Cybersecurity Attack Vectors

Cybersecurity Attack Vectors

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 This could be computers running outdated versions of Windows that have never been patched, routers without up-to-date firmware downloaded and installed, which leaves users vulnerable.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.

Cybersecurity Awareness

Cybersecurity Awareness

Cybersecurity awareness training is a great way to inform employees about the attack surfaces and vectors in their company. Cyberattacks plague businesses, meaning hackers are always on the lookout for ways into your networked devices so they can plunder valuable information from within. Putting together thorough cybersecurity training will prepare all workers who use networked resources with the knowledge of how best to protect themselves against these threats. This will prepare them to become cautious when handling sensitive data while online as well as offline.

Cybersecurity For Employees

A minimum amount of phishing and cyberattack methods should be known by employees:

Suspicious Emails

Alluring emails that have a link that, if opened, download malicious software.

Any Links

It is possible that any link, familiar or unfamiliar, may download malicious software. They may take you to a familiar site that poses for the attacker trying to get you to log on there.

Strange Phone Calls

Attackers try to use voice phishing, or “vishing,” to get you to provide sensitive information over the phone. Most legitimate companies will not ask for such information over the phone, if not initiated by the customer.

Strange Texts

Attackers try to get you to click on malicious links on your mobile device using SMS phishing, or “smishing.” Attackers can gain access to your device’s network or even a company’s network.

Employees’ Precautions

Security precautions vary between organizations, but a good starting point is a thorough security policy that involves personal devices. If employees are allowed to use their own phones for work or download company apps on them, then they should also be trained in proper cybersecurity procedures such as these:

· Passwords on employees’ personal devices or accounts

· Using password-protected wireless networks, or private networks only

· Avoid using third-party apps if necessary

Cybersecurity Awareness Methods

A key part of cybersecurity is remembering that not everyone in an organization has the same level or type of access. Effective training helps employees think differently, which can help them detect malicious activity faster and protect your company from potential data breaches.

Cybersecurity is not just about protecting yourself as a user, it includes understanding who else could put you at risk. This could be both internally (elderly relatives with dementia) and externally via internet connections, for examples.

Regular Notifications

Even simple messages from IT personnel can make employees more aware of threats and other outside attempts.

Penetration Testing

Penetration testing is an important step for any company that wants to keep its employees safe. Third-party organizations can send phishing emails for testing. The business can use this information about how many clicks their links got in order to address any weaknesses or mistakes they made themselves.

Cybersecurity Training Software

Training platforms are available for organizations, offering information and courses that cover topics like compliance and phishing techniques. Some may focus more on informative videos. Others offer entertaining interactive security trainings software to raise employee awareness about the many attacks they see in their business. This could help them spot its weaknesses by themselves.

Organizations can now access a training platform where it offers various learning modules. These could include instructional material or documents pertaining specifically towards an industry vertical. A banking sector might have different subjects compared to a law firm.

Here are the top security awareness training companies:

· Infosec

· KnowBe4

· Webroot

· Barracuda Networks PhishLine

· HoxHunt

IT Process Automation

IT Process Automation

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.

What Is Data Science?

What Is Data Science?

Data science is the study of data using advanced tools and techniques to extract information and detect patterns. These findings can be used by organizations for decision-making. Data science is a hot new area in the field of computer science. It uses Machine Learning (ML) techniques to identify patterns and trends from large amounts of data.

Why Use Data Science?

Data science was born from a need to analyze unstructured data, which is data that does not neatly fit into a spreadsheet or database. Insights and trends collected can inform considerations and decisions, as well as offer solutions for future problems in society. For example, finding more sustainable energy sources or curing diseases like cancer.

Structured data that filled rows and columns like Excel or Google Sheets was easy for entering, searching, comparing, and extracting the past. The 1990s brought a move to predominantly unstructured data. These include business documents, emails, social media, customer feedback, webpages, open-ended survey responses, images, audio and video recordings. There is so much information being generated from diverse sources. Without structure, it would be nearly impossible to either organize it or derive valuable information from it.

Data Scientist Vs. Data Analyst

Here are the major differences between the data scientist and the data analyst:

The Data Scientist

· What is the data?

· Focused on Machine Leaning (ML) and algorithms

· Developing operational models

· In-depth programming knowledge

The Data Analyst

· What does the data tell us?

· Focused on business administration

· Pre-processing and data gathering

· Scripting and statistical skills

How Is Data Science Used?

Data science techniques are becoming increasingly popular because they help decision-makers make better decisions. For example, data scientists can be used to create a product that is more profitable or optimize how you communicate with customers. They can do this by changing email marketing campaigns so that it has more call-to-actions (CTAs).

Data Science Examples

Text Analysis

Text analysis is the process of automatically classifying and extracting meaningful information from unstructured text. It involves detecting trends, patterns to obtain relevant insights in seconds with just one click!

Text analysis is the method of analyzing unstructured and semi-structured text for business insights. It can be used to gain a better understanding about your customers.

Mention Mining

The industry of data mining is lucrative, and it is used in many ways The practice has been to take information from platforms like Facebook, Tik Tok, or Instagram that can tell you what people are interested in. One way that businesses make money through data mining is by gathering user behavioral patterns found in social media posts about topics such as politics, current events, beauty products and services, etc.

Organizations, brands, and products can benefit from social media mentions. With data science applied, text analysis along with Machine Learning (ML) is used to identify key words for digital marketing strategies.

Biometric Analysis

Biometrics and other sensor data are used to authorize smartphone users. Without data analysis, evaluating all images, videos, and biometrics captured on your smartphone would be difficult due to the identity protections that are in place.

Big Data Analytics Guide

Big Data Analytics Guide

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.

What Is Big Data?

What Is Big Data?

In today’s world, Big Data is a phrase often used to describe the volume of both structured and unstructured data. It can be difficult for traditional databases or software systems. When you’re trying to find specific information, the sheer volume of data can be daunting.

Big Data is a phrase often used meaning both structured and unstructured pieces that make up our world today. From transactions on websites or leads collected by sales representatives or every document created in an office, this data adds up quickly.

Big Data is captured, formatted, stored up for analysis. It gives companies insight for concerns such as increasing revenue opportunities and retaining customers or improving operations.

History Of Big Data

The term “Big Data” was first used in the 1990s, but usage had an uptick because data management involved databases and analyzing structured information. In 2000, with heavier Internet usage came about a multiplicity of sources for collecting data such as Web traffic or e-commerce transactions. These sources of data have made it necessary to collect more than before during this decade.

Importance Of Big Data

Data is the currency of today’s business world. It makes companies money by revealing details about every aspect of their operations with customers, products, and marketing data. It helps with office or store layouts for better efficiency.

But Big Data is hard to sort through because there is so much of it. It can also be challenging for researchers and analysts alike, trying to pick out the important information from all that noise in order get their hands on relevant insights.

The right tools and methods are needed that will organize this information into something meaningful enough for decision-making purposes by providing accurate relevant data. Information that leads towards better organizational decisions can be made in companies if they have access to such tools.

Big Data is subject to many legal requirements. Companies are required to comply with data protection regulations, which protect customers’ personal information. The rules set forth by these laws ensures that business operations stay within compliance, even when dealing only internally between employees of different departments. Those guidelines include GDPR and CCPA (both European Union related). These combine strict tracking on who accesses any given piece of information while also limiting total exposure.

Storage Of Big Data

Big Data storage solutions include:

· Databases

· Data warehouses

· Data lakes

· Public cloud

· Private cloud

· Hybrid cloud (combination of public and private clouds)

· On-premises servers

· Disk arrays

· Sold state drive arrays

Big Data Uses

All enterprises that have both large volumes of data stored and incoming can be said to use Big Data. But the degree of usefulness depends on how it is managed.

Software companies and analytics providers themselves use Big Data to power their products. They also provide these services for other businesses as well.

Big Data analytics is used to better understand the security of networks and systems. This includes noticing any anomalies or threats, recording that information for improved threat intelligence.

What Is A Data Scientist?

What Is A Data Scientist?

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.

Hyperautomation Pros And Cons

Hyperautomation Pros And Cons

In today’s economy, organizations need to maximize productivity and minimize expenses for them be successful. Hyperautomation can help you get ahead by maximizing your efficiency with technological advancements that allow for increased automation.

Today’s businesses are constantly striving to find new ways of streamlining their processes to remain competitive. One way that this is being done is employing hyperautomation technology. This uses automation tools for repetitive tasks without the need of manual intervention by human workers.

The use cases include everything from manufacturing lines all the way to administrative workflows such as data entry and invoicing customers. This eliminates anything involving people.

Hyperautomation uses Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) to transform either modern or legacy processes. Hyperautomation can help an organization gain cost efficiencies that enable them to thrive in the current landscape of competition. This competition has been more evident lately as companies have started going headlong into adopting these technologies.

There are both benefits and some challenges that come with hyperautomation. Discussed below.

Hyperautomation Pros

Basic automation is enhanced with hyperautomation using advanced technologies such as AI and ML. Organizations are helped by hyperautomation in these ways:

· Many processes are improved with automation across the business

· With automating repetitive tasks completely and efficiently, workflow allocation is optimized

· Costs are reduced when manual intervention tasks are digitally automated

· Business agility is increased with quick, rich insights allowing more accurate decision-making

· Better products made faster with less inefficiency and human error

· With more data being captured, there are better decisions, better customer experience, and better business improvements from those data insights

· Less technical expertise is required to configure and manage automation solutions

Hyperautomation is a perfect solution for organizations with legacy operations or low automation levels. These companies can see real results through digital process and infrastructure automation to increase connectivity, agility, or efficiency of their business operations.

Hyperautomation Cons

Here are some hyperautomation challenges to be aware of:

· Not knowing how to measure the success of hyperautomation

· Developing and adjusting to a realistic project timeline

· Having end-to-end leadership throughout the project

· Calculating tangible and intangible ROI up-front accurately

· Selecting the best hyperautomation solution from a growing and evolving marketplace

· Gaining stakeholder buy-in for an unfamiliar project

Here are some suggestions to solve hyperautomation challenges:

· Identify and rely on the in-house experts if possible

· Manage the change proactively

· Garner stakeholders’ support

· Assuage the fears of employees who fear replacement

· Retain employees, if possible, to maintain a better knowledge base

· Identify barriers and risks

· Identify which processes can wait and which should be addressed early

· Provide risk-free ways to practice

Conclusion

Hyperautomation is the key to success in today’s highly competitive environment. Any business, regardless of its size and technological capabilities, can benefit from this technology. It helps automate processes quickly while staying on budget with an easy implementation process for most organizations.

Privacy-Enhancing Computation

Privacy-Enhancing Computation

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).

What Is The Internet Of Things?

What Is The Internet Of Things?

The Internet of Things (IoT) is a broad term that encompasses anything connected to the internet. More precisely, it is the connection of devices that “talk” to each other. IoT includes devices such as sensors, smartphones, and wearables with embedded technology. IoT is an ongoing trend in which everyday objects are being connected to the Web. Smart home devices like light bulbs, motion sensors, smart toasters, and even fitness collars for dogs are just some examples. These demonstrate how technology can be increasingly embedded into our lives before long. IoT can help people be more efficient when certain things need to be accomplished, saving time, money, and even fuel emissions.

Connected machines and objects in factories offer the potential for a “fourth industrial revolution.” Experts had predicted that more than half of new businesses would run on IoT by 2020.

By combining these connected IoT devices with automated systems, it can help someone accomplish a task or learn a process. This is done with gathered information that can be used to analyze for actions. For examples, using smart mirrors that monitor health and beacons placed in shops.

Why Do Connected Devices Need To Share Data?

Sensors on product lines can increase efficiency and cut down on waste. One study estimates 35 % of US manufacturers are using data from smart sensors. The company Concrete Sensors has created a device for inserting into concrete, which creates an easy way to monitor the condition of the construction material.

The Internet is an amazing tool that has allowed us to communicate with each other across the globe. However, not all things on this vast network should be connected. A recent argument against data collection in our everyday lives was raised by privacy advocates. They argued that we need protection from too much information being collected about individuals

without their consent or knowledge. Each device is useful data for a specific purpose for a buyer that can benefit the wider marketplace.

Where Does The IoT Go Next?

The Internet of Things (IoT) was created as a way for humans to become less dependent upon our physical environment when accomplishing tasks. Everything from electronics like smart appliances all the way down to items within closets could potentially interact wirelessly.

The need to secure every connected device by 2020 is “critical.” That was according to Samsung, who said that there is a very clear danger in technology running ahead of the game.

But Aren’t There Privacy Implications?

The Internet is an interconnected system of networks, computers, and devices. You would not think that one single piece could be so vulnerable to hackers, but it has happened many times. The company VTech saw videos and pictures taken without customers’ knowledge due to insecure systems on connected toys they made for children.

There is also the issue of surveillance. If every product becomes connected, then there is the potential for unbridled observation and data collection by third parties. This could lead to various negative outcomes such as targeted advertising or theft. As James Clapper said in 2016, “Intelligence services might use IoT devices for identification, surveillance monitoring, location tracking, and targeting.”

We Need Reliable Standards

The Internet of Things (IoT) is an exciting prospect, but it is not without its challenges. One significant issue at the center concerns incompatible standards for all these connected objects to be able to talk to each other. Running devices on differing standards causes inability to communicate with each other. Improved compatible standards will ensure that more things can communicate with each other and share what data they are recording.