A cybercriminal’s approach is complex. They want to gain access to your network, and they have multiple ways of doing so. An attack vector could be a virus sending out emails that look like invoices from companies you have worked with but are malicious attachments. Another possibility would involve gaining root privileges on your device through malware. Attack surfaces can include any location within their chosen network where vulnerabilities exist allowing them temporary control over devices connected into it. This could be computers running outdated versions of Windows that have never been patched, routers without up-to-date firmware downloaded and installed, which leaves users vulnerable.

The list of attack vectors is extensive, with creative ways to steal information and access private networks. It is not just one or two common techniques that cybercriminals use. It is many intricate methods which they have found over time. Such as cleverly devising these attacks through various means. They could plant malware on your computer without you ever knowing when visiting malicious websites (a practice called “drive-by download”). Other times an exploit may be used. An error within code where something goes wrong during execution but still leads back into the program itself.

Common Attack Vectors

· DDoS Attack

A DDoS attack is a Distributed Denial of Service. This means that an absurd number of Internet Protocol (IP) address requests are sent to flood the targeted network with traffic and overload it. This is done often on purpose just for fun, or as revenge.

· Email Fraud

Social engineering is a form of manipulation that involves pretending to be someone you are. This is to trick people into giving up personal

information or doing something they would not normally do. Social engineers will often use email fraud as one way they can get what they want from their victims.

· Man-In_The_Middle Attack

Man-in the middle attacks are a type of eavesdropping attack that often involves an insider trying to listen in on communications between two parties. Once this has been accomplished, both counterparties will exchange encrypted keys according to some agreed upon algorithm.

· IoT Device Hack

The Internet of Things (IoT) device hack is gaining access to a smart phone, computer, or other Internet-connected gadgets for various purposes.

· Phishing

Phishing is a type of social engineering that involves phone calls and emails to gather vital information from people. The idea can be as simple as an email asking someone for their banking login, which could then put them at risk by thieves.

· Unsecure Wi-Fi Connection

Many people do not know that they can be tracked by hackers when using a Wi-Fi connection. This is because the information being sent over these networks is not encrypted, which means it could potentially be eavesdropped on for any reason.

Businesses can take advantage of automation technologies for their IT operations, like handling service requests and performance management. For example, as an IT department’s role becomes more routine it takes up less time than the higher-value cases that require special expertise to address. IT Process Automation (ITPA) is the perfect solution for small businesses that want to automate routine IT tasks.

What Is IT Process Automation?

IT Process Automation (ITPA) tools help to automate and improve IT operations such as application monitoring or service desk and infrastructure management. To do that, these automated processes monitor applications in real time so you can focus on what matters most –your company’s business needs. ITPA is a solution that facilitates the orchestration and integration of tools, people, and processes through automated workflows.

IT Process Automation Basics

ITPA solutions can be useful tools for IT departments. These systems function as an action-reaction system and require some planning from your IT department before they will work properly in your organization’s environment.

These trigger events can be:

Routine events

Such as upgrade controls and daily checks

Frequent technical issues

Such as system errors and bugs

Specific workflows

Such as forwarding concerns to the service desk

The IT department would be ready to use their ITPA solution after these triggers.

The steps for the ITPA solutions are:

Monitoring

ITPA tools help with monitoring system performance metrics including application usage and computer resources for efficiency purposes. The default function in most cases is tracking business applications by automatically continuing this step until a predefined trigger is detected.

Trigger

ITPA tools help identify these trigger events and start an actionable workflow. This might include tasks such as fixing technical issues in the system or simple business needs like backing up files on a weekly basis.

Reaction

The ITPA tools are triggered by a pre-defined event. The ITPA software then performs the reactive task according to the predefined event. The reactions might include restoring system performance or directing workflows towards service desk support.

IT Automation Examples

The benefits of using an ITPA solution is that it can manage a wide range of IT operations automatically and speed processes with fewer errors.

Here are some use cases:

· Automatically managing service requests

· Routine IT tasks are automated

· Asset management is automated

· IT-related onboarding and offboarding tasks are automated

· IT security and compliance are automatically managed

· Automating the digitizing process and digital transformation support

Differences Between ITPA and RPA

IT Process Automation (ITPA) and Robotic Process Automation (RPA) both interchangeably can be seen as a set of shared technologies with many different use cases. However, while they do share some similarities in their focus on automating business operations, there is still much difference between these two solutions.

RPA, or robots for automated tasks, is a technology that lets low-skill, repetitive work be done with no errors. The name RPA originates from its ability to mimic humans and performs these less complex jobs using machines instead of people. It mostly focuses on automating simple office functions like data entry into databases. On the other hand, ITPA solutions are used to streamline specific IT problems. These are typically handled by technically savvy IT teams.

Conclusion

Some of the benefits of an IT Process Automation solution are cost savings, increased productivity, fewer errors, and an improved work experience. The result is that a smaller IT team can effectively manage the routine IT operations of the department.

Big Data analytics is the process of collecting, organizing, and analyzing large amounts of disparate, unstructured data to discover patterns, insights, and other useful information. The power behind this technique comes from its ability not just to find insights but also identify which bits are important for your business needs.

Big Data Requires High-Performance Analytics

Big Data analytics is a collection of tools and applications that work together to help analyze large volumes of data. The process may involve predictive modeling, mining documents for trends or patterns, forecasting future events based on past performance with respect to similar occurrences, etc. These different functions are typically performed by specialized software but can also be done manually if necessary.

Using Big Data tools and software enables an organization to process large volumes of data that a business has collected. Then they can determine which is relevant. This will enable them to make better decisions moving forward with their enterprise.

Some Challenges For Big Data Analytics

For most organizations, Big Data analysis is a challenge. Consider the enormous volume of data and different formats that are collected across an entire organization. Additionally, it must be combined with other types for comparison or analysis

The first challenge of Big Data is in breaking down silos. This enables accessing all the data an organization stores across different places and often within multiple systems. A second difficulty lies with creating platforms that can pull unstructured information as easily structured electronic ledger books are ingested. Structured data would be using traditional database or software methods for processing large volumes.

Some Uses For Big Data Analytics Today

With the improvement of the technology that helps break down the data silos necessary for intelligent data digestion and analysis, businesses themselves can be improved.

With today’s advancements in Big Data analytics:

· Researchers can decode human DNA speedily

· Authorities can predict where terrorists plan to attack

· Health professionals can determine which gene is probably responsible for certain diseases

· Which ads people are most likely to respond to on Facebook.

Some Benefits Of Big Data Analytics Today

Enterprises are increasingly looking to find actionable insights into their data. With the right Big Data analytics platforms, an enterprise can boost sales and increase efficiency while improving operations, customer service, and risk management efforts.

Big Data analytics are used in these common ways:

Artificial Intelligence (AI) and Machine Learning (ML)

Collections of information, such as statistics or images, can be learned when large amounts of data sets are provided to intelligent systems with advanced software.

Customer Relationship Management (CRM)

With the largest amount of data provided for successful analysis, enterprises can provide customers with improved solutions and ideal service.

Risk Management

With the analysis of vast amounts of data regarding multiple business operations, past risks can be learned, and future ones prevented.

Conclusion

Big Data can be a goldmine of valuable and actionable information. But it is challenging to analyze because of the nature of the unstructured data that comprises Big Data.

The Content Analytics Platform (CAP), developed by Scion Analytics, can help with the analysis of some of Big Data. The CAP can quickly shred documents and rearrange the contents into an actionable, structured format.

Data scientists are the nexus of data and insight. They use a wide range of skills to sift, process, and analyze large volumes of structured or unstructured information. These data could be gathered from many sources such as social media posts, customer interactions, etc. These are used to create models with visual representations for patterns or trends within it. These include identifying anomalies when they occur.

Big Data is an integral part of modern life. It influences everything from what you search for on Google to how your favorite TV show unfolds. Now, Big Data has found its way into innovation with scientists playing a more important role than ever before.

What Does A Data Scientist Do?

Data scientists are valuable assets in any business, as they have the power to turn raw data into something that is useful. Data analysis can help decision-makers understand trends and make plans accordingly for future investments or market expansion.

For example, a budget meeting may rely on data scientists to provide insights into consumer habits. Data science professionals are often hired for their specialty, which may be Machine Learning (ML) or Artificial Intelligence (AI).

Necessary Skills For The Data Scientist

Data scientists are responsible for using data to solve problems. With an ever-growing world of information at their fingertips, it is no wonder that they are in high demand. Data miners use complex algorithms and mathematical models so statisticians can find insights from large sets of collected numbers. They need to be fluent with programming languages like Python, C, or Java, which enable incredible productivity when coding database applications.

Data Scientists must excel across math and statistics studies as well as computer science. He must be knowledgeable about compiler tools such as Ruby on Rails among others.

Data scientists must have a variety of skills for them to be effective at their jobs. These include excellent communication and technical knowledge, as well an understanding of software development techniques such as data mining or statistical analysis.

The Data Scientist Tool Set

Data scientists are using database management and visualization systems to control their data. These include Tableau, Microsoft PowerBI, and Qlik. Cloud computing is a major tool for data scientists, who use it to store their valuable and evolving work. Data Scientists frequently rely on cloud-based tools like Amazon Web Services or Microsoft Azure.

Data scientists may use simple spreadsheet tools when working with small amounts of data, though they have limitations compared to a full database management system.

Data scientists have been using NoSQL databases to create dynamic schema. These tools can help them work more efficiently and effectively with unstructured or hierarchical data, which might not be suited for a traditional database scheme.

Data Scientist Vs. Data Analyst

Data scientists and analysts both play an important role in the company they work for. Data scientists ask more questions about the data, whereas data analysts merely find solutions to someone else’s problems.

Data scientists are the brains of any machine learning operation. They are usually responsible for finding, aggregating, and contextualizing data that is used by algorithms to produce predictive models.

Data scientists are the ingenious minds behind our understanding of how to use data. They find more information that can be used for businesses to make better decisions. Data analysts focus on analyzing what has already been collected and drawing conclusions about it from there

The two different types also bring their own skillsets, which help us understand why they are key players within an organization. One kind specializes only at finding new sources or contextualizing business considerations. The other might specialize in gathering all sorts of possible details with little regard towards its application.

Your data is valuable, and you don’t want to share it with just anyone. The solution? Privacy Enhancing Computation (PEC). It is a way for different parties to extract value from information without exposing themselves or their other datasets in return. They collaborate on an individual level using the actionable data without any shared sensitive information between the participants.

Personal data, like your bank account number or social security number, are not available to the public. The way that privacy-enhancing computing creates private datasets for individuals is by using strong encryption methods in combination with other technologies.

Several techniques combine to make up the privacy-enhancing computation. These may be included:

Zero-Knowledge Proofs

Zero-knowledge proof, or zero-knowledge protocol, is used when one party shares true information but only shows what is true without revealing anything else about it.

Multi-Party Computations

Secure Multi-Party Computation (SMC) has the goal for people to work together in computing functions over their inputs without revealing them individually. There is shared computational operations among all parties, but no single party can know what was done by any other at any time during processes.

Homomorphic Encryption

Homomorphic encryption is a new way to protect data that can be used in computing. Parties perform computations on encrypted information that remains all the while encrypted.

Differential Privacy

Differential privacy is a system that allows information about datasets to be shared while still protecting the identities of individual members in each group.

Trusted Execution Environments (TEE)

A trusted execution environment is a secure area of your main processor that ensures code and data loaded inside are protected with respect to confidentiality, integrity.

Privacy-Enhancing Computation Examples

Here are some key uses for Privacy-Enhancing Computation (PEC):

HR

The use of PEC in the Human Resources Department can be in facilitating gender equality and reducing the gender pay gap in the workplace.

Fraud Prevention

Fraudsters are known to victimize certain industries and multiple companies in that industry. Companies can work directly together using PEC to detect the criminals quickly. Also, the good customers can be identified when they collaborate to establish a pool of trusted consumers.

Medical Research

In a pandemic year, it is understandable why the medical communities need to draw large amounts of data, even across borders and laws, for research. Patient records are rightly protected by many regulations. The PEC process makes actionable patient information both accessible and private.

Internal Data Analysis

PEC methods can help large corporations obtain and share information, even between many brands and across borders and policies, while maintaining regulated privacy.

Privacy-Enhancing Computation Benefits

· Ever-Tightening Protected Data Can Be Safely Accessed

Technology leaders of organizations can be spared the difficulties of ever-tightening privacy laws while still being able to access most of their data. PEC methods can make the most of data availability better than the ineffective “anonymizing” method.

· Consumer Data Need Not Be At Risk

By using PEC techniques, consumers can be spared the potential risk of violated privacy due to ineffective methods of shared information with business needs.

Conclusion

Every day, the data gathered from social media to bank accounts on the Web continues to grow enormously. When consumers provide their personal information for goods and services, they expect their information will be protected by these organizations. They want to remain anonymous. But companies want actionable business information to help grow their businesses. Fortunately, there are methods to make use of all this consumer data in secure ways with the techniques of Privacy-Enhancing Computing (PEC).