Trending December 2023 # Guide To Hyperautomation In 2023: Technologies, Pros & Cons # Suggested January 2024 # Top 21 Popular

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Hyperautomation is a term initiated by Gartner. It is the application of automation technologies like RPA and process mining along with machine learning and other emerging technologies to increase the level of automation and digital transformation in companies.

Given the interest in RPA, traditional industry analysts want to capitalize on the growth of enterprise automation and they make up new terms as part of that effort. However, the fact that they made up this specific term also shows that companies are striving to achieve higher levels of automation and are not satisfied by current enterprise automation technologies.

For more on hyperautomation:

What is hyperautomation?

Different industry analysts prefer different names for the concept of increased enterprise automation. Based on all the definitons we see 4 aspects of hyperautomation in enabling higher levels of automation:

Use of existing automation/digital transformation technologies like RPA and process mining

Reliance on machine learning to automate operational decision making

Organizational and cultural change to drive fast experimentation and rapid adoption of automation technologies

Process simplification to reduce automation challenges

Gartner

Hyperautomation: “As no single tool can replace humans, hyperautomation today involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS) and AI, with a goal of increasingly AI-driven decision making.”

Gartner explains that the term “hyperautomation” is different than automation because it is not only about products nor services. They define hyperautomation to include significant changes in the company from IT infrastructure to designing approaches of business processes and decisions, therefore, hyperautomation is a holistic approach to automation. This is a familiar pattern, in any change initiative that fails to achieve the change it targets, outsiders such as consultants and industry analysts stick to blaming lack of cultural change. They are partially right, cultural change is indeed necessary but as importantly, different automation tools need to be more interoperable and provide higher levels of automation for automation to be widespread.

AIMultiple Forrester

Digital Process Automation is defined it as “aligning processes on the back-end to support a true end-to-end customer experience.” The focus on process is relevant as complex processes are harder to automate and hold back organizations’ automation efforts. Even when such processes are automated, they are harder to validate, leading to longer pilots and slower adoption. Though increased machine intelligence is making this easier, current state of automation definitely favors simpler processes.

IDC

Intelligent Process Automation (IPA): “IPA is a strategic business imperative for enterprises to accelerate digital transformation by focusing on business process transformation and automation”

To add to the confusion, vendors also use different terminology to imply the same thing:

Workfusion: Intelligent automation platform

Cognitive process automation

Why is hyperautomation popular?

Interest in hyper automation has begun at the end of 2023. Most terms made up by Gartner do not stick, sometimes Gartner comes up with terms like AI Based Accounts Payable Invoice Automation (APIA) which sound neither cool nor easy-to-understand. However, hyperautomation is becoming more commonly used, it is being picked up by vendors like UiPath. It is attractive to vendors since it is

short

something that companies aspire to

vague so any vendor can claim to enable it. Most automation technologies are included in the definition so pretty much any emerging technology can be associated with hyperautomation.

Why is it important now?

Because it has significant potential for impact and companies are frustrated with the current level of progress in their automation efforts.

Gartner, without citing any quantitative backup, claims that organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes by 2024. The number is probably wrong but it is beyond doubt that automating operational decision making will be impactful and will be a focus area for companies.

Due to several issues, traditional, product based automation approaches with limited reliance on machine learning have failed to deliver significant benefits:

Process complexity has slowed down automation efforts even in rules based processes

Employees have yet to adopt a culture of looking for automation opportunities and rapidly experimenting with new technologies

Most efforts did not rely on building custom machine learning models which limited application areas

All of these lead companies to look for different approaches.

How is it different than automation?

Though RPA has been the fastest-growing segment of the global enterprise software market and the payback period is shorter than other digital transformation technologies, RPA has limitations such as its reliance on structured data. A traditional RPA software cannot understand the context and learn itself by using unstructured data. It requires humans to code rules-based tasks, interpret data, and make decisions about automation.

Compared to conventional RPA automation, hyperautomation shifts the focus of automation to decision making and more complex work. It also includes more focus on process simplification and culture in enabling automation.

Top 6 technologies enabling hyperautomation Robotic Process Automation (RPA)

RPA is at the core of hyperautomation.

Combining technologies enables RPA to become more intelligent and extends the reach of RPA.

If you want to learn more on RPA, feel free to read our following articles or visit our sortable, up-to-date RPA vendor list:

Intelligent Business Process Management Suites (iBPMS)

An iBPMS is an integrated set of technologies that coordinates people and machines in process delivery. An iBPMS enables companies to model, implement, and execute sets of interrelated processes by applying business rules.

Though it is less popular with the emergence of RPA, it is still a useful tool for process automation.

Process Mining

Process mining is an analytical discipline to gain a deep understanding of a company’s processes. Process mining has a broad range of use cases from common applications such as process optimization to industry-specific applications like risk identification in an audit. Process mining is critical for process simplification and process understanding which are major enablers of hyperautomation. You can discover all the latest process mining software vendors in the market from our comprehensive list.

APIs

The technology and frameworks to provide black box software interfaces has been around for a long time. Jeff Bezos famously pushed all Amazon to embrace APIs back in 2002. However, even today numerous enterprises rely on legacy interfaces to exchange data between applications. Embracing APIs facilitates machine-to-machine communication and therefore automation.

Computer Vision

Computer vision (CV) is a combination of AI techniques including image classification and segmentation, and object detection and tracking, which enable machines to interpret information from unstructured data such as images and videos.

Natural Language Processing (NLP)

NLP helps businesses automate tasks that knowledge workers would do. It enables machines to understand unstructured data from emails, social media posts, videos. Then it performs sentiment analysis, automatic language translation or automatic classification of texts into categories depending on your business’ automation needs. Thanks to NLP technology, RPA bots can understand the context of the task.

To explore NLP use in-depth, read our article on top 30 NLP use cases for different business functions.

Other Artificial Intelligence/ Machine Learning

Artificial intelligence (AI) mimics human intelligence processes by machines, and machine learning is the subcategory of AI that contains learning algorithms for machines. Organizations use machine learning to carry out specific tasks without explicit programming, relying on data.

If you want to learn more on AI, feel free to read our following articles:

Optical Character Recognition (OCR)

OCR can eliminate manual labor for certain tasks. OCR can extract data in documents and convert it machine-readable characters. When it is combined with AI and RPA, businesses can automate the processes where human interaction is needed to capture data from documents, analyze it and take action to handle the task.

Digital Twin of an Organization (DTO)

A digital twin is a virtual replica of a product, service, or process. A digital twin of an organization (DTO) makes the previously unseen interactions between processes, functions, and key performance indicators visible by the organization to test the results of automated processes even before automating the process. To learn more about digital twin of an organization applications in business, feel free to check our in-depth guide on DTOs.

You can also check our article on hyperautomation trends to learn more about the most important technologies that enables hyperautomation.

Which processes are ripe for hyperautomation?

Applications of hyperautomation vary depending on which technologies are combined. Some example use cases could be:

Customer support using conversational AI and RPA: Customer support services involve processes that require understanding and replying emails, questions, and queries, as well as making changes to customer data. By combining these technologies, companies can automatically respond to customer queries.

Approving financial transactions: With RPA, businesses can extract relevant information on user’s transactions from different data sources. Then an NLP system can process this information and perform analytics to identify any fraudulent actions.

Claim processing for auto insurance: RPA has been used to automate claims processing by insurance companies. With the integration of computer vision and NLP, auto insurers can process images about car crashes and understand the damages covered by the customers’ policy. They can then use this data to identify whether the claim is covered with the current contract or not.

Hyperautomation is not limited to above applications. If you want to learn more on hyperautomation processes, feel free to read our article on hyperautomation examples.

What are the benefits of hyperautomation?

Automation initiatives mainly focus on cost reduction and increase in compliance. In addition to those, top benefits of hyperautomation for businesses include:

Agility: The business doesn’t need to rely on a single technology for automation purpose. Reliance on a suite of tools along with cultural change, enable organizations to achieve scale and flexibility in operations.

Enabling employees and improving their productivity: Automation frees the time of employees so that they can focus on more value-added tasks.

Improved collaboration: Hyperautomation enables businesses integrate digital technologies across their processes and legacy systems. With the integration of technologies, stakeholders have better access to data and can communicate seamlessly throughout the organization.

You can check our comprehensive article on the benefits of hyperautomation for businesses.

What are the challenges of hyperautomation?

Technologies that are a part of hyperautomation may complement each other. For example, RPA bots can enable the integration of other technologies with legacy systems. However, there are still significant challenges due to the limitations of these technologies as well as organizational challenges. Common challenges are:

Challenges in using AI in automation:

Training data may be unavailable or may include personal data: Risk of data privacy issues may occur when you share personal data with AI vendors, yet, you can’t build everything yourself. Therefore, companies should invest in privacy enhancing technologies such as data masking.

Training data creation can be slow. Synthetic data sets can speed up training data generation in some cases.

Edge cases: In any complex process that is automated with machine learning, there will be cases when humans need to step in. An easy-to-use human-in-the-loop solution is critical for the success of AI in automation

AI systems may contain biases that are either in training data or embedded into algorithm via prejudiced assumptions.

For more, please see our article on challenges of AI.

Challenges in process simplification:

Lack of process understanding: Most processes are not well documented. Process mining tools can help organizations understand processes that rely on log files but still important process information such as the content of calls are hard to analyze, creating challenges.

Specific customer demands: Custom solutions for specific customer needs increase customer satisfaction but reduce maintainability and introduce process complexity. Companies need to smartly trade-off between process simplification and customer satisfaction. A customer with extremely complex requirements may not be the right one to be served with a standardized process serving many other customers.

Organizational challenges: Avoidance of potential errors and inertia are responsible for most cases of slow adoption of automation. Employees should be empowered to run experiments on sandboxes and digital twins to quickly see the impact and challenges of potential automation technologies.

If you are looking for vendors that can provide necessary technologies to achieve hyperautomation, check our data-driven lists for:

And don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Innovation Unleashed: The Hottest Nlp Technologies Of 2023

Introduction Improving Text Representation

Accurate representation of text is necessary as it allows the machine to understand the meaning and intent of the text and allows us to perform various tasks such as text classification, language translation, and text generation.

As we know to input textual data into the NLP models, we need to convert that textual data to their embeddings. And the results of these models depend on these embeddings only.

Data2Vec 2.0

Data2Vec2.0 is an updated release for the model Data2vec. Data2vec is a self-supervised learning algorithm, meaning it can learn from vision, text, and speech without needing explicit labels. Self-supervised learning algorithms learn by using the inherent structure of the data itself.

Data2Vec2.0 has shown tremendous results for tasks like text understanding image segmentation and speech translation task.

Similar to the original data2vec algorithm, data2vec 2.0 predicts contextualized representations of the data, meaning they take the entire training data into account.

Data2Vec2.0 is an improved version then all its predecessors as it is way faster than any other model and does not compromise accuracy.

For speech, the test was done on the LibriSpeech speech recognition benchmark, where it performed more than 11 times faster than wav2vec 2.0 with similar accuracy. For natural language processing (NLP), evaluation was done on the General Language Understanding Evaluation (GLUE) benchmark, which achieved the same accuracy as RoBERTa and BERT.

The architecture of Data2Vec 2.0

Source

To know more about the topic, refer to this link

New and Improved Embedding Model

Text-embedding-ada-002 was recently launched by openAI. It has outperformed all the previous embedding models launched by openAI.

Text-embedding-ada-002 is trained using a supervised learning approach, which means that it is trained on a labeled dataset that consists of text input and corresponding targets.

The model uses a transformer-based architecture designed to process sequential data such as text. The transformer architecture allows the model to effectively capture the relationships between words and phrases in the text and generate embeddings that accurately reflect the meaning of the input.

The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search and is priced way lower than all the previous models.

The context length of the new model is increased, which makes it more convenient to work with large documents, while the embedding size of the new model is decreased, making it more cost-effective.

Image and Video Generation Imagen

Imagen, developed by Google and launched in 2023, is a text-to-image diffusion model. It takes in a description of an image and produces realistic images.

Diffusion models are generative models that produce high-resolution images. These models work in two steps. In the first step, some random gaussian noises are added to the image and then in the second step, the model learns to reverse the process by removing the noise, thereby generating new data.

Imagen encodes the text into encodings and then uses the diffusion model to generate an image. A series of diffusion models are used to produce high-resolution images.

It is a really interesting technology as you can visualize your creative thinking just by describing an image and generating whatever you want in moments.

Now let me show you guys the output image I got using a certain text

Text: A marble statue of a Koala DJ in front of a marble statue of a turntable. The Koala wears large marble headphones.

Output Image:

Output Image of a Koala DJ by Imagen

Source

I know that was something really fascinating, Right!!. To know more about the model, refer to this link

DreamFusion

DreamFusion, developed by Google in 2023, can generate 3D objects based on text input.

The 3D objects created are of high quality and are exportable. They can be further processed in common 3D tools.

Video of some 3D images produced by DreamFusion

Source

The 3D model created is based on 2D images from the generative image model Imagen so you also don’t need any 3D training data for the model.

Interesting, Right!!, Now go and refer to this link to learn more about the model.

DALL-E2

DALL-E2 is an AI system developed by OpenAI and launched in 2023 that can create realistic images and art based on textual descriptions.

We have already seen the same technologies, but this system is too worth exploring and spending some time. I found DALL-E2 as one of the best models present, which works on image generation.

It uses a GPT-3 modified to generate images and is trained on millions of images from over the internet.

DALL-E uses NLP techniques to understand the meaning of the input text and computer vision techniques to generate the image. It is trained on a large dataset of images and their associated textual descriptions, which allows it to learn the relationships between words and visual features. DALL-E can generate coherent images with the input text by learning these relationships.

Let me show you how DALL-E2 works

Input text – Teddy bears

Output Image-

Image of Teddy bears produced by DALL-E2

Source

Here is the link to the research paper if you are interested to read in detail here.

Conversational Agents

Here are some top Conversational models launched in 2023

LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything

LaMDA (Language Model for Dialogue and Answering), developed by Google, is a language model designed for answering and dialog tasks.

This model can be used in various ways, such as chatbots, customer service, Virtual Assistants, etc.

One of the key features of LaMDA is its ability to generate coherent responses grounded in the input text. This is achieved through the use of a transformer-based language model that is trained on a large dataset of human conversations. The model is able to understand the context of the conversation and generate appropriate responses based on the content of the input text.

LaMDA can generate high-quality responses on a wide variety of topics and open-ended questions.

The developers have also kept in mind the sanity of responses generated by the model, and it avoids generating offensive and biased content.

I’m sure you guys would want to see a demo of this amazing bot. So here it is!

Conversation with LaMDA

Source

For in-depth knowledge, refer to the link here

ChatGPT

ChatGPT, developed by OpenAI, was recently released in late November and is one most trending and viral AI product launched in 2023. Almost all data professionals are trying and researching this amazing chatbot.

ChatGPT is based on the GPT-3 (Generative Pre-trained Transformer 3) language model, a large, transformer-based language model trained on a massive dataset of human-generated text.

ChatGPT can generate coherent responses and can, understand the context of the conversation, and generate appropriate responses based on the content of the input text.

It is designed to carry conversations with people. Some of its features include answering follow-up questions for various topics.

The accuracy and the quality of the responses generated by the model are incomparable to any other chatbot.

Here is the demo of how ChatGPT works

Conversation by chatGPT

Refer to this link to learn more about the model here

Automatic Speech Recognition Whisper

Whisper, developed by OpenAI, is a technology that helps in the conversion of Speech to text.

It has multiple uses like Virtual assistants, voice recognition software, etc. Moreover, it enables transcription in multiple languages and translation from those languages into English.

Whisper is trained on 680,000 hours of multilingual and multitask data collected from the web. The use of a large and diverse dataset has led to increased accuracy of the model.

Whisper uses encoder-decoder architecture in which the input audio is split into chunks of 30 seconds, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption.

Whisper can be trained on large datasets of speech and transcription pairs to improve its accuracy and adapt to different accents, languages, and speaking styles.

The architecture of Whisper

Source

Transfer Learning in NLP

Transfer learning is a go-to approach for building high-performance models. In transfer learning, the model is trained on large and general datasets and is fine-tuned for our related task. It has been widely used in natural language processing (NLP) to improve models’ performance on almost each and every task. There has been significant research in 2023 around improving the transfer learning techniques. We will discuss the top 2 breakthroughs in this area now.

Zero-Shot Text Classification with Self-Training

As a result of recent developments in big pre-trained language models, the importance of zero-shot text categorization has increased.

Particularly, zero-shot classifiers developed using natural language inference datasets have gained popularity due to their promising outcomes and ready availability.

You can read more about this approach in this conference paper.

Improving In-Context Few-Shot Learning via Self-Supervised Training

In-context few-shot learning refers to learning a new task using only a few examples within the context of a larger, related task. One way to improve the performance of in-context few-shot learning is through the use of self-supervised training.

Self-supervised learning involves training a model on a task using only input data and without explicit human-provided labels. The goal is to learn meaningful representations of the input data that can be used for downstream tasks.

In the context of in-context few-shot learning, self-supervised training can be used to pre-train a model on a related task, such as image classification or language translation, to learn useful data representations. This pre-trained model can then be fine-tuned on the few-shot learning task using only a few labeled examples.

Read in detail about the approach in this paper.

Conclusion

Ultimate Guide To Ransomware: Tools & Best Practices In 2023

In this article, we explore what ransomware is, how it works, which industries are affected by it, and how to protect yourself against it.

What is ransomware and how does it work?

Ransomware is a type of malware designed to target a user’s device or network, steal their data, and block their access to it until they pay a ransom to the attacker. There are mainly 2 types of ransomware:

Locker: Locker ransomware blocks user’s access to their device’s basic functions such as the desktop, mouse, or keyboard, enabling the user only to react to the ransomware message to make the payment. Typically, locker ransomware does not target specific files or folders in the device.

Crypto: Crypto ransomware targets documents or files on the device, encrypts them, denies user access to them, and typically threatens to destroy or publish the data if the ransom was not paid in a certain time. Crypto ransomware does not affect the way a user interacts with their device.

How is ransomware spread? What are some of the famous ransomware attacks?

There has been an increase in the number of organizations attacked by ransomware attacks in the past 5 years, and ~1500 businesses have been attacked in the US in 2023 alone. Some of the most famous attacks are:

What are the best practices to detect and mitigate ransomware attacks?

Ransomware attacks target devices with limited protection and threaten to destroy important data, therefore, to prevent ransomware attacks and avoid the worst consequences, individuals and businesses need to follow best practices, which include:

Backup the data using the 3-2-1 rule which states that you should have 3 copies of your data in 2 different places (e.g. cloud, device, USB) with 1 copy off-site for disasters.

Conduct regular software updates to ensure the installment of the latest patches for system vulnerabilities. To automate software updates, businesses can leverage:

RPA bots that handle repetitive GUI tasks, including system updates.

Workload automation tools that can trigger system updates at certain times or triggering events.

Employ email filtering to detect phishing and scam emails. Businesses can leverage anti-spam solutions to scan email messages and files attached to the email for potential threats.

Separate business networks according to department or tasks to avoid major data loss in case of a ransomware attack on a centralized point. Businesses can also use network security solutions that monitor network traffic and inform the IT team about any abnormal situations that require further investigation.

Do not log in to sensitive accounts from shared networks such as public WiFi

Employ cyberattack and ransomware detection and mitigation software such as:

Vulnerability management tools that identify, prioritize and manage system vulnerabilities, as well as suggest remediation tips to avoid system breaches.

Cybersecurity software that relies on AI and machine learning technology to prevent, detect and react to various forms of cyber threats.

Avast Antivirus

Kaspersky Anti-Ransomware Tool

Bitdefender Anti-Ransomware Tool

Cybersight RansomStopper

Trend Micro RansomBuster

Check Point ZoneAlarm

CryptoDrop

RPA

RPA bots can be used to increase cybersecurity by automating data enrichment and management, eliminate unauthorized access to privileged data, run cyber threat hunts and penetration tests, detect viruses and malware threats, and automate system updates.

Workload automation (WLA) tools integrate scheduling and triggering capabilities to schedule, execute and monitor backend processes on different business platforms from a centralized point. WLA tools can be used to automate several processes which can affect the overall security of the system because they reduce human intervention and access to privileged data, create event logs of file transfers and loadings to generate audit trails, and detect and alert system errors to ensure system security. See our prioritized list of WLA tools to identify the right ones for your organization.

How can AI help in mitigating ransomware attacks?

Different AI algorithms can be used to detect ransomware attacks depending on the attack type, for example:

Natural Language Processing algorithms (NLP): NLP can be leveraged for filtering phishing and spam emails because it can detect malicious or threatening language and classify messages and emails as spam or ham.

Deep learning: Ransomware has different variants and families. Deep learning can be used to generate and train predictive models, such as recurrent neural networks (RNN) with long short term memory (LSTM), that can learn the behavior of ransomware and use this knowledge to detect evolving variants and families which have not yet been seen.

Cybersecurity analytics: Cybersecurity analytics studies the digital trail left behind by cyber criminals to analyze system weaknesses, provide a holistic view of security considerations, and prevent losses in the future.

The Ultimate Guide To Cyber Threat Intelligence (Cti) In 2023

Data is the most valuable asset of most modern organizations. Organizations are rapidly deploying new technologies and devices that increase vulnerability points that malicious attackers may target. Organizations need to protect their data assets at a time when their attack surface is rapidly growing. Cyber threat intelligence helps businesses identify malicious activity before it happens and speeds up decision-making processes to respond to such threats.

What is Cyber Threat Intelligence?

Threat intelligence or cyber threat intelligence is the data collection and analysis to gain information about existing and emerging threats to a business. Cyber threat intelligence helps organizations avoid unexpected threats. Since cyber threat intelligence information makes unknown threats visible to organizations, businesses can improve their cybersecurity mechanism and mitigate the risk of cyberattacks.

Wikipedia defines the term as follows:

Cyber threat intelligence is information about threats and threat actors that helps mitigate harmful events in cyberspace. Cyber threat intelligence sources include open source intelligence, social media intelligence, human Intelligence, technical intelligence or intelligence from the deep and dark web.

Why is it important now?

As the amount of data generated by businesses increases and as it becomes easier to act on data, the potential risk of a data breach increases. Hackers can easily monetize captured data by sale or ransomware. The number of data breaches is increasing each year (Compared to midyear of 2023, the number of reported breaches was up 54% in 2023) and average cost of a data breach is expected to surpass $150 million in 2023.

Besides these market researches, ESG’s survey highlights the fact that sustaining cybersecurity is more difficult than two years ago due to the following reasons:

cyber threats are getting more sophisticated

number of threats and types of threats are increasing

organizations face a shortage of sufficient skilled professionals

With cyber threat intelligence, organizations gain a deeper understanding of threats and respond to the concerns of the business more effectively.

See our article on cybersecurity stats to explore the numbers in detail.

What are the intelligence sources?

These are some of the common sources that can be used in threat intelligence as identified by Bank of England:

Human intelligence: Enterprises can work with cybersecurity companies to identify threats. Furthermore, companies with global scale and billions in revenues can afford to gather information from sources such as national intelligence agencies.

Open source intelligence: Publicly available sources such as the media, internet, public government data, publications, financial and industrial assessments.

What are the types of Cyber Threat Intelligence? Tactical threat intelligence

Tactical threat intelligence identifies how the organization might be attacked. It helps inform improvements to existing security processes while speeding up incident response. Security teams must identify:

the potential attackers and their motivations,

vulnerable points that attackers may target,

potential actions that organizations may take depending on the threat intelligence

Though tactical threat intelligence is the easiest type of threat intelligence and is mostly automated by organizations, indicators of compromise (IOC) such as malicious IP addresses, URLs, file hashes and domain names get outdated quickly. The short lifespan of IOCs may cause false positive during the analysis that’s why it can not be a long term security plan of an organization.

Reports that are generated by tactical threat intelligence are geared towards technical audiences such as infrastructure architects, administrators and security staff. These personnel use the reports to make improvements in the security system.

Operational Threat Intelligence

Though some of these capabilities overlap with tactical intelligence capabilities, tactical intelligence is more automated while human analysis is needed for effective operational intelligence.

Operational intelligence is mostly used in cybersecurity disciplines such as vulnerability management, incident response and threat monitoring.

Strategic Threat Intelligence

Strategic threat intelligence provides a wider outlook of the organization’s threat landscape. It identifies potential attackers by analyzing the organization in light of global dynamics. For example, major US companies are prepared against cyber attacks by countries that are in conflict with the US in various fields.

Strategic intelligence requires machines to process large volumes of data and analysis of a human who has expertise in both sociopolitical and business concepts. Output mostly comes in the form of reports to inform executives and other decision-makers in the enterprise. Therefore the context of reports contains less technical information compare to tactical and operational intelligence.

Sources used in strategic intelligence are generally open sources including:

policy documents of nation-states,

local and national media,

industry- and subject-specific publications,

whitepapers and research reports of security vendors.

How does AI affect cyber threat intelligence?

AI eases the job of the security team by fastening the task of data processing, image below shows how time-saving AI is for cyber threat intelligence processes.

AI has an active role in the threat intelligence process as well. Since threat intelligence depends on data analysis, NLP technology is heavily used in collecting unstructured data and data processing. Threat intelligence adopts NLP and machine learning to interpret text from various unstructured documents across different languages.

Cyber threat intelligence is an application of predictive analysis that focuses on security. We’ve already written how AI is shaping analytics, feel free to check it out if you want to learn AI capabilities in analytics.

What are the benefits of cyber threat intelligence?

Usage of cyber threat intelligence tools improve organizations’ security in different aspects:

SANS Institute conducted a survey and asked executives the main barrier to implement an effective cyber threat intelligence. The results can be seen below. Lack of technical skills of employees/executives and the difficulty of using security tools are the common pitfalls that inhibit implementing cyber threat intelligence effectively.

Source: SANS Institute

What are the leading companies?

Check our data-driven hub of cybersecurity solutions to explore the different vendors and their offerings.

If you want to improve the security capabilities of your organization but don’t know where to start, we’ve written a few articles about information security solutions. Feel free to check them out:

If you still have questions about cyber threat intelligence, don’t hesitate to contact us.

Source:

CrowdStrike

Recorded Future

eSecurity Planet

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Automated Workflow Management: A Comprehensive Guide In 2023

~78% of business leaders think remote working and workplace reconfiguration will boost automation and digital transformation.

Knowledge of workload automation is important to enhance workflow management. However, many business leaders claim not to know about workflow automation:

~68% of businesses do not understand digital transformation.

~75% of organizations do not have the right information about the digital workplace.

We have curated this article to fill that knowledge gap. We cover:

What is a workflow?

What is workflow automation?

What is workflow management?

What are the types of workflow automation?

Which workflow automation type should be used?

What is workload automation (WLA) based workflow automation?

What is business process automation (BPA) based workflow automation?

What is robotic process automation (RPA ) based workflow automation?

What is a workflow?

A “workflow” is a series of industrial, administrative, or other processes that take a piece of work from start to finish. A workflow is made possible by putting resources into processes that change materials, give services, or process information in a planned way. A workflow can include multiple steps, people, systems, or machines.

Figure 1: A workflow example.

What is workflow automation?

Workflow automation is using software to eliminate or reduce the number of manual tasks and increase work efficiency. Some examples of workflow automation are:

Creation and sending out calendar events and invitations.

Sending messages through platforms like WhatsApp and SMS.

Document creation, like sales contracts, to send customers for signature.

Tasks in data workflows, like data transfer between platforms.

Workload distribution based on volume and time.

What is a workflow management system?

A workflow management system (WfMS or WFMS) or workflow application is a set of tools that lets you set up, run, and observe a predetermined set of tasks.

What are the types of workflow automation?

Workflow automation can be classified into three categories according to the way it functions:

Which workflow automation type should be used?

WLA-based workflow automation can be preferred when back-end workflows are automated, such as:

Server updates.

Data management operations like extract-transform-load (ETL) operations.

Automating tasks for file transfer protocol servers.

BPA-based workflow automation can be used in front-end operations that require flexibility, such as:

Travel requests are processed through digital forms on a single platform.

Creating digital time-off requests forms.

Sorting customer queries to departments by using the software interface.

RPA-based workflow automation can be useful in precisely repeatable front-end operations such as:

Data entering for cashier reports like product ID and product quantity.

Creation of PDF invoices and sending them to a specified email address.

User activity auditing to ensure user compliance.

What is WLA-based workflow automation?

Workload automation (WLA) software lets back-end business workflows be scheduled, started, and triggered from one platform.

What are the differences between BPA and RPA?

In contrast to BPM and RPA, Workload Automation gives more importance to event-driven triggers, situational dependencies, and real-time processing than time-based processing when planning, starting, and running workflows.

To learn more about WLA vs. RPA, check out this quick read.

Why is it important?

There are over 14 case studies of WLA where companies benefit from WLA workflow automation. Workload automation can provide the following benefits:

Discover Creatio – the no-code platform for workflow automation and CRM, offering businesses freedom and flexibility. Trusted by thousands of enterprises in more than 100 countries, millions of workflows are launched on the platform every single day.

Watch their short video clip to get a grasp of their services:

How does it work?

Users can log in to their WLA software to create workflows.

Users can choose the application or service to automate a task on the workflow creation screen.

Then, the user can create an event trigger to initiate another business process with the execution of the application.

In the end, the user can set up an alarm or a message to let him or her know when the business process is done or canceled.

Video 1: An example of the use of the WLA tool:

What are some WLA use cases in workflow automation?

This section highlights some use cases of workflow automation:

IT department:

To automate ETL workflows: WLA tools can automate ETL task triggering and execution to reduce human error.

To manage data centers: By using WLA tools, IT teams can create workflows to manage data centers from a single platform.

Human Resources:

To automate payrolls: WLA tools can be used in HR to automate payroll management tasks like the calculation of compensations or initiation of payments.

To automate hiring tasks: By using WLA, HR can schedule online job posts, interviews, and emails.

Finance:

To automate know your customers (KYC) tasks: WLA automation can be used in finance to automate KYC workflow tasks in finance like

business existence checks

Address checks

Financial document recordings

Client onboarding and offboarding: WLA technologies can automate client onboarding and offboarding emails and permissions.

What is business process automation-based workflow automation?

Business process automation (BPA) uses software to automate business workflows. Excel’s autofill and macro features are some of the first examples of business process automation used in business processes like sales order processing. 36% of businesses automate workflow, and 26% intend to do so.

What is the difference between BPA & RPA?

BPA automates the workflows of business processes to make tasks that used to be done by hand more efficient. BPA focuses more on front-end tasks like automating emails and invoice processing. Or creating more open-ended business tasks like situation-based workflow diagrams to assign tasks to employees. Some of the main differences are:

RPA tools focus on automating specific and exactly repeatable tasks, while BPA can automate situation-dependent workflows.

The use of BPA tools is less predictable, and they are slower than RPA chúng tôi tools are faster and more predictable than BPA tools.

Examine this quick read for more information on RPA vs. BPA.

Why is it important?

BPA can assist in business process management (BPM) and improvement. BPM involves discovering, modeling, analyzing, measuring, improving, optimizing, and automating processes. Improving business process management is important because it can have the following benefits:

Improve efficiency: Analysts and leaders can see every step, task, and employee in workflows with BPM. In 55 process improvement case studies we collected, 72% of them improved process efficiency with BPM efforts.

Figure 2: In 72% of companies, the use of BPM increased process efficiency.

Motivate change: Business leaders can use BPM tools to document, monitor, and anticipate changes in workflows.

Motivate compliance and security: Managers and analysts can easily document and visualize workflows with BPM to ensure compliance. For example, 19% of executives consider BPM for audit and compliance purposes.

Learn more about business process management benefits and best practices. Compare BPM, process intelligence, and process mining tools through our data-driven and comprehensive lists.

How does it work?

Video 2: An application of business process automation software.

What are some BPA use cases?

Document search: BPA software can aid in finding information on platforms or internal systems that your company uses to store documents.

Video 3: An example of a BPA document search:

Diagramming: Business workflow automation BPA can create workflow diagrams with drag-and-drop features. Workflow diagrams help to explain how a business process works with visual workflows. They can show how a business process works from beginning to end. BPA software can:

Support the Business Process Model and Notation (BPMN) graphically or with other diagramming conventions.

Save time by creating workflow diagrams manually with their pre-defined features.

Figure 3: Workflow diagram.

Email management: Service requests from emails and form submissions can be automated using BPA. BPA software can be programmed to process specific emails and forms. These items could include a customer number or the phrase “support ticket.” When an email arrives, the BPA software can start the tasks that the user has specified, such as sending a “thank you” email with the average response time to the request.

Video 4: An exemplary use of BPA for email management.

What is robotic process automation-based workflow automation?

Robotic process automation (RPA) is a common way to make specialized agents, or “bots,” that interact with graphical user interface (GUI) elements to complete repetitive, rules-based workflows. They can, for example, be used to move files to specified folders and read and write databases within a specified time frame.

Figure 4: RPA Services business process architecture

Why is it important?

RPA is important because it has the following benefits:

On-time process execution: RPA bots can start a task on a specific day and time each month.

Boosts remote work and reduces costs: RPA lets employees orchestrate and monitor automated workflows from a dashboard remotely.

RPA can manage remote desktop automation: RPA can automate remote desktops using their user interface.

To learn more about the benefit of RPA, check out this quick read.

How does it work?

RPA software can require programmers to create and automate bots. On the RPA software:

Video 5: An exemplary use of RPA software

What are some of the RPA use cases in workflows?

Data updates: RPA can automate tasks in data processing workflows. RPA can be set to update relevant data from forms or emails automatically. So, HR, marketing services, and sales departments can have access to the most up-to-date and correct information.

Data validation: Data validation workflows can include automatable tasks like cross-checking data against publicly available data. RPA automation is more suitable than other tools due to scalability.

Data extraction from PDFs and scanned documents: RPA can automate tasks in data extraction document processing because screen scraping, OCR (Optical Character Recognition), and basic pattern recognition technologies can be integrated with RPAs.

Learn more about RPA use cases and applications.

You can download our whitepaper to find out more about workload automation:

If you have any more questions about best practices for workload automation, please get in touch with us at:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Best Bitcoin Hardware Wallets In 2023 (Buying Guide)

Bitcoin is a popular cryptocurrency and is considered the first decentralized digital currency. If you are a crypto trader and toying with the idea of providing some extra armor to your private keys, you should glance at these best bitcoin hardware wallets.

We have made a special post on the best bitcoin apps for iPhone and iPad, which allow you to send/receive, and manage your cryptocurrency with aplomb. But if you don’t want to use your smartphone or wish to entrust another device with the responsibility of guarding your private keys, these hardware wallets can do a fairly good job. Read on to explore what makes them so useful and whether or not they deserve your dollar!

1. KeepKey

Equipped to provide high-level security, KeepKey is a trusted bitcoin wallet. It lets you easily backup your data and recovers it without any hassle. It provides a complete safeguard against viruses and malware. Another notable feature of KeepKey is the support of multiple currencies like Ethereum, Litecoin, Namecoin, Dogecoin, Dash, and Testnet. The wallet is designed to work seamlessly on PCs, Mac, Linux, and Android.

Buy it from Amazon

2. Ledger Nano S

Ledger Nano S is a useful asset for storing cryptographic assets and works as neatly as a hardware asset for Bitcoin, Ethereum, and Altcoins. It provides essential security for digital payments. Connecting it to a computer (using a USB cable) displays a secure OLED display to let you check and confirm each transaction with a tap on its side buttons. You can offer more safeguards to your private keys using a pin code.

Buy it from Amazon

3. Trezor

Trezor functions efficiently as a hardware wallet for cryptocurrencies. It offers essential security to your private keys. It shows a small display to let you verify a transaction and confirm it. Trezor is compatible with Windows, macOS, and Linux. It has the support of BitCoin, Ethereum, and Zcash. Dash and Litecoin currencies. Moreover, it’s available in two packs at one price.

Buy it from Amazon

4. Ledger

Ledger has the quality to be your reliable bet when it comes to offering a safeguard to your private keys and working as a handy wallet. It uses a certified microprocessor to shield your keys. You can easily store your bitcoin or other cryptocurrencies in the wallet and access them when needed. It also keeps your data protected from malware. As the bitcoin wallet works with most operating systems, you can manage your data without any problem.

Buy it from Amazon

5. SecuX

This crypto hardware wallet has security features, including a protected production chain and military-grade tamper-resistant packaging. Not just that, it is super compatible with all the devices, from Mac to your iPhone. The interface is simple, easy to use, and ensures the safety of your devices. This hardware wallet has a touch screen display and Bluetooth support to send and receive coins and tokens. Isn’t this perfect?

Buy it from Amazon

That’s all for now!

Which Bitcoin Hardware Wallet have you chosen?

I guess you have handpicked one of these finest hardware wallets for bitcoins. It’d be nice to know its name and the qualities you have found appreciable in it. Besides, let us know the name of any such device worth including in this list.

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The founder of iGeeksBlog, Dhvanesh, is an Apple aficionado, who cannot stand even a slight innuendo about Apple products. He dons the cap of editor-in-chief to make sure that articles match the quality standard before they are published.

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