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TECHNOLOGY

How Classified Information Can Be Secured with Blockchain

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How Classified Information Can Be Secured with Blockchain

No matter what the size of your business is, it is crucial that your data is secure.

Now, you can secure your organizational data with blockchain security methods.

The bread and butter of any organization is its data. Vast chunks of data are collected, as well as, generated in any organization. With the help of such accumulated digital assets, organizations provide us with optimized services. The classified information must be secured since it contains the most sensitive information, without which an organization might burn out. With the onset of the COVID-19 crisis in 2022 and the war in Ukraine, there was increased attention on cyber attacks in the healthcare space. The credit card details of patients were hacked and stolen. There is, therefore, the definite need for a better solution that will secure organizational, as well as, a customer’s classified information. Blockchain is one such technology that offers high security to data. With blockchain security methods, organizations can lock their sensitive information and prevent data breaches.

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Securing Classified Information Through Cryptography

Blockchain is a newly emerging technology that maintains a set of continuously growing blocks. Various digital transactions from multiple industries, such as finance, retail, and healthcare, are carried out within the blockchain network. Blockchain offers many advantages to several sectors. One of the significant benefits that blockchain provides is disintermediation. It eliminates the go-betweens that help organizations with secure transactions. The digital transactions are processed only after the validation of all parties. This helps meet the security constraint. Additionally, all transactions are immutable. Furthermore, blockchain technology helps in faster transactions and is processed around the clock, thus assisting users to reduce the time required for digital transactions.

The blockchain technology uses a distributed ledger and all the blocks are secured via cryptography. Any changes or manipulation will be updated in all nodes. So, the question is – how can blockchain be used to secure sensitive information? To answer that, we need to understand how blockchain security works on any given digital data. When we enter data into the node, it will be updated in all the nodes and will ask for validation from all involved parties. After the validation, the mining computer receives the entered digital data. Here, data is again checked for security, and finally, it is added to the blockchain. Blockchain security lies in its cryptography. Every user will have their unique private key, which is impossible to manipulate. Furthermore, if any hacker tries to manipulate a record, the respective nodes will be invalidated.

The Future Of Classified Information with Blockchain

Data security has become a growing concern and needs to be resolved at the earliest. Blockchain is one of the upcoming and reliable technologies that offer premium security. Blockchain technology, with all its incredible attributes, can help various sectors, especially the financial industry, to secure sensitive data. Blockchain has already made its path in multiple industries. MIT has used blockchain technology to build Enigma that secures all data in the database. The white paper described Enigma as “a peer-to-peer network, enabling different parties to jointly store and run computations on data while keeping the data completely private.”

Blockchain security methods provide a new way and vision to how organizations can secure all classified information. A clear understanding is vital to implement this technology and hence organizations must discover its full potential before leveraging it in their business


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TECHNOLOGY

NLP & Computer Vision in Cybersecurity

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NLP & Computer Vision in Cybersecurity

Natural language processing (NLP) and computer vision are two branches of artificial intelligence (AI) that are disrupting cybersecurity.

NLP is the ability of computers to understand and process human language, including speech and text. In cybersecurity, NLP can be used for fraud detection by analyzing large amounts of text data, such as emails and chat logs, to identify patterns of malicious activity. NLP can also be used for threat intelligence by analyzing data from various sources, such as news articles and social media, to identify potential security threats.

Computer vision, on the other hand, refers to the ability of computers to interpret and understand images and videos. In cybersecurity, computer vision can be used for password cracking by analyzing images and videos that contain passwords or other sensitive information. It can also be used for facial recognition, which verifies the identity of individuals who access sensitive information or systems.

Cybersecurity is a critical issue in our increasingly connected world, and artificial intelligence (AI) is playing an increasingly important role in helping to keep sensitive information and systems secure. In particular, natural language processing (NLP) and computer vision are two areas of AI that are having a major impact on cybersecurity.

NLP_in_Cybersecurity.png

Source: Masernet

NLP and computer vision have the potential to revolutionize the way organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s important to be aware that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must adopt a comprehensive and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with AI technologies. Here are 4 ways NLP & computer vision are useful in cybersecurity.

1. Detecting Fraud

NLP can be used to analyze large amounts of text data, such as emails and chat logs, to identify patterns of fraud and other types of malicious activity. This can help organizations to detect and prevent fraud before it causes significant harm.

2. Analyzing Threats

NLP can also be used to analyze large amounts of text data from a variety of sources, such as news articles and social media, to identify potential security threats. This type of “big data” analysis can help organizations to respond to security threats more quickly and effectively.

3. Preventing Password Cracking

Computer vision can be used to crack passwords by analyzing images and videos that contain passwords or other sensitive information. This type of technology can help organizations to better protect their sensitive information by making it more difficult for attackers to obtain passwords through visual means.

4. Improving Facial Recognition

Computer vision can also be used for facial recognition, which can help organizations to improve their security by verifying the identity of individuals who access sensitive information or systems.

Conclusion

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Source: Visua

AI technologies like NLP and computer vision are playing an increasingly important role in helping to keep sensitive information and systems secure. These technologies have the potential to revolutionize the way that organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s also important to recognize that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must take a holistic and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with these powerful new technologies.

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What’s Wrong with the Algorithms?

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What's Wrong with the Algorithms?

Social media algorithms have become a source of concern due to the spread of misinformation, echo chambers, and political polarization.

The main purpose of social media algorithms is to personalize and optimize user experience on platforms such as Facebook, Twitter, and YouTube.

Most social media algorithms sort, filter, and prioritize content based on a user’s individual preferences and behaviors. Social media algorithms have come under scrutiny in recent years for contributing to the spread of misinformation, echo chambers, and political polarization.

Facebook’s news feed algorithm has been criticized for spreading misinformation, creating echo chambers, and reinforcing political polarization. In 2016, the algorithm was found to have played a role in the spread of false information related to the U.S. Presidential election, including the promotion of fake news stories and propaganda. Facebook has since made changes to its algorithm to reduce the spread of misinformation, but concerns about bias and polarization persist.

Twitter’s trending topics algorithm has also been criticized for perpetuating bias and spreading misinformation. In 2016, it was revealed that the algorithm was prioritizing trending topics based on popularity, rather than accuracy or relevance. This led to the promotion of false and misleading information, including conspiracy theories and propaganda. Twitter has since made changes to its algorithm to reduce the spread of misinformation and improve the quality of public discourse.

YouTube’s recommendation algorithm has been criticized for spreading conspiracy theories and promoting extremist content. In 2019, it was revealed that the algorithm was recommending conspiracy theory videos related to the moon landing, 9/11, and other historical events. Additionally, the algorithm was found to be promoting extremist content, including white nationalist propaganda and hate speech. YouTube has since made changes to its algorithm to reduce the spread of misinformation and extremist content, but concerns about bias and polarization persist.

In this article, we’ll examine the problem with social media algorithms including the impact they’re having on society as well as some possible solutions.

1. Spread of Misinformation

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Source: Scientific American

One of the biggest problems with social media algorithms is their tendency to spread misinformation. This can occur when algorithms prioritize sensational or controversial content, regardless of its accuracy, in order to keep users engaged and on the platform longer. This can lead to the spread of false or misleading information, which can have serious consequences for public health, national security, and democracy.

2. Echo Chambers and Political Polarization

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Source: PEW Research Center

Another issue with social media algorithms is that they can create echo chambers and reinforce political polarization. This happens when algorithms only show users content that aligns with their existing beliefs and values, and filter out information that challenges those beliefs. As a result, users can become trapped in a self-reinforcing bubble of misinformation and propaganda, leading to a further division of society and a decline in the quality of public discourse.

3. Bias in Algorithm Design and Data Collection

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Source: Springer Link

There are also concerns about bias in the design and implementation of social media algorithms. The data used to train these algorithms is often collected from users in a biased manner, which can perpetuate existing inequalities and reinforce existing power structures. Additionally, the designers and developers of these algorithms may hold their own biases, which can be reflected in the algorithms they create. This can result in discriminatory outcomes and perpetuate social injustices.

4. Democracy in Retreat

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Source: Freedom House

Social media algorithms are vulnerable to manipulation and can spread false or misleading information, which can be used to manipulate public opinion and undermine democratic institutions. The dominance of a few large social media companies has led to a concentration of power in the hands of a small number of organizations, which can undermine the diversity and competitiveness of the marketplace of ideas, a key principle of democratic societies.

How to Improve Social Media Algorithms?

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Source: Tech Xplore

Governments and regulatory bodies have a role to play in holding technology companies accountable for the algorithms they create and their impact on society. This could involve enforcing laws and regulations to prevent the spread of misinformation and extremist content, and holding companies responsible for their algorithms’ biases.

There are several possible solutions that can be implemented to improve social media algorithms and reduce their impact on democracy. Some of these solutions include:

  • Increased transparency and accountability: Social media companies should be more transparent about their algorithms and data practices, and they should be held accountable for the impact of their algorithms on society. This can include regular audits and public reporting on algorithmic biases and their impact on society.

  • Regulation and standards: Governments can play a role in ensuring that social media algorithms are designed and operated in a way that is consistent with democratic values and principles. This can include setting standards for algorithmic transparency, accountability, and fairness, and enforcing penalties for violations of these standards.

  • Diversification of ownership: Encouraging a more diverse and competitive landscape of social media companies can reduce the concentration of power in the hands of a few dominant players and promote innovation and diversity in the marketplace of ideas.

  • User education and awareness: Social media users can be educated and empowered to make informed decisions about their usage of social media, including recognizing and avoiding disinformation and biased content.

  • Encouragement of responsible content creation: Social media companies can work to encourage the creation of high-quality and responsible content by prioritizing accurate information and rewarding creators who produce this content.

  • Collaboration between industry, government, and civil society: Addressing the challenges posed by social media algorithms will require collaboration between social media companies, governments, and civil society organizations. This collaboration can involve the sharing of data and best practices, the development of common standards and regulations, and the implementation of public education and awareness programs.

Conclusion

Social media companies have the power to censor and suppress speech, which can undermine the right to free expression and the democratic principle of an open and inclusive public discourse. It is crucial for technology companies and policymakers to address these issues and work to reduce the potential for harm from these algorithms. Social media platforms need to actively encourage and facilitate community participation in the development and improvement of their algorithms. This would involve setting up forums for discussion and collaboration, providing documentation and support for developers, and engaging with the community to address their concerns and ideas. In order to ensure that the algorithms are fair and unbiased, tech companies need to be transparent about the data they collect and use to train their algorithms. This would involve releasing the data sets used to train the algorithms, along with information about how the data was collected, what it represents, and any limitations or biases it may contain.

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TECHNOLOGY

Daasity builds ELT+ for Commerce on the Snowflake Data Cloud

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Cloud Computing News

Modular data platform Daasity has launched ELT+ for Commerce, Powered by Snowflake.

It is thought ELT+ for Commerce will benefit customers by enabling consumer brands selling via eCommerce, Amazon, retail, and/or wholesale to implement a full or partial data and analytics stack. 

Dan LeBlanc, Daasity co-founder and CEO, said: “Brands using Daasity and Snowflake can rapidly implement a customisable data stack that benefits from Snowflake’s dynamic workload scaling and Secure Data Sharing features.

“Additionally, customers can leverage Daasity features such as the Test Warehouse, which enables merchants to create a duplicate warehouse in one click and test code in a non-production environment. Our goal is to make brands, particularly those at the enterprise level, truly data-driven organisations.”

Building its solution on Snowflake has allowed Daasity to leverage Snowflake’s single, integrated platform to help joint customers extract, load, transform, analyse, and operationalise their data. With Daasity, brands only need one platform that includes Snowflake to manage their entire data environment.

Scott Schilling, senior director of global partner development at Snowflake, said: “Daasity’s ELT+ for Commerce, Powered by Snowflake, will offer our joint customers a way to build a single source of truth around their data, which is transformative for businesses pursuing innovation.

“As Snowflake continues to make strides in mobilising the world’s data, partners like Daasity give our customers flexibility around how they build data solutions and leverage data across the organisation.” 

Daasity enables omnichannel consumer brands to be data-driven. Built by analysts and engineers, the Daasity platform supports the varied data architecture, analytics, and reporting needs of consumer brands selling via eCommerce, Amazon, retail, and wholesale. Using Daasity, teams across the organisation get a centralised and normalised view of all their data, regardless of the tools in their tech stack and how their future data needs may change. 

ELT stands for Extract, Load, Transform, meaning customers can extract data from various sources, load the data into Snowflake, and transform the data into actions that marketers can pursue. For more information about Daasity, our 60+ integrations, and how the platform drives more profitable growth for 1600+ brands, visit us at Daasity.com.

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