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Posts from the ‘Louis Columbus’ blog’ Category

Top 25 IoT Startups To Watch In 2019

 

  • 26,792 startups are relying on IoT as one of their main technologies to launch new products and services and support platform-based business models according to Crunchbase.
  • 78.4% of IoT startups Crunchbase tracks have had two funding rounds or less with seed, angel and early-stage rounds being the most common.
  • IoT startup funding reached $16.7B in Q4, 2018, with last years’ funding levels 94% over 2017 according to Venture Scanner.
  • By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation and logistics, and utilities according to the Boston Consulting Group.

The most successful IoT startups selling into enterprises excel at orchestrating analytics, Artificial Intelligence (AI), and real-time monitoring to deliver exceptional customer experiences. As a group, these top 25 IoT startups are showing early potential at enabling profitable new business models, revitalizing industries that have experienced single single-digit growth recently. Each of these startups is taking a unique approach to solving some of the enterprises’ most challenging problems, and in so doing creating valuable new patents that further fuel IoT adoption and growth.

The top 25 startups are concentrating on how to make IoT a growth catalyst for enterprises by designing in AI integration at the platform level. McKinsey found that 27% of AI early adopters are more likely to report using AI to grow their market than companies only experimenting with or partially adopting AI. 52% are more likely to report using it to increase their market share. These and many other survey results are from McKinsey Global Institute’s Artificial Intelligence: The Next Digital Frontier? (PDF, 80 pp., no opt-in).

Top 25 IoT Startups To Watch In 2019

The following list of 25 IoT startups are based on an analysis of their ability to attract new customers, current and projected revenue growth, patents’ current value and potential, and position in their chosen markets. Presented below are the top 25 IoT startups to watch this year:

  1. Armis Security – Armis takes a unique approach to provide visibility into IoT-enabled devices that are unmanaged across an IT network. The company’s solutions treat every IoT device as a threat surface, enabling enterprises to prohibit access to IoT devices and networks based on security guidelines. Another unique aspect of this company’s approach to deployment is the ability to use an enterprises’ existing infrastructure for rapid deployments. Founded in 2015 the company has active customers in finance, healthcare, manufacturing, and high technology industries. Armis Security has raised a total of $47M in funding over 3 Their latest funding was raised on Apr 9, 2018, from a Series B round of $30M from Bain Capital Ventures and Red Dot Capital Partners. Crunchbase reports Armis Security has $2.1M in revenue annually and competes with DigiCert, Skybox Security, and Aruba Networks most often in sales cycles.
  2. Crate.io – Crate.io’s open source SQL database features integrated search for storing and analyzing machine data in real time. The company was founded in 2013 with the purpose of providing SQL developers with an open source SQL database to capture, analyze and manage their machine learning and AI-based data. CrateDB is an open source distributed database offering the scalability and performance of NoSQL with the power and ease of standard SQL. The CrateDB Cloud for Azure IoT is a turnkey data layer, offered as a hosted cloud service on Azure, enabling faster development of IoT platforms and data-driven smart factories. Most CrateDB customers use it for operational analytics workloads, performing fast time series, geospatial, text search, machine learning queries against streams of data and data at rest in Industrial IoT, enterprise cybersecurity & systems monitoring in all industries, smart city and building infrastructure, Vehicle fleet tracking & management and marketing analytics. The company has raised $17.9M in funding over 4 rounds.
  3. Dragos – Dragos specializes in industrial (ICS/IIoT) cybersecurity. Their cloud-based Dragos Platform collects, detects, and automates asset inventorying and visualization, threat detection through threat behavior analytics, and security operations and incident response workflows. Dragos also has a Threat Operations Center that provides customers access to dedicated ICS incident response and threat hunting services as well as industrial specific intelligence reporting on vulnerabilities, threats, and community events. Dragos has raised a total of $48.2M in funding over 3 Their latest funding was raised on Nov 14, 2018, from a $37M Series B round with Canaan Partners.
  4. Drayson Technologies – Drayson Technologies provides an IoT platform startup that is combining wireless charging technology and machine learning software to create smart sensor networks that deliver greater energy and cost efficiencies to its customers. Drayson is known for its expertise in energy-efficient and cost-effective IoT data collection and analysis, which also contributes to their customers’ ability to reduce the cost of deploying, owning and running IoT networks.
  5. Element Analytics –Element Analytics is rapidly establishing itself as a startup to watch in the fields of chemicals & refining, manufacturing, metals & mining, pulp & paper, and upstream oil & gas. Their Element Platform helps industrial organizations easily and rapidly use industrial time-series data to improve production efficiency and product quality. Their platform prepares time-series data, enriches it with analytically relevant context, creating greater contextual insights. The Element Analytics platform also enables machine-learning modeling to surface reliability, productivity, and sustainability insights for operations. Element Analytics has raised a total of $22M in funding over 3 Their latest funding was raised on Jan 8, 2018, from a Series A round. Kleiner Perkins participated in the first two rounds, funding a total of $7M.
  6. FogHorn – FogHorn is a fascinating startup to watch because they excel at embedding real-time analytics and machine-learning support into size- and space- constrained commercial and industry IoT application areas. Realizing that industrial manufacturing and distribution sites often have unreliable Internet connections if they have any at all, Foghorn has designed a miniaturized, scalable complex-event processing (CEP) software engine that is capable of producing analytics in real-time. The FogHorn Lightning™ platform includes the CEP software engine, enabling high-performance edge computing, advanced analytics, Machine Learning, and AI to be implemented highly constrained environments of IIoT. The company has also created a new class of high-performance programming language called Vel ™ which transforms any gateway, programmable logic controller (PLC), industrial PC, or another edge device into an advanced edge computing system. FogHorn has raised a total of $47.5M in funding over 4 Their latest funding was raised on Oct 4, 2017, from a Series B round. The FogHorn Technology Platform is shown below:

  1. GEM – GEM specializes in providing IoT, analytics, and machine learning platforms and solutions for the manufacturing industry, with a specific focus on Overall Equipment Effectiveness (OEE) and predictive maintenance. The company has been able to gain customers in energy, retail, and GEM’s value proposition is based on their ability to increase manufacturers’ OEE levels through greater real-time insights. The GEM Precare platform captures operational data and KPIs in real-time including availability, OEE, performance, quality, MTBF, MTBA, machine statuses, status reasons, and alarms. The following is an example of the GENM technology platform:

  1. IoTium – This is a fascinating company to track due to their patented technology that enables secure connections between Network as a Service (NaaS), legacy onsite systems and cloud-based applications. Customers include CBRE, Emerson, Intelligent Buildings, Obernel, Rexnord, and Sunbelt Controls. IoTium is well positioned to gain new customers in building and industrial automation, oil & gas, manufacturing, transportation, and smart city industries. IoTium has raised a total of $22M in funding over 2 rounds with investors GE Ventures, March Capital, and Juniper Networks. Their latest funding was raised on Sep 19, 2018, from a Series B round.
  2. InfluxData – InfluxData created InfluxDB, their Open Source Platform specifically designed to analyze metrics and events (time series data) for DevOps and IoT applications. Whether the data comes from humans, sensors, or machines, InfluxData enables developers to build monitoring, analytics, and IoT applications at scale, delivering measurable business value quickly. The company reports having 400 customers including Cisco, eBay, IBM, and InfluxData has raised a total of $59.9M in funding over 4 rounds. Their latest funding was raised on Feb 13, 2018, from a Series C round.
  3. Karamba Security – Karamba Security is focused on solving the security challenges of connected vehicles. The company offers Electronic Control Unit (ECU) endpoint security to protect any vehicle with an IoT connection or IP address. What makes this startup so interesting is how they are using patented technologies to reduce IoT-based attacks on vehicles by blocking them autonomously. Internet connectivity or extensive developer work is not needed to implement Karamba across a vehicle fleet. Each device can be reset to its factory settings, eliminating the threat of a vehicle being hacked. Karamba Security has raised a total of $27M in funding over 4 Their latest funding was raised on Apr 10, 2018, from a Series B round.
  4. MachineMetrics – What makes MachineMetrics an interesting company to watch is their innovative approach to using Artificial Intelligence (AI) to discover new insights into manufacturer’s data that improve product quality and performance. It’s one of the first startups to combine Industrial Internet of Things (IIoT) and AI and provide a scalable platform for discrete manufacturers and heavy equipment builders. They’ve also developed an expertise at edge connectivity in manufacturing environments that have enabled greater real-time visibility and more meaningful manufacturing analytics than has been possible in the past. They’re using AI to drive their prescriptive and predictive alerts. MachineMetrics has raised a total of $13.4M in funding over 3 Their latest funding was raised on Dec 11, 2018, from a Series A round. The following is a Workstation View from the MachineMetrics Production platform:

  1. MagicCube – MagicCube is a device independent IoT security platform that protects against on-device, cloud, and network attacks. The MagicCube solution secures digital transactions on any device, in transit, and in the cloud with the same level of security as device hardware solutions without the complexity and cost associated with hardware deployments. MagicCube, Inc. has raised a total of $10.7M in funding over 2 Their latest funding was raised on Aug 8, 2017, from a Series A round.
  2. Myriota – What makes Myriota a fascinating company to watch is their innovative advances in ultra-low-cost satellite Internet of Things (IoT) connectivity and the alliances they are creating, including on with SpaceX. Myriota’s nano-satellite was launched into space aboard the SpaceX Falcon 9 rocket in December 2018. Myriota uses exactEarth’s Low Earth Orbit (LEO) satellite constellation for its connectivity solutions. Myriota is a global leader in low-cost satellite IoT connectivity, providing aggregated sensor reading, environmental sensing, and online tracking and condition monitoring of remote assets. The company has raised a total of $15M in funding over 1 round. This was a Series A round raised on Mar 26, 2018.
  3. Particle – Particle is an Internet of Things (IoT) device platform that enables organizations to develop and fine-tune connectivity across operations using scalable APIs and software development resources. Particle’s development platform is designed to provide organizations with the tools they need to prototype IoT solutions to scale quickly and securely. Over 150,000 product builders in more than 170 countries and half of the Fortune 500 have deployed connected IoT devices powered by Particle. Particle’s customers include NASA, SpaceX, consumer hot tub manufacturer Jacuzzi, and Venture-backed by Root Ventures, Spark Capital, Qualcomm Ventures, and Particle is based in San Francisco, CA and Shenzhen, China. Particle has raised a total of $35.8M in funding over 7 rounds. Their latest funding was raised on Jul 19, 2017, from a Series B round.
  4. Samsara – What makes Samsara noteworthy is their prioritizing how sensor data can increase the safety and efficiency of physical operations, contributing to productivity gains while reducing costs. Samsara is attracting customers from the transportation, logistics, construction, food production, energy, and manufacturing industries with their ability to improve the safety, efficiency, and quality of operations. Samsara builds sensor systems that combine wireless sensors with remote networking and cloud-based analytics. As of February 2019, the company has over 5,000 customers and has a run rate of 200,000 new devices being added every year. Samsara has raised a total of $230M in funding over 5 Their latest funding was raised on Dec 28, 2018, from a Series E round. An example of the company’s Fleet Summary is shown below:

  1. SCADAfence – SCADAfence provides cybersecurity solutions designed to ensure the operational continuity of industrial (ICS/SCADA) networks. The startup excels at integrating Industrial IoT, analytics, realtime monitoring and machine-to-machine connectivity to provide scalable cybersecurity solutions for production networks. As of February 2019 the company has customers in the pharmaceutical, chemical, food & beverage and automotive industries. SCADAFence offers a solution suite that includes continuous real-time monitoring of the industrial environment as well as lightweight tools designed to automate the process of security assessment. The suite provides visibility of day-to-day operations, detection of cyber-attacks and forensics tools designed to improve responsiveness. SCADAfence has raised a total of $10M in funding over 3 Their latest funding was raised on Nov 21, 2017, from a Series A round.
  2. SequoiaDB – SequoiaDB develops and provides commercial support for the open source database SequoiaDB, a document-oriented NewSQL database that supports JSON transaction processing and SQL query. Their database can either be a standalone product to interface with applications providing high performance and horizontally scalable data storage and processing functions or serve as the frontend of Hadoop and Spark for both real-time query and data analysis. It is designed to integrate with Spark, Hadoop/Cloudera. SequoiaDB has raised a total of $40M in funding over 3 Their latest funding was raised on Sep 19, 2018, from a Series C round.
  3. Sight Machine – This is a fascinating startup to watch, I’ve been tracking Sight Machine for several years. The company is succeeding at attracting Fortune 500-level manufacturers as clients by providing them with AI-driven insights into how they can improve operations. Sight Machine’s AI and analytics platform, purpose-built for discrete and process manufacturing, uses artificial intelligence, machine learning, and advanced analytics to help address critical challenges in quality and productivity throughout the enterprise. The platform is powered by the industry’s only Plant Digital Twin, which enables real-time visibility and actionable insights for every machine, line, and plant throughout an enterprise. Sight Machine is optimized to run on the major cloud platforms including AWS, Google Cloud Platform, and Microsoft Azure. The company has raised a total of $30.5M in funding over 5 Their latest funding was raised on Dec 23, 2017, from a Series B round. An example of a Sight Machine dashboard is shown below:

  1. Splice Machine –. Splice Machine provides an open-source dual-engine RDBMS for mixed operational and analytical workloads, powered by Apache Hadoop® and Apache Spark™. The Splice Machine RDBMS executes operational workloads on Apache HBase® and analytical workloads on Apache Spark. Splice Machine is known for its ease of development and use for IoT-based applications and is successfully offload operational and analytical workloads from Oracle, Teradata, and Netezza legacy systems. The company excels at ETL, operational reporting or real-time applications and use cases. Splice Machine has raised a total of $40M in funding over 4 Their latest funding was raised on Dec 20, 2017, from Salesforce Ventures.
  2. SWIM.AI – Swim provides edge-based software that executes real-time analytics and machine learning for enterprises, equipment manufacturers, smart-cities, and IoT and IIoT businesses. Its software locally processes and analyzes massive volumes of streaming data from devices/sensors/equipment where it is created, reducing network volumes, and generating real-time machine-learning business insights. Swim deploys its software at the edge to transform data into insights in real-time and delivers them to businesses, staff, operators, and customers. Swim has successfully been deployed and is in use in existing equipment and brownfield environments. In manufacturing customers’ operations Swim is improving real-time synchronization across multiple systems, reduce project implementation costs, optimizing efficiency using machine learning insights from full resolution edge data and making insights available via real-time APIs. Swim.ai has raised a total of $10M in funding over 2 rounds. Their latest funding was raised on Jul 17, 2018, from a Series B round. Swim’s model is shown below:

  1. Tulip – Tulip was started by a team of engineers out of the MIT Media Lab, and the company’s platform is based on over ten years of research in digital manufacturing. Their self-service technology fills the gap between rigid back-end manufacturing IT systems and the dynamic operations taking place on the shop floor. Tulip’s Manufacturing App Platform combines research in intelligent hardware sensors, computer vision, assistive user interfaces, and applied machine learning. Tulip was launched to bring these latest technological developments from the lab to the factory floor. Today, Tulip’s Manufacturing App Platform is deployed at dozens of global customers in six countries across multiple industries including Electronics, Aerospace & Defense, Medical Devices, Footwear, Pharmaceuticals, and Contract Manufacturing. Tulip Interfaces has raised a total of $13M in funding over 3
  2. Tuya Smart – Tuya Smart is an IoT solution provider for device manufacturers. Their platform enables fast, agile app development, allowing smart device manufacturers to bring their product to market quickly and at competitive prices. Tuya Smart is founded by Jerry Wang, a founding executive of AliYun, Alibaba’s cloud division, along with a group of veterans from Alibaba, Baidu and Haier Electronics. With extensive knowledge in cloud computing, software development, and hardware and supply chain management, Tuya Smart’s team is enabling manufacturers to produce next-generation smart, connected products. Tuya has raised a total of $200M in funding over 3 Their latest funding was raised on Jul 24, 2018, from a Series C round.
  3. Uptake – Uptake Technologies provides a predictive analytics and asset performance management (APM) platform gaining traction in key industrial IoT market segments today. The Uptake platform analyzes data from inside a company and from third party sources to predict and prevent failures, uncover hidden profits, and discover new opportunities to healthcare, insurance, locomotives, construction, manufacturing, and other industries. Uptake Technologies offers a platform for equipment monitoring, diagnostic troubleshooting, event, and condition prediction, and task management to improve uptime, streamline operations, and spot growth opportunities. Key customers include Caterpillar, Progress Rail, Berkshire Hathaway Energy, and the U.S. Army.
  4. VDOO– VDOO has developed a platform of automated solutions to help IoT makers put the right security in their devices before release and enable post-deployment security. The end-to-end platform takes the maker from security analysis to implementation guidance to certification and enables IoT makers to quickly add the right security to their devices with minimal resources. VDOO’s solution is built upon a comprehensive taxonomy of IoT devices and consists of five interrelated and integrated products including the Security Requirements Generator, Security Gap Analysis, Actionable Security Plan, Certification, and Post-Deployment Security Enablement. VDOO has raised a total of $13M in funding over 1 round. This was a Series A round raised on Jan 17, 2018.
  5. Xage Security – Xage provides decentralized security services for industrial manufacturing and distribution businesses including oil and gas, transportation, and utilities. The Xage architecture relies on blockchain to provide a distributed, scalable and highly reliable data store that prevents hackers from attacking and gaining access through any threat surface in an organization. Xage takes a unique approach to using blockchain to thwart hacking attempts at scale, by simultaneously protecting every active ledger in an organization. Xage Security has raised a total of $16M in funding over 2 Their latest funding was raised on Dec 28, 2018, from a Series A round.
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Digital Transformation’s Missing Link Is Zero Trust

    • Enterprises will invest $2.4T by 2020 in digital transformation technologies including cloud platforms, cognitive systems, IoT, mobile, robotics, and integration services according to the World Economic Forum.
    • Digital transformation software and services revenue in the U.S. is predicted to reach $490B in 2025, soaring from $190B in 2019, attaining a Compound Annual Growth Rate (CAGR) of 14.49% according to Grand View Research published by Statista.
    • IDC predicts worldwide spending on the technologies and services that enable the digital transformation of business practices, products, and organizations will reach $1.97T in 2022.
    • Legacy approaches to Privileged Access Management (PAM) don’t protect the new threatscapes digital transformation initiatives create, making Zero Trust Privilege essential for enterprises.

B2B customers, including manufacturers looking to replace legacy production equipment with smart, connected machines, have high expectations when it comes to product quality, ease of integration, and intuitive user experiences. Replacing factories full of legacy assets with smart, connected machinery is one of the most powerful catalysts driving digital transformation today. Innovative smart, connected machinery and the performance gains they provide are the oxygen that keeps customer relationships alive. That’s why digital transformation forecasts from the World Economic Forum, Grand View ResearchIDC, and many others predict perennial growth. The many forecasts reflect a fundamental truth: digital transformation done with intensity creates a customer-driven renaissance for any business.

Businesses digitally transforming themselves are succeeding because they’ve made themselves accountable and transparent to customers. Earning and protecting that trust is the heartbeat of any business’ growth. 51% of enterprises invest in digital transformation to capture growth opportunities in new markets, with 46% investing to stay in front of evolving customer behaviors and preferences. Brian Solis’ excellent report, The State of Digital Transformation, 2018 – 2019 Edition (31 pp., PDF, opt-in) shows how digitally transforming any business with the customer first leads to greater growth. The graphic from his study illustrates this point:

 

Closing The Digital Transformation Gap With Zero Trust

Gaps exist between the results digital transformation initiatives are delivering today, and the customer-driven value they’re capable of. According to Gartner, 75% of digital transformation projects are not aligned internally today, leading to delayed new product launches, mediocre experiences, and greater security risks than ever before. Interactive, IoT-enabled experiences and products are expanding the threatscape of enterprises to include Big Data, cloud, containers, DevOps, IoT systems, and more. With that comes a host of new exposure points, many of which allow access to sensitive data that must be protected with modern Privileged Access Management solutions that reduce risk in these modern enterprise use cases.

The new security perimeter is identity. Forrester estimates that 80% of data breaches are caused by privileged access abuse. Every smart, connected machine that replaces legacy production equipment is another identity that defines a manufacturer’s security perimeter.

As the use cases and adoption of smart, connected machines proliferate, so too does the urgency that manufacturers need to replace their legacy approaches to Privileged Access Management (PAM). Relying on outdated strategies for protecting administrative access to all machines needs to be replaced with a “never trust, always verify, enforce least privilege” approach.

IT needs to improve how they’re protecting the most privileged access credentials, the ‘keys to the kingdom,’ by granting just-enough, just-in-time privilege. Of the many cybersecurity approaches available today, Zero Trust Privilege (ZTP) enables IT to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

The more diverse any digital transformation strategy, the greater the risk of privileged credential abuse. Thwarting privileged credential abuse needs to start with a least privilege access approach, minimizing each attack surface, improving audit and compliance visibility while reducing risk, complexity, and costs. Leaders in Zero Trust include CentrifyMobileIronPalo Alto Networks, and others. Of these companies, Centrify’s approach to Zero Trust to prevent privileged access abuse shows the greatest potential for securing digital transformation initiatives and strategies.

How To Secure Digital Transformation Strategies

IDG Research found in their Security Priorities for 2018 study that 71% of security-focused IT decision-makers are aware of the Zero Trust model and 18% of enterprises are either running pilots or have implemented Zero Trust.

Zero Trust Privilege (ZTP) is the force multiplier digital transformation initiatives need to reach their true potential by securing administrative access to the complex mix of machinery and infrastructure – and the sensitive data they hold and use – that manufacturers rely on daily.

Starting with a strategic perspective, ZTP’s contribution to securing digital transformation deployments apply to every area of planning, pilots, platforms, product, and service data being designed to stop the leading cause of breaches, which is privileged credential abuse. The following graphic illustrates how ZTP needs to span every aspect of an enterprise’s digital transformation capabilities.

Source: World Economic Forum, Digital Transformation Initiative, May 2018

Conclusion

By 2020, 30% of Global 2000 companies will have allocated capital budget equal to at least 10% of revenue to fuel their digital transformation strategies according to IDC.  European spending on technologies and services that enable the digital transformation of business practices, products, and organizations is forecasted to reach $378.2B in 2022. The perennial growth these forecasts promise is predicated on enterprises delivering new experiences and innovative products, which create the oxygen that keeps their customer relationships alive.

Amidst all the potential for growth, enterprises need to realize every new infrastructure element, machine, or connected production asset is a new identity that collectively comprises the fabric of their security perimeter. Legacy cybersecurity approaches won’t scale to protect the proliferating number of smart machines being put into use today. Relying entirely on legacy approaches to PAM, where privileged access to systems and resources only inside the network are secure, is failing today. Smart, connected machinery and the products and experiences they deliver require an entirely new cybersecurity strategy, one based on a “never trust, always verify, enforce least privilege” approach. Centrify Zero Trust Privilege shows potential to meet this challenge by granting least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

Top 10 Ways Internet Of Things And Blockchain Strengthen Supply Chains

  • The majority of enterprises are prioritizing their blockchain pilots that concentrate on supply chains improvements (53%) and the Internet of Things (51%) according to Deloitte’s latest blockchain survey.
  • By 2023, blockchain will support the global movement and tracking of $2T of goods and services annually based on a recent Gartner
  • By 2020, Discrete Manufacturing, Transportation & Logistics and Utilities industries are projected to spend $40B each on IoT platforms, systems, and services.
  • The Supply Chain Management enterprise software market is growing from $12.2B in 2017 to $20.4B in 2022, achieving a 10.7% Compound Annual Growth Rate (CAGR) according to Gartner’s latest market forecast.
  • Of the many blockchain and IoT Proof of Concept (POC) pilots running today, track-and-trace shows the most significant potential of moving into production.

Combining blockchain’s distributed ledger framework with the Internet of Things’ (IoT) proven real-time monitoring and tracking capability is redefining supply chains. Blockchain shows potential for increasing the speed, scale, and visibility of supply chains, eliminating counterfeit-goods transactions while also improving batching, routing and inventory control. Blockchain’s shared, distributed ledger architecture is becoming a growth catalyst for IoT’s adoption and commercial use in organizations.

Blockchain and IoT are defining the future of supply chains based on the initial success of Proof of Concept (POC) pilots focused on the logistics, storage and track-and-trace areas of supply chains across manufacturing. Supply-chain centric pilots are the most popular today, with enterprises looking at how they can get more value out of IoT using blockchain. One CIO told me recently his company deliberately spins up several POCs at once, adding “they’re our proving grounds, we’re pushing blockchain and IoT’s limits to see if they can solve our most challenging supply chain problems and we’re learning a tremendous amount.” The senior management team at the manufacturer says the pilots are worth it if they can find a way to increase inventory turns just 10% using blockchain and IoT. They’re also running Proof of Concept pilots to optimize batching, routing and delivery of goods, reduce fraud costs, and increase track-and-trace accuracy and speed. Of the many pilots in progress, track-and-trace shows the greatest potential to move into production today.

The following are the top 10 ways IoT and blockchain are defining the future of supply chains:

  • Combining IoT’s real-time monitoring support with blockchain’s shared distributed ledger strengthens track-and-trace accuracy and scale, leading to improvements across supply chains. Improving track-and-trace reduces the need for buffer stock by providing real-time visibility of inventory levels and shipments. Urgent orders can also be expedited and rerouted, minimizing disruptions to production schedules and customer shipments.  The combination of blockchain and IoT sensors is showing potential to revolutionize food supply chains, where sensors are used to track freshness, quality, and safety of perishable foods.  The multiplicative effects of combining IoT and blockchain to improve track-and-traceability are shown in the context of the following table from the Boston Consulting Group. Please click on the graphic to expand for easier reading.

  • Improving inventory management and reducing bank fees for letters of credit by combining blockchain and IoT show potential to deliver cost savings. A recent study by Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, completed a hypothetical analysis of how much a $1B electronics equipment company implementing blockchain-as-a-service, a decentralized track-and-trace application, and 30 nodes that share among key supply chain stakeholders could save. The study found that the electronics equipment company could save up to $6M a year or .6% of annual sales. A summary of the business case is shown here:

  • Combining blockchain and IoT is providing the pharmaceutical and healthcare industry with stronger serialization techniques, reducing counterfeit drugs and medical products. Pharmaceutical serialization is the process of assigning a unique identity (e.g., a serial number) to each sealable unit, which is then linked to critical information about the product’s origin, batch number, and expiration date. According to the World Health Organization (WHO) approximately 1 million people each year die from counterfeit drugs, 50% of pharmaceutical products sold through rogue websites are considered fake, and up to 30% of pharmaceutical products sold in emerging markets are counterfeit according to a recent study by DHL Research. DHL and Accenture are finalizing a blockchain-based track-and-trace serialization prototype comprising a global network of nodes across six geographies. The system comprehensively documents each step that a pharmaceutical product takes on its way to the store shelf and eventually the consumer. The following graphic illustrates the workflow.

  • Improving distribution and logistics, tracking asset maintenance, improving product quality, preventing counterfeit products and enabling digital marketplaces are the use cases Capgemini predicts blockchain will have the greatest impact. IoT’s potential contribution in each of these five use case areas continues to accelerate as real-time monitoring dominates manufacturing. Tracking provenace, contracts management, digital threads, and trade financing also show potential for high adoption. The following graphic illustrates blockchain use cases in the supply chain.

  • Combining blockchain and IoT is enabling manufacturers to pursue and excel at digital twin initiatives across their value chains. A digital twin is a dynamic, digital representation of a physical asset which enables companies to track its past, current and future performance throughout the asset’s lifecycle. The asset, for example, a vehicle or spare part, sends performance data and events directly to its digital twin, even as it moves from the hands of the manufacturer to the dealer and ultimately the new owner. Blockchain can be used to securely document everything related to the asset and IoT provides the real-time monitoring and updates. Microsoft and VISEO are partnering to use blockchain to connect each new vehicle’s maintenance events to the vehicle’s digital twin. The graphic below illustrates how digital twins streamline additive manufacturing.

  • 54% of suppliers and 51% of customers are expecting the organizations they do business with to take a leadership position on blockchain and IoT. The majority of suppliers and customers expect the manufacturers, suppliers, and vendors they do business with to take a leadership position on these two emerging technologies and define a vision with them in it. Deloitte’s excellent study, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey, provides insights into how supplier and customer expectations are a factor in driving blockchain and IoT adoption, further helping to shape the future of supply chains.

  • Consumer products and manufacturing lead adoption of blockchain today, followed by life sciences according to the latest Deloitte estimates. IoT adoption is flourishing in manufacturing, transportation & logistics and utilities. By 2020, each of these industries is projected to spend $40B each on IoT platforms, systems, and services. The following graphic compares blockchain adoption levels by industry. Given how dependent manufacturers are on supply chains, the high adoption rates for blockchain and IoT make sense. Please click on the graphic to expand for easier reading.

  • 32% of enterprises are adopting blockchain to gain greater speed compared to existing systems, and 28% believe blockchain will open up new business models and revenue sources. The majority of manufacturers, transportation & logistics and utilities companies have real-time monitoring running on their shop floors and across their production facilities today. Many are transitioning from Wi-Fi enabled monitoring to IoT, which creates a real-time data stream that blockchain ledgers categorize and track to provide greater track-and-trace speed and accuracy. A recent Capgemini survey found that 76% of manufacturers also plan to have a product-as-a-service strategy to drive revenue in less than two years.

  • Blockchain has the potential to deliver between $80B and $110B in value across seven strategic financial sectors when supported by IoT, redefining their supply chains in the process. McKinsey completed an extensive analysis of over 60 viable use case for blockchain in financial services where IoT would provide greater visibility across transactions. The combination of technologies has the potential to deliver over $100B in value.

  • Reducing product waste and perishable foods’ product margins while increasing traceability is attainable by combining blockchain and IoT. IBM’s Food Trust uses blockchain technology to create greater accountability, traceability, and visibility in supply chains. It’s the only consortium of its kind that connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data. Partners include Carrefour, Dole, Driscoll’s, Golden State Foods, McCormick and Co., McLane Co., Nestlé, ShopRite parent Wakefern Food Corp.,  grocery group purchasing organization Topco Associates  The Kroger Co., Tyson Foods, Unilever and Walmart. An example of the Food Trust’s traceability application is shown below:

Additional Research:

Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems86, 614–628.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, By Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra. December 18, 2018

Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust?, 2018 (PDF, 32 pp., no opt-in)

DHL Trend Research, Blockchain In Research,  Perspectives on the upcoming impact of blockchain technology and use cases for the logistics industry (PDF, 28 pp., no opt-in)

Deloitte, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey,48 pp., PDF, no opt-in. Summary available here.

Deloitte, Continuous Interconnected Supply Chain, Using Blockchain & Internet-of-Things in supply chain traceability (PDF, 24 pp., no opt-in)

Deloitte University Press,  3D opportunity for blockchain Additive manufacturing links the digital thread, 2018 (PDF, 20 pp, no opt-in)

EBN, How IoT, AI, & Blockchain Empower Tomorrow’s Autonomous Supply Chain, June 18, 2018

Forbes, How Blockchain Can Improve Manufacturing In 2019, October 28, 2018.

Forbes, 10 Charts That Will Challenge Your Perspective Of IoT’s Growth, June 6, 2018

Gettens, D., Jauffred, F., & Steeneck, D. W. (2016). IoT Can Drive Big Savings in the Post-Sales Supply Chain. MIT Sloan Management Review, 60(2), 19–21. Accessible on the MIT Sloan Management Review site here.

Jagtap, S., & Rahimifard, S. (2019). Unlocking the potential of the internet of things to improve resource efficiency in food supply chains. Springer International Publishing© Springer Nature Switzerland AG.

McKinsey & Company, Blockchain beyond the hype: What is the strategic business value?, June, 2018

McKinsey & Company, Blockchain Technology in the Insurance Sector, Quarterly meeting of the Federal Advisory Committee on Insurance (FACI) Jan 5, 2017

McKinsey & Company, The IoT as a growth driver, By Markus Berger-De Leon, Thomas Reinbacher, and Dominik Wee. March 2018

McKinsey & Company, How digital manufacturing can escape ‘pilot purgatory’,  by Andreas Behrendt, Richard Kelly, Raphael Rettig, and Sebastian Stoffregen. July 2018

Miller, D. (2018). Blockchain and the Internet of Things in the Industrial Sector. IT Professional20(3), 15-18.

PwC, Global Blockchain Survey, 2018.

Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management46, 70-82.

Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems88, 173–190

Smith, K. J., & Dhillon, G. (2019). Supply Chain Virtualization: Facilitating Agent Trust Utilizing Blockchain Technology. In Revisiting Supply Chain Risk (pp. 299-311). Springer, Cham.

Tu, M., Lim, M. K., & Yang, M.-F. (2018). IoT-based production logistics and supply chain system – Part 1. Industrial Management & Data Systems118(1), 65–95.

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Wall Street Journal, 5 Supply Chain Use Cases for IoT, Blockchain, November 8, 2018

How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility

Bottom Line: Manufacturers’ most valuable data is generated on shop floors daily, bringing with it the challenge of analyzing it to find prescriptive insights fast – and an ideal problem for machine learning to solve.

Manufacturing is the most data-prolific industry there is, generating on average 1.9 petabytes of data every year according to the McKinsey Global Insititute. Supply chains, sourcing, factory operations, and the phases of compliance and quality management generate the majority of data.

The most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory. Manufacturing’s massive problem is in getting quality inspection results out fast enough across brands & retailers, other factories, suppliers and vendors to make a difference in future product quality.

How A Machine Learning Startup Is Revolutionizing Product Inspections

Imagine you’re a major brand or retailer and you’re relying on a network of factories across Bangladesh, China, India, and Southeast Asia to produce your new non-food consumer goods product lines including apparel. Factories, inspection agencies, suppliers and vendors that brands and retailers like you rely on vary widely on ethics, responsible sourcing, product quality, and transparency. With your entire consumer goods product lines (and future sales) at risk based on which suppliers, factories and product inspection agencies you choose, you and your companies’ future are riding on the decisions you make.

These career- and company-betting challenges and the frustration of gaining greater visibility into what’s going on in supply chains to factory floors led Carlos Moncayo Castillo and his brothers Fernando Moncayo Castillo and Luis Moncayo Castillo to launch Inspectorio. They were invited to the Target + Techstars Retail Accelerator in the summer of 2017, a competition they participated in with their cloud-based inspection platform that includes AI and machine learning and pervasive support for mobile technologies. Target relies on them today to bring greater transparency to their supply chains. “I’ve spent years working in non-food consumer goods product manufacturing seeing the many disconnects between inspections and suppliers, the lack of collaboration and how gaps in information create too many opportunities for corruption – I had to do something to solve these problems,” Carlos said. The many problems that a lack of inspection and supply chain visibility creates became the pain Inspectorio focused on solving immediately for brands and retailers. The following is a graphic of their platform:

Presented below are a few of the many ways the combining of a scalable inspection cloud platform combined with AI, machine learning and mobile technologies are improving inspections, product quality, and supply chain visibility:

  • Enabling the creation of customized inspector workflows that learn over time and are tailored to specific products including furniture, toys, homeware and garments, the factories they’re produced in, quality of the materials used. Inspectorio’s internal research has found 74% of all inspections today are done manually using a pen and paper, with results reported in Microsoft Word, Excel or PDFs, making collaboration slow and challenging. Improving the accuracy, speed and scale of inspection workflows including real-time updates across production networks drive major gains in quality and supply chain performance.
  • Applying constraint-based algorithms and logic to understand why there are large differences in inspection results between factories is enabling brands & retailers to manage quality faster and more completely. Uploading inspections in real-time from mobile devices to an inspection platform that contains AI and machine learning applications that quickly parse the data for prescriptive insights is the future of manufacturing quality. Variations in all dimensions of quality including factory competency, supplier and production assembly quality are taken into account. In a matter of hours, inspection-based data delivers the insights needed to avert major quality problems to every member of a production network.
  • Reducing risk, the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. One of the most powerful catalysts driving inspections’ inflection point is the combination of automated workflows that deliver high-quality data that machine learning produces prescriptive insights from. And those insights are shared on performance dashboards across every brand, retailer, supplier, vendor and factory involved in shared production strategies today.
  • Matching the most experienced inspector for a given factory and product inspection drastically increases accuracy and quality. When machine learning is applied to the inspector selection and assignment process, the quality, and thoroughness of inspections increase. For the first time, brands, retailers, and factories have a clear, quantified view of Inspector Productivity Analysis across the entire team of inspectors available in a given region or country. Inspections are uploaded in real-time to the Inspectorio platform where advanced analytics and additional machine learning algorithms are applied to the data, providing greater prescriptive insights that would have ever been possible using legacy manual methods. Machine learning is also making recommendations to inspectors on which defects to look for first based on the data patterns obtained from previous inspections.
  • Knowing why specific factories and products generated more Corrective Action/Preventative Action (CAPA) than others and how fast they have been closed in the past and why is now possible. Machine learning is making it possible for entire production networks to know why specific factory and product combinations generate the most CAPAs. Using constraint-based logic, machine learning can also provide prescriptive insights into what needs to be improved to reduce CAPAs, including their root cause.

Predicting The Future Of Next-Gen Access And Zero Trust Security In 2019

Bottom Line:  The most valuable catalyst all digital businesses need to continue growing in 2019 is a Zero Trust Security (ZTS) strategy based on Next-Gen Access (NGA) that scales to protect every access point to corporate data, recognizing that identities are the new security perimeter.

The faster any digital business is growing, the more identities, devices and network endpoints proliferate. The most successful businesses of 2019 and beyond are actively creating entirely new digital business models today. They’re actively recruiting, and onboarding needed experts independent of their geographic locations and exploring new sourcing and patent ideas with R&D partners globally. Businesses are digitally transforming themselves at a faster rate than ever before. Statista projects businesses will spend $190B on digital transformation in 2019, soaring to $490B by 2025, attaining a 14.4% Compound Annual Growth Rate (CAGR) in six years.

Security Perimeters Make Or Break A Growing Business

80% of IT security breaches involve privileged credential access according to a recent Forrester study. The Verizon Mobile Security Index 2018 Report found that 89% of organizations are relying on just a single security strategy to keep their mobile networks safe. A typical data breach cost the average company $3.86M in 2018, up 6.4% from $3.62M in 2017 according to IBM Security’s latest  2018 Cost of a Data Breach Study.

The hard reality for any digital business is realizing that their greatest growth asset is how well they protect the constantly expanding perimeter of their business. Legacy approaches to securing infrastructure that relies on trusted and untrusted domains can’t scale to protect every identity and device that comprises a company’s rapidly changing new security perimeter. All these factors and more are why Zero Trust Security (ZTS) enabled by Next-Gen Access (NGA) is as essential to digital businesses’ growth as their product roadmaps, pricing strategies, and services with Idaptive being an early leader in the market. To learn more about Identity-as-a-Service please see the Forrester report, The Forrester Wave™: Identity-As-A-Service, Q4 2017 (client access required)

Predicting The Future Of Next-Gen Access And Zero Trust Security

The following are predictions of how Next-Gen Access (NGA) powered by Zero Trust Security (ZTS) will evolve in 2019:

  • Behavior-based scoring algorithms will improve markedly in 2019, improving the user experience by calculating risk scores with greater precision than before. Thwarting attacks start with a series of behavior-based algorithms that calculate a risk score based on a wide variety of variables including past access attempts, device security posture, operating system, location, time of day, and many other measurable factors. Expect to see these algorithms and the risk scores they generate using machine learning techniques improve from accuracy and contextual intelligence standpoint in 2019. Leading companies in the field including Idaptive are actively investing in machine learning technologies to accomplish this today.
  • Multifactor Authentication (MFA) adoption soars as digital businesses seek to protect new R&D projects, patents in progress, roadmaps, and product plans. State-sponsored hacking organizations and organized crime see the intellectual property in fast-growing digital businesses as among the most valuable assets they can exfiltrate and sell on the Dark Web. MFA, one of the most effective single defenses against compromised passwords, will be adopted by the most successful businesses in AI, aerospace & defense, chip design for cellular and IoT devices, e-commerce, enterprise software and more.
  • Smart, connected products without adequate security designed in will proliferate in 2019, further challenging the security perimeters of the digital businesses. The era of smart, connected products is here, with Capgemini estimating the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50% of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in). With every smart, connected device creating a new threat surface for a company, expect to see at least one device manufacturer design Zero Trust Security (ZTS) support to the board level to increase their sales into enterprises by reducing the threat of a breach starting from their device.
  • Looking for greater track and traceability, healthcare and medical products supply chains will adopt Zero Trust Security (ZTS). What’s going to make this an urgent issue in healthcare and medical products are the combined effects of greater regulatory reporting and compliance, combined with the pressure to improve time-to-market for new products and delivery accuracy for current customers. The pillars of ZTS are a perfect fit for healthcare and medical supply chains’ need for track and traceability. These pillars are real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors.
  • Real-time Security Analytics Services is going to thrive in 2019 as digital businesses seek insights into how they can fine-tune their ZTS strategies across every threat surface and machine learning algorithms improve. Many enterprises are in for an epiphany in 2019 when they see just how many potential breaches they’ve stopped using a combination of security strategies including Single Sign-On (SSO) and Multi-factor Authentication (MFA). Machine learning algorithms will continue to improve using behavior-based scoring, further improving the user experience. Leaders in the field include Idaptive who is setting a rapid pace of innovation in Real-Time Security Analytics Services.   

Conclusion

Security is at an inflection point today. Long-standing methods of protecting IT systems and a businesses’ assets can’t scale to protect every new identity, device or threat surface. When every identity is a new security perimeter, a new approach is needed to securing any digital business. The pillars of ZTS including real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors are proving to be effective at thwarting breaches and securing company’ digital assets of all kinds. It’s time for more digital businesses to see security as the growth catalyst it is and take action now to ensure their operations continue to flourish.

Microsoft Leads The AI Patent Race Going Into 2019

  • There have been over 154,000 AI patents filed worldwide since 2010 with the majority being in health fields (29.5%), Industry-specific solutions (25.3%) and AI-based digital security (15.7%).
  • AI-based marketing patents are the fasting growing global category, reaching a Compound Annual Growth Rate (CAGR) of 29.3% between 2010 and 2018.
  • The second- and third-fastest growing global AI patent categories between 2010 and 2018 are AI-based digital security (23.4% CAGR) and AI-based mobility (23% CAGR).
  • 79,936 patents were filed in the United States between 2010 and 2018, with the majority being in the health field (32.6%) followed by Industry-specific solutions (20.5%) and AI-based digital security (18%).
  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.

These and many other insights are from an excellent presentation recently given by Kai Gramke, Managing Director of EconSight titled Artificial Intelligence As A Key Technology and Driver of Technological Progress. EconSight clients include the Swiss Federal Council, German Federal Chancellery, leading European think tanks, research institutes and half of the German DAX-30 companies.  The presentation and information shared in this post were generated using the PatentSight analytics platform. PatentSight is a LexisNexis company and you can learn more about them here.  The following are the key takeaways from Kai’s recent research and presentation using PatentSight:

  • EconSight finds that Microsoft leads the AI patent race going into 2019 with 697 world class patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. The following graphic provides a comparison of the top 3o in the group. Please click on the graphic to expand it for easier reading.

  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.  Machine learning is based on the foundational concepts of Bayesian analysis, data mining, and predictive analytics. Machine learning algorithms and the applications they rely on are designed to find patterns in large-scale data sets, while also being able to solve complex, constraint-based problems by learning from the data.  Enterprise software companies including Microsoft, SAP, and others are actively developing AI technologies that integrate into their existing platforms, streamlining adoption across their many customers. Please click on the graphic to expand for easier reading.

  • There have been 225,833 AI-based patents filed globally since 2000, with 30.7% being Industry specific (Industry 4.0 on the graphic below) followed by health-related patents (28.1%) 13.8% of all AI-based patents are for digital security and 11.9% for energy. It’s interesting to note that the fastest growing patents between 2000 and 2018 are for applying AI to marketing (22% CAGR) and AI-based digital security (18.8% CAGR). Please click on the graphic to expand for easier reading.

6 Best Practices For Increasing Security In AWS In A Zero Trust World

  • Amazon Web Services (AWS) reported $6.6B in revenue for Q3, 2018 and $18.2B for the first three fiscal quarters of 2018.
  • AWS revenue achieved an impressive 46% year-over-year net sales growth between Q3, 2017 and Q3, 2018 and 49% year-over-year growth for the first three quarters of the year.
  • AWS’ 34% market share is bigger than its next four competitors combined with the majority of customers taken from small-to-medium sized cloud operators according to Synergy Research.
  • The many announcements made at AWS Re:Invent this year reflect a growing focus on hybrid cloud computing, security, and compliance.

Enterprises are rapidly accelerating the pace at which they’re moving workloads to Amazon Web Services (AWS) for greater cost, scale and speed advantages. And while AWS leads all others as the enterprise public cloud platform of choice, they and all Infrastructure-as-a-Service (IaaS) providers rely on a Shared Responsibility Model where customers are responsible for securing operating systems, platforms and data.  In the case of AWS, they take responsibility for the security of the cloud itself including the infrastructure, hardware, software, and facilities. The AWS version of the Shared Responsibility Model shown below illustrates how Amazon has defined securing the data itself, management of the platform, applications and how they’re accessed, and various configurations  as the customers’ responsibility:

Included in the list of items where the customer is responsible for security “in” the cloud is identity and access management, including Privileged Access Management (PAM) to secure the most critical infrastructure and data.

Increasing Security for IaaS in a Zero Trust World

Stolen privileged access credentials are the leading cause of breaches today. Forrester found that 80% of data breaches are initiated using privileged credentials, and 66% of organizations still rely on manual methods to manage privileged accounts. And while they are the leading cause of breaches, they’re often overlooked — not only to protect the traditional enterprise infrastructure — but especially when transitioning to the cloud.

Both for on-premise and Infrastructure-as-a-Service (IaaS), it’s not enough to rely on password vaults alone anymore. Organizations need to augment their legacy Privileged Access Management strategies to include brokering of identities, multi-factor authentication enforcement and “just enough, just-in-time” privilege, all while securing remote access and monitoring of all privileged sessions. They also need to verify who is requesting access, the context of the request, and the risk of the access environment. These are all essential elements of a Zero Trust Privilege strategy, with Centrify being an early leader in this space.

6 Ways To Increase Security in AWS

The following are six best practices for increasing security in AWS and are based on the Zero Trust Privilege model:

  1. Vault AWS Root Accounts and Federate Access for AWS Console

Given how powerful the AWS root user account is, it’s highly recommended that the password for the AWS root account be vaulted and only used in emergencies. Instead of local AWS IAM accounts and access keys, use centralized identities (e.g., Active Directory) and enable federated login. By doing so, you obviate the need for long-lived access keys.

  1. Apply a Common Security Model and Consolidate Identities

When it comes to IaaS adoption, one of the inhibitors for organizations is the myth that the IaaS requires a unique security model, as it resides outside the traditional network perimeter. However, conventional security and compliance concepts still apply in the cloud. Why would you need to treat an IaaS environment any different than your own data center? Roles and responsibilities are still the same for your privileged users. Thus, leverage what you’ve already got for a common security infrastructure spanning on-premises and cloud resources. For example, extend your Active Directory into the cloud to control AWS role assignment and grant the right amount of privilege.

  1. Ensure Accountability

Shared privileged accounts (e.g., AWS EC2 administrator) are anonymous. Ensure 100% accountability by having users log in with their individual accounts and elevate privilege as required. Manage entitlements centrally from Active Directory, mapping roles, and groups to AWS roles.

  1. Enforce Least Privilege Access

Grant users just enough privilege to complete the task at hand in the AWS Management Console, AWS services, and on the AWS instances. Implement cross-platform privilege management for AWS Management Console, Windows and Linux instances.

  1. Audit Everything

Log and monitor both authorized and unauthorized user sessions to AWS instances. Associate all activity to an individual, and report on both privileged activity and access rights. It’s also a good idea to use AWS CloudTrail and Amazon CloudWatch to monitor all API activity across all AWS instances and your AWS account.

  1. Apply Multi-Factor Authentication Everywhere

Thwart in-progress attacks and get higher levels of user assurance. Consistently implement multi-factor authentication (MFA) for AWS service management, on login and privilege elevation for AWS instances, or when checking out vaulted passwords.

Conclusion

One of the most common reasons AWS deployments are being breached is a result of privileged access credentials being compromised. The six best practices mentioned in this post are just the beginning; there are many more strategies for increasing the security in AWS.  Leveraging a solid Zero Trust Privilege platform, organizations can eliminate shared Amazon EC2 key pairs, using auditing to define accountability to the individual user account level, execute on least privilege access across every login, AWS console, and AWS instance in use, enforce MFA and enable a common security model.

Which Analytics And BI Technologies Will Be The Highest Priority In 2019?

  • 82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
  • 54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
  • 50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
  • 43%  plan to both build and buy AI and machine learning applications and platforms.
  • 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

  • 82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

  • Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

  • The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

  • 82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

  • The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

  • Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

10 Ways Machine Learning Is Revolutionizing Sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritization are the top three AI and machine learning use cases in sales.

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The Race for Sales-Focused AI & Machine Learning Patents Is On

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue aggressively invest in Research & Development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3B or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 Ways Machine Learning Is Revolutionizing Sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionizing selling today:

 

  1. AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers. Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritizing new prospects using this approach saves sales teams thousands of hours a year.
  2. Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), strengthening sales pipelines in the process. One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.
  3. Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts. Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.
  4. Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value. Analyzing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritized roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.
  5. Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights. All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.
  6. Guided Selling is progressing rapidly from a personalization-driven selling strategy to one that capitalized on data-driven insights, further revolutionizing sales. AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  7. Improving the sales team’s productivity by using AI and machine learning to analyze the most effective actions and behaviors that lead to more closed sales. AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.
  8. Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyze pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimized price more achievable.
  9. Personalizing sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning. Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalized nature of the content itself. Combining analytics, personalization and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.
  10. Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning. CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.

How To Protect Healthcare Records In A Zero Trust World

  • There’s been a staggering 298.4% growth in the reported number of patient records breached as a result of insider-wrongdoing this year alone according to Protenus.
  • The total disclosed number of breached patient records has soared from 1.1M in Q1 2018 to 4.4M in Q3 2018 alone, 680K of which were breached by insiders.
  • There were 117 disclosed health breaches in the last 90 days alone.
  • On average it’s taking 402 days to discover a healthcare provider has been breached.

Diagnosing Healthcare’s Breach Epidemic

Using access credentials stolen from co-workers or stolen laptops, unethical healthcare insiders are among the most prolific at stealing and selling patient data of any insider threat across any industry. Accenture’s study, “Losing the Cyber Culture War in Healthcare: Accenture 2018 Healthcare Workforce Survey on Cybersecurity,” found that the most common ways healthcare employees financially gain from stealing medical records is to commit tax return and credit card fraud.

Treating healthcare’s breach epidemic needs to start by viewing every threat surface, access point, identity, and login attempt as the new security perimeter. Healthcare providers urgently need to take a “never trust, always verify” approach, adopting  Zero Trust Security to protect every threat surface using Next-Gen Access for end-user credentials and Privileged Access Management (PAM) for privileged credentials. One of the leaders in Next-Gen Access is Idaptive, a newly created spin-off of Centrify. Centrify itself is offering Zero Trust Privilege Services helping over half of the Fortune 100 to eliminate privileged access abuse, the leading cause of breaches today. Centrify Zero Trust Privilege grants least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, according to a recent Accenture study. 24% of employees know of someone who has sold access to patient data to outsiders. 58% of all healthcare breaches are initiated by insiders. Confidential patient diagnosis, treatment, payment histories, and medical records are the most valuable on the Dark Web, selling for as much as $1,000 per record according to Experian.

Key insights from Protenus’ Breach Barometer illustrate how healthcare’s breach epidemic is growing exponentially:

  • There’s been a staggering 298.4% growth in the number of patient records breached as a result of insider-wrongdoing this year alone. In Q1 of this year, there were 4,597 patient records exfiltrated by insider wrong-doing, jumping to 70,562 in Q2 and soaring to 290,689 in Q3. Healthcare insiders can easily thwart healthcare systems’ legacy security approaches today by using compromised access credentials. Zero Trust Security, either in the form of Next-Gen Access for end-user credentials or Zero Trust Privilege for privileged access credentials has the potential to stop this

  • The total number of breached patient records has soared from 1.1M in Q1 of this year to 4.4M in Q3, a 58.7% jump in less than a year. Protenus found a total of 117 incidents were disclosed to U.S. Department of Health and Human Services (HHS) or the media in Q3 2018 alone. Details were disclosed for 100 of these incidents, affecting 4,390,512 patient records, the highest level ever recorded. Jumping from 1.1M medical records in Q1 to 4.4M in Q3, healthcare providers could easily see over 6.5M records breached in Q4 2018 alone.

  • Hackers targeted healthcare systems aggressively in Q3 of this year, exfiltrating 3.6M patient records in just 90 days. Compromised access credentials are hackers’ favorite technique for exfiltrating massive quantities of medical records they resell on the Dark Web or use to commit tax and credit card fraud. Healthcare providers need to minimize their attack surfaces, improve audit and compliance visibility, reduce risk, complexity, and costs across their modern, hybrid enterprises with Zero Trust. Healthcare providers need to shut down hackers now, taking away the opportunities they’re capitalizing on to exfiltrate medical records almost at will.
  • It takes 71 days on average for healthcare providers to realize their data is breached with one breach lasting over 15 years. Protenus found a wide variation in the length of time it takes healthcare providers to realize they’ve been breached and one didn’t know until 15 years after the initial successful breach. All breaches tracked by Protenus found that the insiders and/or hackers were successful in gaining access to a wealth of patient information including addresses, dates of birth, medical record numbers, healthcare providers, visit date, health insurance information, financial histories, and payment information.

Conclusion

Zero Trust is the antidote healthcare needs to treat its raging breach epidemic.  It’s exponentially growing as insiders’ intent on wrongdoing turn to exfiltrating patients’ data for personal gain. Hackers also find healthcare providers’ legacy systems among the easiest to access using stolen access credentials, exfiltrating millions of records in months. With every new employee and device being a new security perimeter on their networks, the time is now for healthcare providers to discard the old model of “trust but verify” which relied on well-defined boundaries. Zero Trust mandates a “never trust, always verify” approach to access, from inside or outside healthcare providers’ networks.

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