here assets packed within the provided package id will be unpacked again. 

Track An Asset – This process returns all the tracking details of an asset. 

Get An Asset Detail – This process gives details about an asset.  

Check If An Asset Exists For A User – This process checks if an asset exists for the provided user address and returns “true” or “false” in response to the call.  

Get All Asset ids – This process returns asset ids already registered on the Blockchain   

Some or all these APIs can be used as per the requirement of the decentralized application being created. The smart contracts for these APIs have been deployed on the Goerli Testnet for users to test and interact with the API’s functionality.   

In order to have upgraded options like customized smart contracts or deploying smart contracts on a Blockchain of the user’s choice are also provided. To know about our product and know how to use it, visit HERE.

How To Perform Smart Contracts Auditing?


Blockchain is a secure, reliable, immutable network that provides its users with a lot of trusts despite being trustless. This trust comes in through various conditions that are put over a blockchain in a tamper-proof, decentralized platform in the form of smart contracts. These smart contracts are self-executing computer programs that automatically execute the terms of an agreement when certain conditions are met. These conditions are the terms of an agreement between the two participating parties written in the form of code in the smart contract. For example, contracts for releasing finances when certain preset conditions are met or managing supply chain logistics, voting, etc.  

As smart contracts are codes deployed on the blockchain, it becomes necessary to check them thoroughly before deploying them over the blockchain. Once deployed, they become immutable, which means any vulnerability left unchecked in the smart contract might compromise the security & reliability of the whole system. This thorough checking of vulnerabilities is done through auditing.  

The DAO Hack

The DAO hack is a famous example to understand the extent of loss one might have to bear if smart contracts are not audited properly. In 2016, a vulnerability in the smart contract deployed by DAO was found by a hacker. The vulnerability resulted from a combination of factors, including a lack of proper error handling and insufficient testing. Specifically, it allowed an attacker to repeatedly withdraw the same funds multiple times before the funds were properly transferred and recorded on the blockchain allowing the malicious attacker to drain approximately 3.6 million ETH (worth around $50 million at that time) from the organization’s funds.   

This vulnerability was due to a programming error in the smart contract code, which allowed the attacker to take advantage of a race condition and execute the malicious code before the smart contract had a chance to update its internal state.  

It’s worth noting that this vulnerability was not widely understood or anticipated at the time, and it took the Ethereum community by surprise, resulting in a hard fork of Ethereum and dividing the Ethereum blockchain into Ethereum Classic (ETC) and Ethereum (ETH). Thus, the DAO hack serves as a cautionary tale & a reminder of the importance of thoroughly auditing and testing smart contract code before deploying it on the blockchain.  

Steps For Auditing Smart Contract

Auditing helps ensure that the smart contract is functioning as intended and meets the users’ requirements. The auditing process involves thoroughly examining the code, its logic, and the underlying business requirements. It helps identify potential issues or areas for improvement and ensures that the smart contract will function as expected.  

This auditing follows a two-step approach: the first is to conduct an audit using automated tools, and the second is to get smart contracts audited manually through a certified auditor.  

Automated Audit

Automated audits are done with the help of automated tools (free or paid) available online. Some of the freely available tools that can be used to perform automated audits are as follows –   

1. Mythril

It is an open-source security analysis tool that uses concolic analysis, taint analysis, & control flow checking to detect various security vulnerabilities in Ethereum smart contracts. To use Mythril, install the tool on the computer & run the following command in the terminal to analyze a smart contract:

myth analyze <contract-file> 

For example, if a smart contract is saved in a file named MyContract.sol, then run the following command: 

myth analyze MyContract.sol 

Mythril will then analyze the code and produce a report that lists any potential vulnerabilities it identified. More details on Mythril can be found at  

2. Remix

It is a web-based Integrated Development Environment (IDE) for writing, testing, and deploying smart contracts. To audit smart contracts in Remix, activate the MythX plugin and sign in. After signing in, compile the code in the Remix editor and click the “Analysis” button. It will analyze code & produce a report that lists any potential vulnerabilities identified & suggestions for fixing them. 

3. Solhint

It is a linter for Solidity, the programming language used to write smart contracts on the Ethereum blockchain. To use Solhint, install the tool on the computer. Thereafter, run the following command in the terminal to analyze a smart contract:

solhint <contract-file> 

For example, if a smart contract is saved with a file named MyContract.sol, run the following command: 

solhint MyContract.sol 

Solhint will then analyze the code and produce a report of identified issues, such as potential security risks, coding style violations, and missing documentation. 

These are just a few examples of how some of the free smart contract auditing tools can be used. However, these tools can be useful in identifying potential security risks and vulnerabilities in smart contracts. But these tools should not be used alone for auditing. A thorough security audit should also include manual auditing.

Manual Audit

Following are some steps that are taken to conduct a manual audit on smart contracts to ensure the security & functionality of the code –   

Smart Contract Manual Auditing Step

1. Code Review

The first step in auditing a smart contract is thoroughly reviewing the code. The auditor examines the code line by line, looking for any potential vulnerabilities or security risks. They also check that the code adheres to best practices and industry standards. It includes checking for common security pitfalls such as reentrancy, overflow/underflow, and integer rounding errors.  

2. Test Suite Review

The auditor also reviews the test suite to ensure it covers all possible scenarios and edge cases. They verify that the test suite is comprehensive and includes tests for potential security risks.  

3. Testing and Deployment

The auditor deploys the smart contract on a test network & runs a series of tests to check vulnerabilities or security risks. They also check that the smart contract functions as expected and meets the requirements outlined in the code. It may involve using automated tools, such as fuzz testers, to stress-test the code and identify any weaknesses.  

4. Security Analysis

The auditor performs a smart contract security analysis to identify potential risks, such as exploits, unauthorized access, or data leaks. It may involve running security scans and penetration testing to identify potential vulnerabilities.  

5. Report Generation

The auditor generates a report summarizing their findings and recommendations after the audit. This report provides a clear picture of the security & functionality of the smart contract and outlines necessary modifications or fixes issues.  

After following all the steps above, one needs to remember that auditing is an ongoing process, as the code and the underlying blockchain technology keep evolving. Thus, regular security reviews and updates become necessary to maintain the security & functionality of the smart contract.  

Ending Note

Auditing smart contracts is a critical step in ensuring the security and functionality of decentralized applications. A comprehensive audit can help to identify potential vulnerabilities & security risks and help to ensure the successful deployment and operation of the smart contract on the blockchain.  

As a result, it is critical to identify & address any potential security vulnerabilities or bugs before deployment, as they cannot be fixed afterward. Additionally, auditing helps to increase the overall trust and confidence in the smart contract and the platform it operates on. It provides assurance to users that the contract is secure and meets the necessary standards for quality and reliability. So, if you are looking forward to auditing your smart contracts, get in touch with a reliable Blockchain development company (Infrablok). It will help you perform smart contract auditing in a secure and efficient manner. 

Exploring The Different Layers Of Blockchain Technology


In 2021, worldwide spending on Blockchain solutions was predicted to reach $6.6 billion. As Blockchain solutions become more prevalent in the coming years, spending is expected to reach $19 billion by 2024.  

Blockchain technology is a decentralized digital ledger created with blocks that form data across a peer-to-peer (P2P) network. Once data is stored on this ledger, it becomes near unattainable to delete, modify and hack. In fact, this unique characteristic of Blockchain has inspired many to build Blockchain-based projects for their businesses.  

However, before pondering how the Blockchain can be utilized in your business, you must first understand how it works. To take advantage of Blockchain technology fully, let’s examine Blockchain layers.   

The Structure Of Blockchain Layered Architecture

Blockchain technology is built on a layering architecture. It is divided into various layers, where each layer serves a specific purpose. At its core, all the layers work together to provide a secure, reliable, decentralized layered network called Blockchain.  

Let’s explore the various layers that make Blockchain technology work – 

Blockchain Layered Architecture 

1. Infrastructure Or Hardware Layer

It is the first layer of the blockchain network that consists of network and data servers. It consists of data servers that securely store the data of the Blockchain. Blockchain follows peer-to-peer (P2P) network architecture where each node is connected to every other node in a network.   

But this network is distributed across the globe, making it decentralized. Sharing of data is quick and easy because of this P2P architecture. It is done by following the client-server architecture approach. Thus, a distributed ledger is constructed where a node communicates with another node and is allowed to randomly check the transactional data.

When a blockchain dApp sends a request to the data server for access, it connects with the peer client in its P2P network, letting the exchange of data with each other. The transactions in this network are computed, validated, and recorded in an organized way in a shared ledger creating a distributed database.      

2. Data Layer

It is the second layer of blockchain technology and is responsible for storing and organizing data on the Blockchain. It uses a linked list of blocks to arrange the transactions, and when a certain number of transactions are authenticated by nodes, the data is clubbed into a block. This block is uploaded to the Blockchain’s linked list as a new block and linked to the previous block. If it’s the first block in the list, then there can be no linkage to the previous block; in this case, it is known as the genesis block. 

Each block in the Blockchain has the Merkle tree’s root hash along with the previous block’s hash, timestamp, nonce, and block version number. This information in each block guarantees the Blockchain’s security, integrity, and irrefutability. All transactions in Blockchain are digitally signed by the private key of the sender’s wallet and are known as finality. 

Every transaction on the block is signed digitally with the private key from the sender’s wallet. Since this key is only available to the sender, data can’t be tampered with by anyone. This step is termed “finality”. The data is also protected by the digital signature that protects the owner’s identity, which is encrypted for security reasons. 

Layers Present In Data Layer

The layers in the data layer of a Blockchain can be categorized as follows – 

  • Data Storage Layer – This is the layer that actually stores the data on the Blockchain. It can be implemented using various distributed ledger technologies, such as a distributed hash table or a directed acyclic graph. An example of a distributed ledger technology used in Blockchain networks is the Blockchain itself, which stores transactions and blocks in a chain-like structure using cryptographic hashes to ensure the integrity and security of the data. 
  • Data Organization Layer – This is the layer that organizes the data on the Blockchain in a way that is easy to access and retrieve. This can be accomplished using indexing systems or data structures that allow for efficient data storage and retrieval. An example of a data organization layer in a blockchain is the Merkle tree, which is a data structure that enables efficient verification of large amounts of data. 

 3. Network Layer 

It is also known as the P2P layer or propagation layer that does inter-node communication. The transaction on the Blockchain is carried out by nodes. P2P helps nodes in a network detect other nodes to make inter-node communication easy and quick. 

Moreover, it lets multiple nodes transmit transactions to settle an agreement on the transaction’s legality. It handles block generation, node detection, and block addition in the blockchain network. 

4. Consensus Layer 

The most vital layer out of all the layers on the Blockchain is the Consensus layer. This layer holds the major responsibility that is authenticating transactions. If this layer fails to authenticate the transaction or does not do it correctly, then the whole system will fail. It implements the protocol to validate transactions that are based on some complex mathematical formula that needs a certain number of nodes to validate that single transaction.  

Thus, several nodes process every transaction when the required number of nodes reach the same conclusion and agree to its legality. Thereafter the transaction is validated, making it a decision based on the consensus mechanism, where no single node has control, therefore, maintaining the decentralized characteristic of Blockchain.   

At any given time, multiple nodes are processing transactions, bundling them, & adding them to the Blockchain, which may result in the generation of several blocks at the same time resulting in a blockchain branch. But the consensus layer ensures that only a single block is added at any given time to the Blockchain and addresses all the disputes related to it, enforcing uniformity of the network. 

Examples Of Consensus Algorithm 

A couple of examples of consensus algorithm used in Blockchain is –  

  • Proof of Work (PoW): This is the consensus algorithm used by the Bitcoin Blockchain. It requires nodes to solve a complex mathematical problem in order to validate new transactions and create new blocks. The first node to solve the problem gets to add the new block to the Blockchain and is rewarded with Bitcoin. 
  • Proof of Stake (PoS): This consensus algorithm allows nodes to “stake” their tokens to validate new transactions and create new blocks. The more tokens a node stakes, the higher the chances of it being chosen to validate a new block.

5. Application Layer

It is the topmost layer of the Blockchain network stack where decentralized applications (dApps), smart contracts, etc., interact with the users via scripts, APIs, user interfaces, and frameworks. Users can perform any action at this layer, like buying or selling a cryptocurrency or NFT, tracking transactions over the network, tracking the supply chain, securely voting, etc. 

Additionally, the protocols in this layer are subdivided into application and execution layers. End-users utilize the programs at the application layer to communicate with the Blockchain network. The application layer then gives instructions to the execution layer to execute the transaction, ensuring that the deterministic nature of the Blockchain is maintained.  

Examples of applications built on the Application layer are cryptocurrency wallets, decentralized exchanges, and prediction markets, to name a few.  

Different Layers Of Blockchain Protocols

Another way to understand Blockchain technology is by knowing the different layers of Blockchain protocols. The blockchain network comprises four layers, as follows – 

Difference between blockchain layers

Layer 0

It is a base of the Blockchain ecosystem on which the rest layers are built and is often referred to as the Blockchain itself. It consists of the network and all its components (physical infrastructure and hardware) that work together to make the blockchain function. 

The solutions provided at this layer are compatible with the scaling solutions of Layer 1 and Layer 2. In addition, this layer supports the functionality of cross-chain communication or inter-chain operability. The native token of respective chains belongs to this layer. 

Some examples of Layer 0 are Bitcoin, Ethereum, Polkadot, Avalanche, Cardano, Cosmos, etc.   

Layer 1

It consists of the protocol or architecture that defines how the blockchain network operates. It carries out tasks like resolving disputes, consensus mechanisms like proof-of-work or proof-of-stake, protocols, and constraints. 

Layer 1 can validate and finalize transactions on its own. Blockchains at Layer 1 have their own native token, known as a coin, which is used to pay transaction or gas fees. It symbolized the actual Blockchain. 

Tasks that are required to be solved in this layer give rise to the Blockchain’s scalability issue. Any increase in the Blockchain means higher computational power required to solve & add blocks in the Blockchain, resulting in high fees and longer processing times. 

Some examples of Layer 1 are Bitcoin, Ethereum, Binance, and Solana.   

Layer 2

This layer is also known as the execution layer of the Blockchain. The increase in scalability and, thus, throughput was getting restricted in layer 1, which gave rise to layer 2. It helps in solving the scalability problems of Layer 1 to a certain extent by computing the transactions off-chain.

Moreover, it took off the heavy lifting from Layer 1 by moving those transactions off-chain, which need not be recorded on-chain. This allowed third-party or off-chain solutions on Layer 2 to be integrated with Layer 1.  

As this layer sits on top of Layer 1 and exchanges information with it, it adds to the features of Layer 1. 

Example – Lightning Network deployed on Bitcoin blockchain or plasma on Ethereum.   

Layer 3

This is the application layer or user interface where the users interact with the Blockchain using dApps. It hosts dApps and other protocols that enable users to use other applications over Blockchain. Moreover, it aims to provide ease to the user, enabling intra and inter-chain operability.  

Examples of Layer 3 decentralized applications include decentralized exchanges like Pancake swap, wallets like Coinbase, liquidity management protocols like Aave, and payment mechanisms like Tornado cash, to name a few.  

Wrapping Up

Blockchain is a layer stack made up of several layers where each layer is dedicated to serving a specific purpose. From setting up of network and servers in the infrastructure layer to letting the users interact with the dApps in the Application layer, all the layers work in conjunction with each other to create a secure, decentralized, reliable network. Understanding all the layers of Blockchain can help understand its core functioning and create better dApps. 

If you are interested in building feature-rich dApps for your business vertical, connect with one of the best Blockchain development companies (Infrablok). By doing so, you will get a dedicated development team who will help you form unique and business-centric dApps.