How Can Your Business Benefit from Fog Computing?
How much data do we create every day? The World Economic Forum reports that the entire digital world is expected to reach 44 zettabytes by 2020. So, each day, we witness the colossal growth of data and this pace is only increasing with the growth of IoT. The agility and flexibility of big data applications are the foundation of the Internet of Things (IoT). The escalation of IoT has resulted in an increased volume of digitally generated data and managing that data has become a major challenge. This has led to the emergence of fog computing – an answer to the new challenges of computing technologies.
Read more: Gearing up for IoT in 2020
Defogging The Term Fog Computing
Let us start by defining it.
What is fog computing?
Fog computing is a decentralized computing infrastructure in which computing resources such as data, computers, storage, and applications are located between the data source and the cloud. This term refers to a new breed of applications and services related to data management and analysis.
According to Mung Chiang, Dean of the Purdue University, “fog provides the missing link for what data needs to be pushed to the cloud, and what can be analyzed locally, at the edge.” In simple terms, fog computing is a distributed network fabric that stretches from the outer edges of data creation to the point of storage.
Are fog computing and edge computing the same?
Edge computing is a subset or a component of fog computing. For example, if fog computing is compared to a basket of various fruits, edge computing would be one fruit from a single variety.
Edge computing refers to data being analyzed locally, at the point of creation. Fog computing encapsulates edge processing as well as the network connections required to bring that data from the edge (point of creation) to its endpoint.
Evidently, fog computing and edge computing are complementary.
Difference between fog computing and cloud computing
Just as the literal fog is a cloud closer to the ground, fog computing is stationed as a layer to reduce the latency in hybrid cloud scenarios. Cloud computing forms a comprehensive platform that helps businesses with the power to process important data and generate insights. Fog computing is like the express highway that supplies computing power to IoT devices which are not capable of doing it on their own.
How Does Fog Computing Work?
Fog computing uses the concept of ‘fog nodes.’ These fog nodes are located closer to the data source and have higher processing and storage capabilities. Fog nodes can process the data far quicker than sending the request to the cloud for centralized processing.
The cloud is getting cluttered due to the enormous number of devices connecting to the internet. Since cloud computing is not viable in some cases, it has become necessary to use fog computing for IoT devices. It can handle the enormous data generated by these devices.
When implemented, fog-empowered devices locally analyze time-critical data that includes alarm status, device status, fault warnings, and so on. This minimizes latency and prevents major damage. Fog computing can effectively reduce the amount of bandwidth required, which in turn speeds up the communication with the cloud and various sensors.
Fog computing example:
If a user with a hand-held device wants to review the latest CCTV footage from a locally positioned IoT security camera, he would need to request the stream from the cloud since the camera does not have storage. This could take a bit of time, which can be eliminated with fog computing, where a local fog node can be accessed for video streaming which is far quicker.
Step-by-step Fog Computing Process:
- Signals are wired from IoT devices to an automation controller which executes a control system program to automate those devices.
- A control system program wires data through a protocol gateway.
- Data is converted into a protocol such as HTTP so that it can be understood easily by internet-based services.
- A fog node collects the data for further analysis.
- It filters the data and saves it for later use.
Key Takeaways for Your Business
- Increased business agility: It is evident that fog computing is cost-effective because it makes the production of revenue-generating products and services more efficient. It accelerates rollout cycles, broadens revenue bases, and reduces costs.
This revenue stream creates value for IoT fostering highly functioning internal business services. Fog computing also provides a common framework for seamless collaboration and communication helping OT and IT teams to work together to bring cloud capabilities closer.
- Privacy control: Fog computing facilitates better control of privacy because you can process and analyze sensitive data locally instead of sending it to a centralized cloud for analysis. It also enables the IT team to track, monitor, and control any device that collects or stores data.
- Data security: Since fog computing allows you to connect multiple devices to a network, it helps identify threats such as potential hacks, or malware. Additionally, such identified threats can be curbed at the device level without risking the entire network.
The Future is Fog Computing
Fog computing has several advantages over cloud computing. Fog computing can boost usability and accessibility in various computing environments. Soon, cloud computing for IoT may fade away but fog computing will take over. IoT is seeing an impressive growth rate and so it needs a special infrastructure base that can handle all its requirements. Fog computing is the key to accomplish this critical work. So get in touch with us and let’s get this happening for your business.
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Facial Recognition Technology – What’s In Store For The Future
When Facebook started automatically tagging faces in uploaded images, nobody realized that this facial recognition technology would hike up to tracking people down while walking on the streets. In the past several years, this disruptive technology has gained immense popularity, that it is now used everywhere, from airports to shopping centers, to law enforcement. With its growing predominance in national safety and security, the face recognition market is estimated to reach USD 11.30 Billion by 2026.
“Facial recognition has been around for a long time—like the 1960s. Perhaps the father of facial recognition, Woodrow Wilson Bledsoe, an American mathematician and computer scientist who classified photos of faces all by hand, (RAND tablet), even he might have been alarmed at how facial recognition technology is supercharged today by advances in computing power, 5G speeds and AI paired with machine learning.” – Tamara McCleary, CEO of Thulium, and a unique advisor to leading global technology companies such as SAP, Dell, Oracle, IBM.
Moreover, the advancements in artificial intelligence and machine learning are bringing about an active expansion to this technology. It won’t be long when the automation of facial recognition technology will fundamentally change the way we do many things. However, many minds still doubt on the path this revolution is leading to.
Let’s dig deeper into the advancements of the facial recognition technology, what it holds for the future and whether it’s completely safe to rely on such a disruptive technology that fiddles with personal identities.
Facial Recognition Technology In-Depth
So what is facial recognition technology and how exactly does it work?
Facial recognition is a biometric technology that utilizes unique facial features to recognize individuals. Today’s plethora of innumerable photos and videos make the dataset for this technology to work. Through artificial intelligence and machine learning capabilities, software mathematically maps distinguishable facial features, to compare patterns in newly available images with visual data stored in the database. Such a recognition process allows the simple unlocking of phones to security checks at airports.
In a way, artificial intelligence plays a vital role in the complete identity recognition process. A branch of artificial intelligence known as computer vision works through measuring nodal points on a face to make a face-print. This faceprint is a unique code that is applicable only to a particular person. This enables identification.
“I believe AI in Facial Recognition could add great value to society but we have to be careful to use clean data and we have to educate the public for the need for good, clean, accurate data to be sure we do not accidentally disenfranchise certain groups even more in the future. We must assure the data does not include unconscious bias or even deliberate bias programmed into the code. It is also important to note that we have a major lack of data for many disenfranchised groups including the community of persons with disabilities.”– Debra Ruh, CEO, Ruh Global IMPACT, Global Disability, and Aging Inclusion Strategist.
Once this faceprint is made, the technology runs through an identity database to match this face with a name and other required details. Thus, the probability of error is near to rare; maybe an eight out of 1000 scans could mistakenly identify the person. This is what makes this technology an excellent prospect for performing crucial functions.
Innovative Uses Of Facial Recognition Technology
As the facial recognition technology evolves with time, few industries and countries apply the technology in innovative ways.
China is rising to be the leader in facial recognition technology. Although part of the technology remains a perspective, its innovative use is what amazes the audience. A few other countries following the trend are Japan and the United Arab Emirates. The US doesn’t stand back either. Look at these impressive ways of face recognition technology implementation.
- Face recognition is on its go, to replacing cash and credit cards. At fast-food units like KFC, customers can just smile into a self-serve screen to automate the identification and withdrawal of cash from banks. Some banks are also allowing customers to use face recognition instead of bank cards.
- The automobile brand Subaru has integrated facial recognition cameras to its Forester brand of SUVs. This is intended to detect when a driver is tired or about to sleep to take necessary actions to prevent accidents. This indeed is a tremendous innovation towards road safety.
- The 2020 Tokyo Olympics, is reported to make use of facial recognition to boost their security systems. Instead of relying on ID cards that have a high probability of being fake, the authorization is now implementing the FR technology to allow media, competitors or other such people to enter the premises.
- Dubai Airport also makes use of the FR technology to strengthen their security. A virtual aquarium fitted with 80 facial recognition cameras examines every passerby to easily recognize criminals or offenders. Also, police cars are on their go-to implement FR cameras to identify criminals and wanted vehicles quickly.
- The facial recognition technology is no doubt making a great impact on national security systems, promising a safe and crime-free future. The US government is also making use of biometric exits and AI cameras to track people crossing their international boundaries without proper documents.
The Growing Concern
Though face recognition technology offers innovative and impressive use cases in security and surveillance, there are numerous challenges that it faces. Privacy being a major concern, not everybody is happy with the storage of sensitive and personal data. A potential downside of this technology is the data and privacy breaches. The databases containing facial scans and identities are being used by multiple parties such as banks, police forces, and other defense firms and are hence prone to misuse.
Considering the face recognition tech as a threat to their citizens’ privacy, many cities including San Francisco, Massachusetts, Cambridge, and others are planning to put a complete ban on real-time face recognition surveillance.
“Concerns around AI’s practical applications like facial recognition have begun crystallizing over the last few years and will continue unabated. Current AI-based face recognition systems possess a grave threat to individual privacy, which if unregulated may end up jeopardizing sensitive user data to the wrong hands in times to come.” – Varghese Samuel, CEO & MD Fingent.
Moreover, how much can this technology eliminate crime is still being discussed. The accuracy of the system in detecting people who cover their faces from cameras or disguise themselves is yet a topic of dispute. However, to everyone’s relief, the technology is showing constant improvement in this matter. According to the U.S. National Institute of Standards and Technology (NIST), facial recognition systems got 20 times better at finding a match in a database over a period that covered 2014 to 2018.
“Artificial intelligence has made great strides, but still has a long way to go. It is powerful to use on a daily basis, when the stakes are low (for example, in tagging photos or recommending advertisements), but not yet trustworthy enough to stand fully on its own in high-stakes applications, such as driverless cars, medical diagnosis, and face recognition, where errors can deeply affect people’s lives. – Gary Marcus, Founder and CEO, Robust.AI Professor Emeritus, New York University, Author of book: REBOOTING AI
The Untold Future
It is pretty much tough to predict where the facial recognition technology would be in the coming years, but the increase in AI advancements is sure to widespread this technology around the globe. Major industries have already capacitated the FR capabilities to replace the traditional process of paying bills, opening bank accounts, checking controls at airports and such. A few of these industries include healthcare, retail, marketing, and social media platforms.
In a nutshell, face recognition technology is expected to predominate the globe in the near future. The increasing usage of mobile devices and demand for robust fraud detection and prevention is predicted to majorly drive the implementation of this technology. As per the predictions made by Markets and Markets, a prominent research firm, the global facial recognition market size is expected to grow from USD 3.2 billion in 2019 to USD 7.0 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 16.6% during 2019–2024.
“The more people grow accustomed to using facial recognition products and services that enhance efficiency and that can, at the moment, seem altogether too fun or mundane to be harmful — whether it’s tagging photos, unlocking a phone, or projecting how your face might look in the future — the more facial recognition technology becomes normalized.”– Jarno M. Koponen, Head of AI & Personalization at Yle News Lab. His work has been covered by The New York Times, New Scientist, Oxford Reuters Institute, Mashable, TechCrunch.
Face recognition technology is revolutionizing the world more than you think. It’s time to figure out how this technology could bring added value to your firm. Contact our experts today!
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6 Chatbot Security Practices You Need To Implement
According to a survey by Oracle, regarding the benefits of using chatbots for their consumer-facing products, which included responses from 800 decision-makers, including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK, it was found out that “80 percent of companies wanted to have some type of chatbots implemented by 2020!
It is also forecasted that 90% of bank-related interactions will be automated by 2022. Moreover, 80% of businesses will have chatbot automation implemented by 2020. Also, 47% of consumers would buy items from a chatbot when 28% of top-performing companies are already using AI for marketing! With chatbots turning into the trend, it is vital to implement chatbot security measures.
A Back Door Open To Hackers
Chatbots are nowadays mostly used in industries such as retail, banking, financial services, and travel that handles very crucial data such as credit/debit cards, SSN, bank accounts, and other Sensitive PII (Personally identifiable information).
The aggregation of such data is crucial for the chatbot to perform. Thus, it is required that chatbots are not vulnerable to be exploited by any hackers.
A recently released report from MIT Technology Review and Genesys showed that 90% of companies are already using AI strategies to increase revenue. The research also found that on average, between 25% and 50% of all customer queries can be solved through automated techniques. This has made it easier than before to handle complex tasks.
Related Reading: Read on to know more about the top AI trends of 2019.
The HTTPS Protocol For Security Of Chatbots
HTTPS protocol is the basic and default setting required for a good security system. The data that is being transferred over the HTTP via encrypted connections are secured by Transport Layer Security (TLS) or Secure Sockets Layer (SSL).
Related Reading: Check out how Fingent helped create an enhanced and engaging learning experience through chatbots.
Types of Security Issues
Security Issues fall into two main categories:
Threats are usually defined as different methods by which a system can be negotiated or compromised. Threats can include incidents such as Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privileges, and many other threats.
Vulnerabilities are defined as methods that a system is compromised and cannot be identified and solved correctly and on time. A system becomes open to attack when it has poor coding, lax security, or because of human errors. The most effective way to solve the issues of a possible vulnerability is to implement SDL (Security Development Lifecycle) activities into the development and deployment methods.
As per the study by the Ponemon Institute, In 2017, the average total cost of a successful cyber-attack was over $5 million, or $301 per employee!
Here are 6 chatbot security issues that you need to consider right away:
Data while transit can also be misused. There exist different protocols that provide encryption, while addressing these problems of misuse and tampering.
According to article 32 (a) of the General Data Protection Regulation (GDPR), “it is specifically required that companies take measures to de-identify and encrypt personal data. So, chatbots have access only to encrypted channels and communicate through those”.
For instance, Facebook Messenger introduced the new feature called “Secret Conversations” that enabled end-to-end encryption based on Signal Protocol.
2. Authentication and Authorization
Authentication is performed when the user needs to verify their identity. This is often used for bank chatbots.
Generated authentication tokens verify data that are requested through a chatbot. On completing the verification of the user’s identity, the Application produces a secure authentication token, along with the request.
Another step of security measures is an authentication timeout. The token generated is used for only a certain amount of time, after which the application has to process a new one.
Two-way verification is another process where the user is asked to authorize their email address or to receive a code via SMS. This is a crucial process which is necessary to verify that the user of that account is the real user that is using the chatbot.
3. Self-destructing Messages
When Sensitive PII (Personally identifiable information) is being transferred, the message with this data is deleted after a definite period of time.
Personally identifiable information (PII) is any data which can be used to identify a particular person. It includes records such as a person’s medical, educational, financial and employment information. Examples of data elements that can identify and locate an individual include their name, fingerprints or other biometric (including genetic) data, email address, telephone number or even their social security number.
This kind of security measure is crucial when working with banking and other financial chatbots.
4. Personal Scan
When working with personal data, it is necessary to take security precautions and measures.
Apple was the first company that added finger authentication to their iPhones. This technology is now being used widely to verify an individual’s identity. This is performed when initiating a transaction or when you want to access your bank account using a chatbot that a personal scan is required.
5. Data Storage
Chatbots are effective because they retrieve and store information from users.
For instance, if you have a chatbot that performs online payments, this can mean that your clients are providing their financial information to a chatbot.
The best solution in this situation is to store such information in a secure state for a required amount of time and to discard these data later on.
Some other concerns are the following:
- Biometric authentication: Iris scans and fingerprint scans are popular and robust.
- User ID: User IDs involve processing secure login credentials.
- Authentication Timeouts: A ‘ticking clock’ for correct authentication input. This prevents giving hackers an opportunity to guess more passwords.
- Other strategies could include 2FA, behavior analytics, and kudos to the ever-evolving AI trends.
6. Tackling Human Causes
The one and only other factor or cause that cannot be altered is the human factor. With commercial applications in specific, that chatbot security and end-user technique have to be resolved. This will ensure the chatbots from being vulnerable to threats.
Related Reading: Find how artificial intelligence can drive business value.
To know more about secure bot building, get in touch with our IT consultants today!
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What seemed to be a far-fetched idea or an unclear, undefined entity, a few years ago, is what is ruling the world of business today. Enterprise mobility.
People are all for the concept of “bringing their own devices” at their workplaces these days. More than 60% of workers have access to their company data or work through their smartphones and tablets. Interestingly, only about a third of business enterprises have any kind of BYOD (Bring-Your-Own-Device) management strategy in place.
“People are bringing their own devices, but in many cases, they and their companies are not taking care of those devices and the applications on them appropriately.”, says Richard Absalom, consumer impact technology analyst at Ovum.
It is very important to have a proper Enterprise Mobility Management strategy for every organization, especially since most of them are spending a pretty significant amount on mobility.
Here is what you can do to better manage enterprise mobility.
Any BYOD policy has to be developed with inputs from all over the organization. Driven by the CIO, BYOD management efforts should involve everyone from developers to users to the IT team. Even the human resource and legal departments have to be included. As it means an entire culture change in the organization, it necessitates the need for everyone to be on the same page, about what can and cannot be accessed over personal devices.
The use of mobile devices automatically implies the use of various technologies offered by them such as Global Positioning System (GPS) receivers, cameras, audio recorders and other sensors. Widespread use of such technologies across the organization may cause loss of data and even loss of important intellectual property to be looming concerns. Imagine the consequences of loss of an in-house video of an application’s development processes! Worse yet, are the legal implications of the same, or of other videos involving the organization’s officials in compromising positions. Hence, BYOD policies should be formulated after taking into consideration these aspects as well.
An Exit or Loss Policy
While devising BYOD policies, something that people generally tend to miss out, is an exit strategy. When an employee in any department of an organization leaves, he will be carrying a lot of information concerning his department on his smartphone, with him. This is a huge loss for the organization.
“When an employee leaves, say in sales, and they take all of the contacts on their personal phone, that is a big corporate asset that goes missing.”- Absalom
Hence, you need to develop appropriate theft, loss as well as exit policies. Along with technical issues you need to raise the security stakes. You need to find a balance with all these features and risks, so as to protect your employees’ personal information as well as your business reputation.
Usage of insecure Wifi networks might need separate provisions in a BYOD policy since their security measures are very limited. For example, some WiFi networks may be labeled as “off-limits” on the basis of security alerts. Devices used in an enterprise should be protected against possible loss of data and attacks. An organization has to make sure that all the personal devices are well in line with the enterprise security standards. Encryption and access control are ways in which valuable corporate data residing on any device can be protected.
Credentials for users, such as usernames and passwords, need to be created securely with utmost care. Credentials which may be sufficient for certain kinds of applications may not be suitable for other kinds of applications that need more security. Short number strings for example,while may be appropriate authentication for a user on game leaderboards and scoreboards, they won’t be enough for a social networking application.
All of these practices call for crisp and clear policy guidelines. Their compliance needs to be made mandatory as well. You need to make sure that all your employees are well aware of the rules regarding joining, leaving or altering their role or participation in a BYOD initiative. Signatures on policy agreements need to be made compulsory. Absalom also said that it would be good to have all employees agree to legally upholding their policies and to getting their devices locked on events of it being stolen, lost or compromised in any way.
Hence, if well managed, enterprise mobility can be the best thing that ever happened to your business. Keep the above pointers in mind, embrace mobility and manage it efficiently.