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IBM Research’s Deep Search product uses natural language processing (NLP) to “ingest and analyze massive amounts of data—structured and unstructured.” Over the years, Deep Search has seen a wide range of scientific uses, from Covid-19 research to molecular synthesis. Now, IBM Research is streamlining the scientific applications of Deep Search by open-sourcing part of the product through the release of Deep Search for Scientific Discovery (DS4SD).

DS4SD includes specific segments of Deep Search aimed at document conversion and processing. First is the Deep Search Experience, a document conversion service that includes a drag-and-drop interface and interactive conversion to allow for quality checks. The second element of DS4SD is the Deep Search Toolkit, a Python package that allows users to “programmatically upload and convert documents in bulk” by pointing the toolkit to a folder whose contents will then be uploaded and converted from PDFs into “easily decipherable” JSON files. The toolkit integrates with existing services, and IBM Research is welcoming contributions to the open-source toolkit from the developer community.

IBM Research paints DS4SD as a boon for handling unstructured data (data not contained in a structured database). This data, IBM Research said, holds a “lot of value” for scientific research; by way of example, they cited IBM’s own Project Photoresist, which in 2020 used Deep Search to comb through more than 6,000 patents, documents, and material data sheets in the hunt for a new molecule. IBM Research says that Deep Search offers up to a 1,000× data ingestion speedup and up to a 100× data screening speedup compared to manual alternatives.

The launch of DS4SD follows the launch of GT4SD—IBM Research’s Generative Toolkit for Scientific Discovery—in March of this year. GT4SD is an open-source library to accelerate hypothesis generation for scientific discovery. Together, DS4SD and GT4SD constitute the first steps in what IBM Research is calling its Open Science Hub for Accelerated Discovery. IBM Research says more is yet to come, with “new capabilities, such as AI models and high quality data sources” to be made available through DS4SD in the future. Deep Search has also added “over 364 million” public documents (like patents and research papers) for users to leverage in their research—a big change from the previous “bring your own data” nature of the tool.

The Deep Search Toolkit is accessible here.

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Killexams : Workload Scheduling Software Market Size and Growth 2022 Analysis Report by Development Plans, Manufactures, Latest Innovations and Forecast to 2028

The MarketWatch News Department was not involved in the creation of this content.

Aug 03, 2022 (The Expresswire) -- "Final Report will add the analysis of the impact of COVID-19 on this industry."

Global “Workload Scheduling Software Market” 2022 report presents a comprehensive study of the entire Global market including market size, share trends, market dynamics, and overview by segmentation by types, applications, manufactures and geographical regions. The report offers the most up-to-date industry data on the real market situation and future outlook for the Workload Scheduling Software market. The report also provides up-to-date historical market size data for the period and an illustrative forecast to 2028 covering key market aspects like market value and volume for Workload Scheduling Software industry.

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Market Analysis and Insights: Global Workload Scheduling Software Market

System management software is an application that manages all applications of an enterprise such as scheduling and automation, event management, workload scheduling, and performance management. Workload scheduling software is also known as batch scheduling software. It automates, monitors, and controls jobs or workflows in an organization. It allows the execution of background jobs that are unattended by the system administrator, aligning IT with business objectives to Improve an organization's performance and reduce the total cost of ownership. This process is known as batch processing. Workload scheduling software provides a centralized view of operations to the system administrator at various levels: project, organizational, and enterprise.
The global Workload Scheduling Software market size is projected to reach USD million by 2028, from USD million in 2021, at a CAGR of during 2022-2028.
According to the report, workload scheduling involves automation of jobs, in which tasks are executed without human intervention. Solutions like ERP and customer relationship management (CRM) are used in organizations across the globe. ERP, which is a business management software, is a suite of integrated applications that is being used by organizations in various sectors for data collection and interpretation related to business activities such as sales and inventory management. CRM software is used to manage customer data and access business information.

The major players covered in the Workload Scheduling Software market report are:

● BMC Software ● Broadcom ● IBM ● VMWare ● Adaptive Computing ● ASG Technologies ● Cisco ● Microsoft ● Stonebranch ● Wrike ● ServiceNow ● Symantec ● Sanicon Services ● Cloudify

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Global Workload Scheduling Software Market: Drivers and Restrains

The research report has incorporated the analysis of different factors that augment the market’s growth. It constitutes trends, restraints, and drivers that transform the market in either a positive or negative manner. This section also provides the scope of different segments and applications that can potentially influence the market in the future. The detailed information is based on current trends and historic milestones. This section also provides an analysis of the volume of production about the global market and about each type from 2017 to 2028. This section mentions the volume of production by region from 2017 to 2028. Pricing analysis is included in the report according to each type from the year 2017 to 2028, manufacturer from 2017 to 2022, region from 2017 to 2022, and global price from 2017 to 2028.

A thorough evaluation of the restrains included in the report portrays the contrast to drivers and gives room for strategic planning. Factors that overshadow the market growth are pivotal as they can be understood to devise different bends for getting hold of the lucrative opportunities that are present in the ever-growing market. Additionally, insights into market expert’s opinions have been taken to understand the market better.

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Global Workload Scheduling Software Market: Segment Analysis

The research report includes specific segments by region (country), by manufacturers, by Type and by Application. Each type provides information about the production during the forecast period of 2017 to 2028. By Application segment also provides consumption during the forecast period of 2017 to 2028. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

Segment by Type

● On-Premises ● Cloud-Based

Segment by Application

● Large Enterprises ● Small And Medium-Sized Enterprises (SMEs) ● Government Organizations

Workload Scheduling Software Market Key Points:

● Characterize, portray and Forecast Workload Scheduling Software item market by product type, application, manufactures and geographical regions. ● deliver venture outside climate investigation. ● deliver systems to organization to manage the effect of COVID-19. ● deliver market dynamic examination, including market driving variables, market improvement requirements. ● deliver market passage system examination to new players or players who are prepared to enter the market, including market section definition, client investigation, conveyance model, item informing and situating, and cost procedure investigation. ● Stay aware of worldwide market drifts and deliver examination of the effect of the COVID-19 scourge on significant locales of the world. ● Break down the market chances of partners and furnish market pioneers with subtleties of the cutthroat scene.

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Geographical Segmentation:

Geographically, this report is segmented into several key regions, with sales, revenue, market share, and Workload Scheduling Software market growth rate in these regions, from 2015 to 2028, covering

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia, and Vietnam) ● South America (Brazil etc.) ● Middle East and Africa (Egypt and GCC Countries)

Some of the key questions answered in this report:

● Who are the worldwide key Players of the Workload Scheduling Software Industry? ● How the opposition goes in what was in store connected with Workload Scheduling Software? ● Which is the most driving country in the Workload Scheduling Software industry? ● What are the Workload Scheduling Software market valuable open doors and dangers looked by the manufactures in the worldwide Workload Scheduling Software Industry? ● Which application/end-client or item type might look for gradual development possibilities? What is the portion of the overall industry of each kind and application? ● What centered approach and imperatives are holding the Workload Scheduling Software market? ● What are the various deals, promoting, and dissemination diverts in the worldwide business? ● What are the key market patterns influencing the development of the Workload Scheduling Software market? ● Financial effect on the Workload Scheduling Software business and improvement pattern of the Workload Scheduling Software business?

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Detailed TOC of Global Workload Scheduling Software Market Research Report 2022

1 Workload Scheduling Software Market Overview

1.1 Product Overview and Scope

1.2 Segment by Type

1.2.1 Global Market Size Growth Rate Analysis by Type 2022 VS 2028

1.3 Workload Scheduling Software Segment by Application

1.3.1 Global Consumption Comparison by Application: 2022 VS 2028

1.4 Global Market Growth Prospects

1.4.1 Global Revenue Estimates and Forecasts (2017-2028)

1.4.2 Global Production Capacity Estimates and Forecasts (2017-2028)

1.4.3 Global Production Estimates and Forecasts (2017-2028)

1.5 Global Market Size by Region

1.5.1 Global Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 2028

1.5.2 North America Workload Scheduling Software Estimates and Forecasts (2017-2028)

1.5.3 Europe Estimates and Forecasts (2017-2028)

1.5.4 China Estimates and Forecasts (2017-2028)

1.5.5 Japan Estimates and Forecasts (2017-2028)

2 Workload Scheduling Software Market Competition by Manufacturers

2.1 Global Production Capacity Market Share by Manufacturers (2017-2022)

2.2 Global Revenue Market Share by Manufacturers (2017-2022)

2.3 Market Share by Company Type (Tier 1, Tier 2 and Tier 3)

2.4 Global Average Price by Manufacturers (2017-2022)

2.5 Manufacturers Production Sites, Area Served, Product Types

2.6 Market Competitive Situation and Trends

2.6.1 Market Concentration Rate

2.6.2 Global 5 and 10 Largest Workload Scheduling Software Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Workload Scheduling Software Production Capacity by Region

3.1 Global Production Capacity of Workload Scheduling Software Market Share by Region (2017-2022)

3.2 Global Revenue Market Share by Region (2017-2022)

3.3 Global Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.4 North America Production

3.4.1 North America Production Growth Rate (2017-2022)

3.4.2 North America Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.5 Europe Production

3.5.1 Europe Production Growth Rate (2017-2022)

3.5.2 Europe Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.6 China Production

3.6.1 China Production Growth Rate (2017-2022)

3.6.2 China Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.7 Japan Production

3.7.1 Japan Production Growth Rate (2017-2022)

3.7.2 Japan Production Capacity, Revenue, Price and Gross Margin (2017-2022)

4 Global Workload Scheduling Software Market Consumption by Region

4.1 Global Consumption by Region

4.1.1 Global Consumption by Region

4.1.2 Global Consumption Market Share by Region

4.2 North America

4.2.1 North America Consumption by Country

4.2.2 United States

4.2.3 Canada

4.3 Europe

4.3.1 Europe Consumption by Country

4.3.2 Germany

4.3.3 France

4.3.4 U.K.

4.3.5 Italy

4.3.6 Russia

4.4 Asia Pacific

4.4.1 Asia Pacific Consumption by Region

4.4.2 China

4.4.3 Japan

4.4.4 South Korea

4.4.5 China Taiwan

4.4.6 Southeast Asia

4.4.7 India

4.4.8 Australia

4.5 Latin America

4.5.1 Latin America Consumption by Country

4.5.2 Mexico

4.5.3 Brazil

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5 Workload Scheduling Software Market Segment by Type

5.1 Global Production Market Share by Type (2017-2022)

5.2 Global Revenue Market Share by Type (2017-2022)

5.3 Global Price by Type (2017-2022)

6 Workload Scheduling Software Market Segment by Application

6.1 Global Production Market Share by Application (2017-2022)

6.2 Global Revenue Market Share by Application (2017-2022)

6.3 Global Price by Application (2017-2022)

7 Workload Scheduling Software Market Key Companies Profiled

7.1 Manufacture 1

7.1.1 Manufacture 1 Corporation Information

7.1.2 Manufacture 1 Product Portfolio

7.1.3 Manufacture 1 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.1.4 Manufacture 1 Main Business and Markets Served

7.1.5 Manufacture 1 latest Developments/Updates

7.2 Manufacture 2

7.2.1 Manufacture 2 Corporation Information

7.2.2 Manufacture 2 Product Portfolio

7.2.3 Manufacture 2 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.2.4 Manufacture 2 Main Business and Markets Served

7.2.5 Manufacture 2 latest Developments/Updates

7.3 Manufacture 3

7.3.1 Manufacture 3 Corporation Information

7.3.2 Manufacture 3 Product Portfolio

7.3.3 Manufacture 3 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.3.4 Manufacture 3 Main Business and Markets Served

7.3.5 Manufacture 3 latest Developments/Updates

8 Workload Scheduling Software Manufacturing Cost Analysis

8.1 Key Raw Materials Analysis

8.1.1 Key Raw Materials

8.1.2 Key Suppliers of Raw Materials

8.2 Proportion of Manufacturing Cost Structure

8.3 Manufacturing Process Analysis of Workload Scheduling Software

8.4 Workload Scheduling Software Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers

9.1 Marketing Channel

9.2 Workload Scheduling Software Distributors List

9.3 Workload Scheduling Software Customers

10 Market Dynamics

10.1 Workload Scheduling Software Industry Trends

10.2 Workload Scheduling Software Market Drivers

10.3 Workload Scheduling Software Market Challenges

10.4 Workload Scheduling Software Market Restraints

11 Production and Supply Forecast

11.1 Global Forecasted Production of Workload Scheduling Software by Region (2023-2028)

11.2 North America Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.3 Europe Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.4 China Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.5 Japan Workload Scheduling Software Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast

12.1 Global Forecasted Demand Analysis of Workload Scheduling Software

12.2 North America Forecasted Consumption of Workload Scheduling Software by Country

12.3 Europe Market Forecasted Consumption of Workload Scheduling Software by Country

12.4 Asia Pacific Market Forecasted Consumption of Workload Scheduling Software by Region

12.5 Latin America Forecasted Consumption of Workload Scheduling Software by Country

13 Forecast by Type and by Application (2023-2028)

13.1 Global Production, Revenue and Price Forecast by Type (2023-2028)

13.1.1 Global Forecasted Production of Workload Scheduling Software by Type (2023-2028)

13.1.2 Global Forecasted Revenue of Workload Scheduling Software by Type (2023-2028)

13.1.3 Global Forecasted Price of Workload Scheduling Software by Type (2023-2028)

13.2 Global Forecasted Consumption of Workload Scheduling Software by Application (2023-2028)

13.2.1 Global Forecasted Production of Workload Scheduling Software by Application (2023-2028)

13.2.2 Global Forecasted Revenue of Workload Scheduling Software by Application (2023-2028)

13.2.3 Global Forecasted Price of Workload Scheduling Software by Application (2023-2028)

14 Research Finding and Conclusion

15 Methodology and Data Source

15.1 Methodology/Research Approach

15.1.1 Research Programs/Design

15.1.2 Market Size Estimation

15.1.3 Market Breakdown and Data Triangulation

15.2 Data Source

15.2.1 Secondary Sources

15.2.2 Primary Sources

15.3 Author List

15.4 Disclaimer

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Killexams : IBM Watson

This page shows the latest IBM Watson news and features for those working in and with pharma, biotech and healthcare.

Sanofi taps Google’s expertise for digital transformation

A latest GlobalData report suggests that more than 100 companies are working on applying AI to healthcare, with big tech names such as Google, Microsoft, Amazon and IBM Watson paving the

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    old project with IBM Watson Health applying AI to real-world data to Improve insight on the expected outcomes of breast cancer treatment.

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    Pfizer is using IBM’s Watson machine-learning platform to help find new immuno-oncology targets and drugs, for example, while Sanofi is using a rival system from UK firm Exscientia ... costs. That’s an approach already being explored by IBM’s

  • Novo Nordisk to launch connected insulin pens Novo Nordisk to launch connected insulin pens

    Building upon its existing work with IBM Watson Health and Glooko, Novo Nordisk aims to seamlessly integrate insulin-dosing data from connected pen devices with its partners' open ecosystems and diabetes

  • Disruptive technologies set to stabilise soaring drug prices Disruptive technologies set to stabilise soaring drug prices

    The report states that there are currently over 100 companies applying AI to healthcare, with big names such as Google, Microsoft, Amazon and IBM Watson paving the way.

  • Bayer looks to leverage AI across the pharma value chain Bayer looks to leverage AI across the pharma value chain

    The company is, he said, “all over this”, having already collaborated with the likes of Verily, the life sciences unit at Google parent company Alphabet, and IBM’s Watson and Deep ... I can see artificial intelligence in diagnosis. IBM Watson has a

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Killexams : IBM Acquires Observability Platform Databand.ai

IBM has announced the acquisition of data observability software vendor Databand.ai. Today’s announcement marks IBM’s fifth acquisition of 2022. The company says the acquisition “further strengthens IBM’s software portfolio across data, AI, and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.”

Data observability is an expanding sector in the big data market, spurred by explosive growth in the amount of data organizations are producing and managing. Data quality issues can arise with large volumes, and Gartner shows that poor data quality costs businesses $12.9 million a year on average.

“Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist,” said IBM in a press release. “When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.”

IBM says this acquisition will extend Databand.ai’s resources for expanding its observability capabilities for broader integration across more open source and commercial solutions, and enterprises will have flexibility in how they run Databand.ai, either with a subscription or as-a-Service.

IBM has made over 25 strategic acquisitions since Arvind Krishna took the helm as CEO in April 2020. The company mentions that Databand.ai will be used with IBM Observability by Instana APM, another observability acquisition, and IBM Watson Studio, its data science platform, to address the full spectrum of observability across IT operations. To provide a more complete view of a data platform, Databand.ai can alert data teams and engineers when data they are working with is incomplete or missing, while Instana can explain which application the missing data originates from and why the application service is failing.

A dashboard view of Databand.ai’s observability platform. Source: Databand.ai

“Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don’t have access to the data they need in any given moment, their business can grind to a halt,” said Daniel Hernandez, General Manager for Data and AI, IBM. “With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale.”

Databand.ai is headquartered in Tel Aviv, and its employees will join IBM’s Data and AI division to grow its portfolio of data and AI products, including Watson and IBM Cloud Pak for Data.

“You can’t protect what you can’t see, and when the data platform is ineffective, everyone is impacted –including customers,” said Josh Benamram, co-founder and CEO of Databand.ai. “That’s why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients.”

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Wed, 06 Jul 2022 00:02:00 -0500 text/html https://www.datanami.com/2022/07/06/ibm-acquires-observability-platform-databand-ai/
Killexams : IBM acquires Israeli startup Databand to boost data capabilities

US tech giant IBM said Wednesday that it acquired Israeli startup Databand.ai, the developer of a data observability software platform for data scientists and engineers, to strengthen the multinational’s data, artificial intelligence, and automation offerings.

The terms of the acquisition were not disclosed. According to the agreement, Databand employees will join the IBM Data and AI division to further enhance IBM’s portfolio of data and AI products including its IBM Watson, a question-answering computer system, and IBM Cloud Pak for Data, a data analytics platform.

IBM said the acquisition was finalized in late June and that the purchase will build on IBM’s research and development investments, as well as strategic acquisitions in AI and automation. Databand is IBM’s fifth acquisition this year, the company noted.

Databand was founded in 2018 by Josh Benamram, Victor Shafran, and Evgeny Shulman, and rolled out a software platform that the company says helps enterprises and organizations get on top of their data to ensure “data health” and fix issues like errors and anomalies, pipeline failures, and general quality.

The data observability and data quality market is likely to see further growth, as more organizations look to closely track and protect their data. A Statista report estimated that the sector will grow from about $13 billion in worth in 2020 to almost $20 billion in 2024.

Based in Tel Aviv, Databand has raised about $20 million, according to the Start-Up Nation Finder database, with investors such as VCs Accel, Blumberg Capital, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2 Ventures.

“By using Databand.ai with IBM Observability by Instana APM [an application performance monitoring solution] and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations,” IBM said in the announcement Wednesday.

“Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don’t have access to the data they need in any given moment, their business can grind to a halt,” said Daniel Hernandez, general manager for IBM Data and AI, in a statement.

“With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale,” he explained.

Benamram, who serves as Databand CEO, said: “You can’t protect what you can’t see, and when the data platform is ineffective, everyone is impacted –including customers. That’s why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts.

Joining IBM will help Databand “scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients,” he added.

Databand is one of a number of leading Israeli data observability companies including Coralogix, which raised a $142 million Series D funding round announced in May, and Monte Carlo, which secured a $135 million Series D round at a valuation of $1.6 billion, also in May.

Separately, IBM has been active in Israel for decades and runs an R&D center in Tel Aviv and a research lab in Haifa.

The Haifa team is the largest lab of IBM Research Division outside of the United States. Founded as a small scientific center in 1972, it grew into a lab that leads the development of innovative technological products and cognitive solutions for the IBM corporation. Its various projects utilize AI, cloud data services, blockchain, healthcare informatics, image and video analytics, and wearable solutions.

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Killexams : IBM Report: Consumers Pay the Price as Data Breach Costs Reach All-Time High

60% of breached businesses raised product prices post-breach; vast majority of critical infrastructure lagging in zero trust adoption; $550,000 in extra costs for insufficiently staffed businesses

CAMBRIDGE, Mass., July 27, 2022 /PRNewswire/ -- IBM (NYSE: IBM) Security today released the annual Cost of a Data Breach Report,1 revealing costlier and higher-impact data breaches than ever before, with the global average cost of a data breach reaching an all-time high of $4.35 million for studied organizations. With breach costs increasing nearly 13% over the last two years of the report, the findings suggest these incidents may also be contributing to rising costs of goods and services. In fact, 60% of studied organizations raised their product or services prices due to the breach, when the cost of goods is already soaring worldwide amid inflation and supply chain issues.

60% of breached businesses studied stated they increased the price of their products or services due to the data breach

The perpetuality of cyberattacks is also shedding light on the "haunting effect" data breaches are having on businesses, with the IBM report finding 83% of studied organizations have experienced more than one data breach in their lifetime. Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.

The 2022 Cost of a Data Breach Report is based on in-depth analysis of real-world data breaches experienced by 550 organizations globally between March 2021 and March 2022. The research, which was sponsored and analyzed by IBM Security, was conducted by the Ponemon Institute.

Some of the key findings in the 2022 IBM report include:

  • Critical Infrastructure Lags in Zero Trust – Almost 80% of critical infrastructure organizations studied don't adopt zero trust strategies, seeing average breach costs rise to $5.4 million – a $1.17 million increase compared to those that do. All while 28% of breaches amongst these organizations were ransomware or destructive attacks.
  • It Doesn't Pay to Pay – Ransomware victims in the study that opted to pay threat actors' ransom demands saw only $610,000 less in average breach costs compared to those that chose not to pay – not including the cost of the ransom. Factoring in the high cost of ransom payments, the financial toll may rise even higher, suggesting that simply paying the ransom may not be an effective strategy.
  • Security Immaturity in Clouds – Forty-three percent of studied organizations are in the early stages or have not started applying security practices across their cloud environments, observing over $660,000 on average in higher breach costs than studied organizations with mature security across their cloud environments.
  • Security AI and Automation Leads as Multi-Million Dollar Cost Saver – Participating organizations fully deploying security AI and automation incurred $3.05 million less on average in breach costs compared to studied organizations that have not deployed the technology – the biggest cost saver observed in the study.

"Businesses need to put their security defenses on the offense and beat attackers to the punch. It's time to stop the adversary from achieving their objectives and start to minimize the impact of attacks. The more businesses try to perfect their perimeter instead of investing in detection and response, the more breaches can fuel cost of living increases." said Charles Henderson, Global Head of IBM Security X-Force. "This report shows that the right strategies coupled with the right technologies can help make all the difference when businesses are attacked."

Over-trusting Critical Infrastructure Organizations
Concerns over critical infrastructure targeting appear to be increasing globally over the past year, with many governments' cybersecurity agencies urging vigilance against disruptive attacks. In fact, IBM's report reveals that ransomware and destructive attacks represented 28% of breaches amongst critical infrastructure organizations studied, highlighting how threat actors are seeking to fracture the global supply chains that rely on these organizations. This includes financial services, industrial, transportation and healthcare companies amongst others.

Despite the call for caution, and a year after the Biden Administration issued a cybersecurity executive order that centers around the importance of adopting a zero trust approach to strengthen the nation's cybersecurity, only 21% of critical infrastructure organizations studied adopt a zero trust security model, according to the report. Add to that, 17% of breaches at critical infrastructure organizations were caused due to a business partner being initially compromised, highlighting the security risks that over-trusting environments pose.

Businesses that Pay the Ransom Aren't Getting a "Bargain"
According to the 2022 IBM report, businesses that paid threat actors' ransom demands saw $610,000 less in average breach costs compared to those that chose not to pay – not including the ransom amount paid. However, when accounting for the average ransom payment, which according to Sophos reached $812,000 in 2021, businesses that opt to pay the ransom could net higher total costs - all while inadvertently funding future ransomware attacks with capital that could be allocated to remediation and recovery efforts and looking at potential federal offenses.

The persistence of ransomware, despite significant global efforts to impede it, is fueled by the industrialization of cybercrime. IBM Security X-Force discovered the duration of studied enterprise ransomware attacks shows a drop of 94% over the past three years – from over two months to just under four days. These exponentially shorter attack lifecycles can prompt higher impact attacks, as cybersecurity incident responders are left with very short windows of opportunity to detect and contain attacks. With "time to ransom" dropping to a matter of hours, it's essential that businesses prioritize rigorous testing of incident response (IR) playbooks ahead of time. But the report states that as many as 37% of organizations studied that have incident response plans don't test them regularly.

Hybrid Cloud Advantage
The report also showcased hybrid cloud environments as the most prevalent (45%) infrastructure amongst organizations studied. Averaging $3.8 million in breach costs, businesses that adopted a hybrid cloud model observed lower breach costs compared to businesses with a solely public or private cloud model, which experienced $5.02 million and $4.24 million on average respectively. In fact, hybrid cloud adopters studied were able to identify and contain data breaches 15 days faster on average than the global average of 277 days for participants.

The report highlights that 45% of studied breaches occurred in the cloud, emphasizing the importance of cloud security. However, a significant 43% of reporting organizations stated they are just in the early stages or have not started implementing security practices to protect their cloud environments, observing higher breach costs2. Businesses studied that did not implement security practices across their cloud environments required an average 108 more days to identify and contain a data breach than those consistently applying security practices across all their domains.

Additional findings in the 2022 IBM report include:

  • Phishing Becomes Costliest Breach Cause – While compromised credentials continued to reign as the most common cause of a breach (19%), phishing was the second (16%) and the costliest cause, leading to $4.91 million in average breach costs for responding organizations.
  • Healthcare Breach Costs Hit Double Digits for First Time Ever– For the 12th year in a row, healthcare participants saw the costliest breaches amongst industries with average breach costs in healthcare increasing by nearly $1 million to reach a record high of $10.1 million.
  • Insufficient Security Staffing – Sixty-two percent of studied organizations stated they are not sufficiently staffed to meet their security needs, averaging $550,000 more in breach costs than those that state they are sufficiently staffed.

Additional Sources

  • To obtain a copy of the 2022 Cost of a Data Breach Report, please visit: https://www.ibm.com/security/data-breach.
  • Read more about the report's top findings in this IBM Security Intelligence blog.
  • Sign up for the 2022 IBM Security Cost of a Data Breach webinar on Wednesday, August 3, 2022, at 11:00 a.m. ET here.
  • Connect with the IBM Security X-Force team for a personalized review of the findings: https://ibm.biz/book-a-consult.

About IBM Security
IBM Security offers one of the most advanced and integrated portfolios of enterprise security products and services. The portfolio, supported by world-renowned IBM Security X-Force® research, enables organizations to effectively manage risk and defend against emerging threats. IBM operates one of the world's broadest security research, development, and delivery organizations, monitors 150 billion+ security events per day in more than 130 countries, and has been granted more than 10,000 security patents worldwide. For more information, please check www.ibm.com/security, follow @IBMSecurity on Twitter or visit the IBM Security Intelligence blog.

Press Contact:

IBM Security Communications
Georgia Prassinos
gprassinos@ibm.com

1 Cost of a Data Breach Report 2022, conducted by Ponemon Institute, sponsored, and analyzed by IBM
2 Average cost of $4.53M, compared to average cost $3.87 million at participating organizations with mature-stage cloud security practices

IBM Corporation logo. (PRNewsfoto/IBM)

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SOURCE IBM

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