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Posts from the ‘Enterprise software’ Category

Machine Learning Is Helping To Stop Security Breaches With Threat Analytics

Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users’ behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up.

Separating Security Hacks Fact from Fiction

It’s time to demystify the scale and severity of breaches happening globally today. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren’t expending the time and effort to hack into systems; they’re looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon’s 2019 Data Breach Investigations Report, ‘Phishing’ (as a pre-cursor to credential misuse), ‘Stolen Credentials’, and ‘Privilege Abuse’ account for the majority of threat actions in breaches (see page 9 of the report).

It only really takes one compromised credential to potentially impact millions — whether it’s millions of individuals or millions of dollars. Undeniably, identities and the trust we place in them are being used against us. They have become the Achilles heel of our cybersecurity practices. According to a recent study by Centrify among 1,000 IT decision makers, 74% of respondents whose organizations have been breached acknowledged that it involved access to a privileged account. This number closely aligns with Forrester Research’s estimate “that at least 80% of data breaches . . . [involved] compromised privileged credentials, such as passwords, tokens, keys, and certificates.”

While the threat actors might vary according to Verizon’s 2019 Data Breach Investigations Report, the cyber adversaries’ tactics, techniques, and procedures are the same across the board. Verizon found that the fastest growing source of threats are from internal actors, as the graphic from the study illustrates below:


Internal actors are the fastest growing source of breaches because they’re able to obtain privileged access credentials with minimal effort, often obtaining them through legitimate access requests to internal systems or harvesting their co-workers’ credentials by going through the sticky notes in their cubicles. Privileged credential abuse is a challenge to detect as legacy approaches to cybersecurity trust the identity of the person using the privileged credentials. In effect, the hacker is camouflaged by the trust assigned to the privileged credentials they have and can roam internal systems undetected, exfiltrating sensitive data in the process.

The reality is that many breaches can be prevented by some of the most basic Privileged Access Management (PAM) tactics and solutions, coupled with a Zero Trust approach. Most organizations are investing the largest chunk of their security budget on protecting their network perimeter rather than focusing on security controls, which can affect positive change to protect against the leading attack vector: privileged access abuse.

The bottom line is that investing in securing perimeters leaves the most popular attack vector of all unprotected, which are privileged credentials. Making PAM a top priority is crucial to protect any business’ most valuable asset; it’s systems, data, and the intelligence they provide. Gartner has listed PAM on its Top 10 Security Projects for the past two years for a good reason.

Part of a cohesive PAM strategy should include machine learning-based threat analytics to provide an extra layer of security that goes beyond a password vault, multi-factor authentication (MFA), or privilege elevation.

How Machine Learning and Threat Analytics Stop Privileged Credential Abuse 

Machine learning algorithms enable threat analytics to immediately detect anomalies and non-normal behavior by tracking login behavioral patterns, geolocation, and time of login, and many more variables to calculate a risk score. Risk scores are calculated in real-time and define if access is approved, if additional authentication is needed, or if the request is blocked entirely.

Machine learning-based threat analytics also provide the following benefits:

  • New insights into privileged user access activity based on real-time data related to unusual recent privilege change, the command runs, target accessed, and privilege elevation.
  • Gain greater understanding and insights into the specific risk nature of specific events, computing a risk score in real time for every event expressed as high, medium, or low level for any anomalous activity.
  •  Isolate, identify, and track which security factors triggered an anomaly alert.
  • Capture, play, and analyze video sessions of anomalous events within the same dashboard used for tracking overall security activity.
  • Create customizable alerts that provide context-relevant visibility and session recording and can also deliver notifications of anomalies, all leading to quicker, more informed investigative action.

What to Look for In Threat Analytics 
Threat analytics providers are capitalizing on machine learning to improve the predictive accuracy and usability of their applications continually. What’s most important is for any threat analytics application or solution you’re considering to provide context-aware access decisions in real time. The best threat analytics applications on the market today are using machine learning as the foundation of their threat analytics engine. These machine learning-based engines are very effective at profiling the normal behavior pattern for any user on any login attempt, or any privileged activity including commands, identifying anomalies in real time to enable risk-based access control. High-risk events are immediately flagged, alerted, notified, and elevated to IT’s attention, speeding analysis, and greatly minimizing the effort required to assess risk across today’s hybrid IT environments.

The following is the minimum set of features to look for in any privilege threat analytics solution:

  • Immediate visibility with a flexible, holistic view of access activity across an enterprise-wide IT network and extended partner ecosystem. Look for threat analytics applications that provide dashboards and interactive widgets to better understand the context of IT risk and access patterns across your IT infrastructure. Threat analytics applications that give you the flexibility of tailoring security policies to every user’s behavior and automatically flagging risky actions or access attempts, so that you’ll gain immediate visibility into account risk, eliminating the overhead of sifting through millions of log files and massive amounts of historical data.
  • They have intuitively designed and customizable threat monitoring and investigation screens, workflows, and modules. Machine learning is enabling threat analytics applications to deliver more contextually-relevant and data-rich insights than has ever been possible in the past. Look for threat analytics vendors who offer intuitively designed and customizable threat monitoring features that provide insights into anomalous activity with a detailed timeline view. The best threat analytics vendors can identify the specific factors contributing to an anomaly for a comprehensive understanding of a potential threat, all from a single console. Security teams can then view system access, anomaly detection in high resolutions with analytics tools such as dashboards, explorer views, and investigation tools.
  • Must provide support for easy integration to Security Information and Event Management (SIEM) tools. Privileged access data is captured and stored to enable querying by log management and SIEM reporting tools. Make sure any threat analytics application you’re considering has installed, and working integrations with SIEM tools and platforms such as Micro Focus® ArcSight™, IBM® QRadar™, and Splunk® to identify risks or suspicious activity quickly.
  • Must Support Alert Notification by Integration with Webhook-Enabled Endpoints. Businesses getting the most value out of their threat analytics applications are integrating with Slack or existing onboard incident response systems such as PagerDuty to enable real-time alert delivery, eliminating the need for multiple alert touch points and improving time to respond. When an alert event occurs, the threat analytics engine allows the user to send alerts into third-party applications via Webhook. This capability enables the user to respond to a threat alert and contain the impact of a breach attempt.

Conclusion 
CentrifyForresterGartner, and Verizon each have used different methodologies and reached the same conclusion from their research: privileged access abuse is the most commonly used tactic for hackers to exfiltrate sensitive data. Breaches based on privileged credential abuse are extremely difficult to stop, as these credentials often have the greatest levels of trust and access rights associated with them. Leveraging threat analytics applications using machine learning that is adept at finding anomalies in behavioral data and thwarting a breach by denying access is proving very effective against privileged credential abuse.

Companies, including Centrify, use risk scoring combined with adaptive MFA to empower a least-privilege access approach based on Zero Trust. This Zero Trust Privilege approach verifies who or what is requesting privileged access, the context behind the request, and the risk of the access environment to enforce least privilege. These are the foundations of Zero Trust Privilege and are reflected in how threat analytics apps are being created and improved today.

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Smart Machines Are The Future Of Manufacturing

Smart Machines Are The Future Of Manufacturing

  • Industrial Internet of Things (IIoT) presents integration architecture challenges that once solved can enable use cases that deliver fast-growing revenue opportunities.
  • ISA-95 addressed the rise of global production and distributed supply chains yet are still deficient on the issue of data and security, specifically the proliferation of IIoT sensors, which are the real security perimeter of any manufacturing business.
  • Finding new ways to excel at predictive maintenance, and cross-vendor shop floor integration are the most promising applications.
  • IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations.

These and many other fascinating insights are from an article McKinsey published titled IIoT platforms: The technology stack as value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. It’s based on a thorough study also published this month, Leveraging Industrial Software Stack Advancement For Digital TransformationA copy of the study is downloadable here (PDF, 50 pp., no opt-in). The study shows how smart machines are the future of manufacturing, exploring how IIoT platforms are enabling greater machine-level autonomy and intelligence.

The following are the key takeaways from the study:

  • Capturing IIoT’s full value potential will require more sophisticated integrated approaches than current automation protocols provide. IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations and support the design-to-manufacturing process. Digital manufacturing platforms are already enabling real-time monitoring to the machine and shop floor level. The data streams real-time monitoring is delivering today is the catalyst leading to greater real-time analytics accuracy, machine learning adoption and precision and a broader integration strategy to the PLC level on legacy machinery. Please click on the graphic to expand for easier reading.

  • Inconsistent data structures at the machine, line, factory and company levels are slowing down data flows and making full transparency difficult to attain today in many manufacturers. Smart machines with their own operating systems that orchestrate IIoT data and ensure data structure accuracy are being developed and sold now, making this growth constraint less of an issue. The millions of legacy industrial manufacturing systems will continue to impede IIoT realizing its full potential, however. The following graphic reflects the complexities of making an IIoT platform consistent across a manufacturing operation. Please click on the graphic to expand for easier reading.

  • Driven by price wars and commoditized products, manufacturers have no choice but to pursue smart, connected machinery that enables IIoT technology stacks across shop floors. The era of the smart, connected machines is here, bringing with it the need to grow services and software revenue faster than transaction-based machinery sales. Machinery manufacturers are having to rethink their business models and redefine product strategies to concentrate on operating system-like functionality at the machine level that can scale and provide a greater level of autonomy, real-time data streams that power more accurate predictive maintenance, and cross-vendor shop floor integration. Please click on the graphic for easier reading.

  • Machines are being re-engineered starting with software and services as the primary design goals to support new business models. Machinery manufacturers are redefining existing product lines to be more software- and services-centric. A few are attempting to launch subscription-based business models that enable them to sell advanced analytics of machinery performance to customers. The resulting IIoT revenue growth will be driven by platforms as well as software and application development and is expected to be in the range of 20 to 35%. Please click on the graphic to expand for easier reading.

Industry 4.0’s Potential Needs To Be Proven On The Shop Floor

  • 99% of mid-market manufacturing executives are familiar with Industry 4.0, yet only 5% are currently implementing or have implemented an Industry 4.0 strategy.
  • Investing in upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES) are manufacturers’ top three priorities based on interviews with them.
  • Mid-market manufacturers getting the most value out of Industry 4.0 excel at orchestrating a variety of technologies to find new ways to excel at product quality, improve shop floor productivity, meet delivery dates, and control costs.
  • Real-time monitoring is gaining momentum to improve order cycle times, troubleshoot quality problems, improve schedule accuracy, and support track-and-trace.

These and many other fascinating insights are from Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver ValueBDO is a leading provider of assurance, tax, and financial advisory services and is providing the report available for download here (PDF, 36 pp., no opt-in). The survey was conducted by Market Measurement, Inc., an independent market research consulting firm. The survey included 230 executives at U.S. manufacturing companies with annual revenues between $200M and $3B and was conducted in November and December of 2018. Please see page 2 of the study for additional details regarding the methodology. One of the most valuable findings of the study is that mid-market manufacturers need more evidence of Industry 4.0, delivering improved supply chain performance, quality, and shop floor productivity.

Insights from the Shop Floor: Machine Upgrades, Smart Machines, Real-Time Monitoring & MES Lead Investment Plans

In the many conversations I’ve had with mid-tier manufacturers located in North America this year, I’ve learned the following:

  • Their top investment priorities are upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES).
  • Manufacturers growing 10% or more this year over 2018 excel at integrating technologies that improve scheduling to enable more short-notice production runs, reduce order cycle times, and improve supplier quality.

Key Takeaways from BDO’s Industry 4.0 Study

  • Manufacturers are most motivated to evaluate Industry 4.0 technologies based on the potential for growth and business model diversification they offer. Building a business case for any new system or technology that delivers revenue, even during a pilot, is getting the highest priority by manufacturers today. Based on my interviews with manufacturers, I found they were 1.7 times more likely to invest in machine upgrades and smart machines versus spending more on marketing. Manufacturers are very interested in any new technology that enables them to accept short-notice production runs from customers, excel at higher quality standards, improve time-to-market, all the while having better cost visibility and control. All those factors are inherent in the top three goals of business model diversification, improved operational efficiencies, and increased market penetration.

  • For Industry 4.0 technologies to gain more adoption, more use cases are needed to explain how traditional product sales, aftermarket sales, and product-as-a-service benefit from these new technologies. Manufacturers know the ROI of investing in a machinery upgrade, buying a smart, connected machine, or integrating real-time monitoring across their shop floors. What they’re struggling with is how Industry 4.0 makes traditional product sales improve. 84% of upper mid-market manufacturers are generating revenue using Information-as-a-Service today compared to 67% of middle market manufacturers overall.

  • Manufacturers who get the most value out of their Industry 4.0 investments begin with a customer-centric blueprint first, integrating diverse technologies to deliver excellent customer experiences. Manufacturers growing 10% a year or more are relying on roadmaps to guide their technology buying decisions. These roadmaps are focused on how to reduce scrap, improve order cycle times, streamline supplier integration while improving inbound quality levels, and provide real-time order updates to customers. BDOs’ survey results reflect what I’m hearing from manufacturers. They’re more focused than ever before on having an integrated engagement strategy combined with greater flexibility in responding to unique and often urgent production runs.

  • Industry 4.0’s potential to improve supply chains needs greater focus if mid-tier manufacturers are going to adopt the framework fully. Manufacturing executives most often equate Industry 4.0 with shop floor productivity improvements while the greatest gains are waiting in their supply chains. The BDO study found that manufacturers are divided on the metrics they rely on to evaluate their supply chains. Upper middle market manufacturers are aiming to speed up customer order cycle times and are less focused on getting their total delivered costs down. Lower mid-market manufacturers say reducing inventory turnover is their biggest priority. Overall, strengthening customer service increases in importance with the size of the organization.

  • By enabling integration between engineering, supply chain management, Manufacturing Execution Systems (MES) and CRM systems, more manufacturers are achieving product configuration strategies at scale. A key growth strategy for many manufacturers is to scale beyond the limitations of their longstanding Make-to-Stock production strategies. By integrating engineering, supply chains, MES, and CRM, manufacturers can offer more flexibility to their customers while expanding their product strategies to include Configure-to-Order, Make-to-Order, and for highly customized products, Engineer-to-Order. The more Industry 4.0 can be shown to enable design-to-manufacturing at scale, the more it will resonate with senior executives in mid-tier manufacturing.

  • Manufacturers are more likely than ever before to accept cloud-based platforms and systems that help them achieve their business strategies faster and more completely, with analytics being in the early stages of adoption. Manufacturing CEOs and their teams are most concerned about how quickly new applications and platforms can position their businesses for more growth. Whether a given application or platform is cloud-based often becomes secondary to the speed and time-to-market constraints every manufacturing business faces. The fastest-growing mid-tier manufacturers are putting greater effort and intensity into mastering analytics across every area of their business too. BDO found that Artificial Intelligence (AI) leads all other technologies in planned use.

How To Improve Supply Chains With Machine Learning: 10 Proven Ways

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  • Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

  • The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning. Applying machine learning algorithms and techniques to improve supply chains starts with data sets that have the greatest variety and variability in them. The most challenging issues supply chains face are often found in optimizing logistics, so materials needed to complete a production run arrive on time. Source: KPMG, Supply Chain Big Data Series Part 1

  • Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

  • Reducing forecast errors up to 50% is achievable using machine learning-based techniques. Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimization techniques. Inventory reductions of 20 to 50% are also being achieved today when machine learning-based supply chain management systems are used. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

  • DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models. DHL’s research team continually tracks and evaluates the impact of emerging technologies on logistics and supply chain performance. They’re also predicting that AI will enable back-office automation, predictive operations, intelligent logistics assets, and new customer experience models. Source: DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

  • Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. The greatest growth barrier is the lack of skilled labor available. Using machine learning and advanced analytics manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour, 2018

  • Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today. 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. Inspectorio is a machine learning startup to watch in this area. They’re tackling the many problems that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers. The graphic below explains their platform. Source: Forbes, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility, January 23, 2019

  • Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before. Combining multi-enterprise commerce networks for global trade and supply chain management with AI and machine learning platforms are revolutionizing supply chain end-to-end visibility. One of the early leaders in this area is Infor’s Control Center. Control Center combines data from the Infor GT Nexus Commerce Network, acquired by the company in September 2015, with Infor’s Coleman Artificial Intelligence (AI) Infor chose to name their AI platform after the inspiring physicist and mathematician Katherine Coleman Johnson, whose trail-blazing work helped NASA land on the moon. Be sure to pick up a copy of the book and see the movie Hidden Figures if you haven’t already to appreciate her and many other brilliant women mathematicians’ many contributions to space exploration. ChainLink Research provides an overview of Control Center in their article, How Infor is Helping to Realize Human Potential, and two screens from Control Center are shown below.

  • Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains. By taking a least privilege access approach, organizations can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse in their supply chains by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.  Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.  Centrify is a leader in this area, with globally-recognized suppliers including Cisco, Intel, Microsoft, and Salesforce being current customers.  Source: Forbes, High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019, November 28, 2018.
  • Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs. McKinsey found that predictive maintenance enhanced by machine learning allows for better prediction and avoidance of machine failure by combining data from the advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible and overall maintenance costs may be reduced by up to 10%. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

References

Accenture, Reinventing The Supply Chain With AI, 20 pp., PDF, no opt-in.

Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics. Journal Of Business Logistics37(1), 6-17.

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

CIO’s Guide To Stopping Privileged Access Abuse – Part 2

Why CIOs Are Prioritizing Privileged Credential Abuse Now

Enterprise security approaches based on Zero Trust continue to gain more mindshare as organizations examine their strategic priorities. CIOs and senior management teams are most focused on securing infrastructure, DevOps, cloud, containers, and Big Data projects to stop the leading cause of breaches, which is privileged access abuse.

Based on insights gained from advisory sessions with CIOs and senior management teams, Forrester estimates that 80% of data breaches have a connection to compromised privileged credentials, such as passwords, tokens, keys, and certificates. In another survey completed by Centrify, 74% of IT decision makers surveyed whose organizations have been breached in the past, say it involved privileged access abuse. Furthermore, 65% of organizations are still sharing root or privileged access to systems and data at least somewhat often. Centrify’s survey, Privileged Access Management in the Modern Threatscape, is downloadable here.

The following are the key reasons why CIOs are prioritizing privileged access management now:

  • Identities are the new security perimeter for any business, making privileged access abuse the greatest challenge CIOs face in keeping their businesses secure and growing. Gartner also sees privileged credential abuse as the greatest threat to organizations today, and has made Privileged Account Management one of the Gartner Top 10 Security Projects for 2018, and again in 2019Forrester and Gartner’s findings and predictions reflect the growing complexity of threatscapes every CIO must protect their business against while still enabling new business growth. Banking, financial services, and insurance (BFSI) CIOs often remark in my conversations with them that the attack surfaces in their organizations are proliferating at a pace that quickly scales beyond any trust but verify legacy approach to managing access. They need to provide applications, IoT-enabled devices, machines, cloud services, and human access to a broader base of business units than ever before.
  • CIOs are grappling with the paradox of protecting the rapidly expanding variety of attack surfaces from breaches while still providing immediate access to applications, systems, and services that support their business’ growth. CIOs I’ve met with also told me access to secured resources needs to happen in milliseconds, especially to support the development of new banking, financial services, and insurance applications in beta testing today, scheduled to be launched this summer. Their organizations’ development teams expect more intuitive, secure, and easily accessible applications than ever before, which is driving CIOs to prioritize privileged access management now
  • Adapting and risk-scoring every access attempt in real-time is key to customer experiences on new services and applications, starting with response times. CIOs need a security strategy that can flex or adapt to risk contexts in real-time, assessing every access attempt across every threat surface and generating a risk score in milliseconds. The CIOs I’ve met with regularly see a “never trust, always verify, enforce least privilege” approach to security as the future of how they’ll protect every threat surface from privileged access abuse. Each of their development teams is on tight deadlines to get new services launch to drive revenue in Q3. Designing in Zero Trust with a strong focus on Zero Trust Privilege is saving valuable development time now and is enabling faster authentication times of the apps and services in testing today.

Strategies For Stopping Privileged Credential Abuse – Part 2  

Recently I wrote a CIO’s Guide To Stopping Privileged Access Abuse – Part 1 detailing five recommended strategies for CIOs on how to stop privileged credential abuse. The first five strategies focus on the following: discovering and inventorying all privileged accounts; vaulting all cloud platforms’ Root Accounts; auditing privileged sessions and analyzing patterns to find privileged credential sharing not found during audits; enforcing least privilege access now within your existing infrastructure as much as possible; and adopting multi-factor authentication (MFA) across all threat surfaces that can adapt and flex to the risk context of every request for resources.

The following are the second set of strategies CIOs need to prioritize to further protect their organizations from privileged access abuse:

  1. After completing an inventory of privileged accounts, create a taxonomy of them by assigning users to each class or category, personalizing privileged credential access to the role and entitlement level for each. CIOs tell me this is a major time saver in scaling their Privileged Access Management (PAM) strategies. Assigning every human, machine and sensor-based identity is the goal with the overarching objective being the creation of a Zero Trust-based enterprise security strategy. Recommended initial classes or categories include IT administrators who are also responsible for endpoint security; developers who require occasional access to production instances; service desk teams and service operations; the Project Management Office (PMO) and project IT; and external contractors and consultants.
  2. By each category in the taxonomy, automate the time, duration, scope, resources, and entitlements of privileged access for each focusing on the estimated time to complete each typical task. Defining a governance structure that provides real-time access to resources based on successful authentication is a must-have for protecting privileged access credentials. By starting with the attributes of time, duration, scope and properties, organizations have a head start on creating a separation of duties (SOD) model. Separation of duties is essential for ensuring that privileged user accounts don’t have the opportunity to carry out and conceal any illegal or unauthorized activities.
  3. Using the taxonomy of user accounts created and hardened using the separation of duties model, automate privileged access and approval workflows for enterprise systems. Instead of having administrators approve or semi-automate the evaluation of every human- and machine-based request for access, consider automating the process with a request and approval workflow. With time, duration, scope, and properties of privileged access already defined human- and machine-based requests for access to IT systems and services are streamlined, saving hundreds of hours a year and providing a real-time log for audit and data analysis later.
  4. Break-glass, emergency or firecall account passwords need to be vaulted, with no exceptions. When there’s a crisis of any kind, the seconds it takes to get a password could mean the difference between cloud instances and entire systems being inaccessible or not. That’s why administrators often only manually secure root passwords to all systems, cloud platforms and containers included. This is the equivalent of leaving the front door open to the data center with all systems unlocked. The recent Centrify survey found that just 48% of organizations interviewed have a password vault. 52% are leaving the keys to the kingdom available for hackers to walk through the front door of data centers and exfiltraticate data whenever they want.
  5. Continuous delivery and deployment platforms including Ansible, Chef, Puppet, and others need to be configured when first installed to eliminate the potential for privileged access abuse. The CIOs whose teams are creating new apps and services are using Chef and Puppet to design and create workloads, with real-time integration needed with customer, pricing, and services databases and the systems they run on. Given how highly regulated insurance is, CIOs are saying they need to have logs that show activity down to the API level in case of an audit. The more regulated and audited a company, the more trusted and untrusted domains are seen as the past, Zero Trust as the future based on CIO’s feedback.

Conclusion

The CIOs I regularly meet with from the banking, financial services, and insurance industries are under pressure to get new applications and services launched while protecting their business’ daily operations. With more application and services development happening in their IT teams, they’re focusing on how they can optimize the balance between security and speed. New apps, services, and the new customers they attract are creating a proliferation of new threat surfaces, making every new identity the new security perimeter.

Public Cloud Soaring To $331B By 2022 According To Gartner

Gartner is predicting the worldwide public cloud services market will grow from $182.4B in 2018 to $214.3B in 2019, a 17.5% jump in just a year. Photo credit: Getty

  • Gartner predicts the worldwide public cloud service market will grow from $182.4B in 2018 to $331.2B in 2022, attaining a compound annual growth rate (CAGR) of 12.6%.
  • Spending on Infrastructure-as-a-Service (IaaS) is predicted to increase from $30.5B in 2018 to $38.9B in 2019, growing 27.5% in a year.
  • Platform-as-a-Service (PaaS) spending is predicted to grow from $15.6B in 2018 to $19B in 2019, growing 21.8% in a year.
  • Business Intelligence, Supply Chain Management, Project and Portfolio Management and Enterprise Resource Planning (ERP) will see the fastest growth in end-user spending on SaaS applications through 2022.

Gartner’s annual forecast of worldwide public cloud service revenue was published last week, and it includes many interesting insights into how the research firm sees the current and future landscape of public cloud computing. Gartner is predicting the worldwide public cloud services market will grow from $182.4B in 2018 to $214.3B in 2019, a 17.5% jump in just a year. By the end of 2019, more than 30% of technology providers’ new software investments will shift from cloud-first to cloud-only, further reducing license-based software spending and increasing subscription-based cloud revenue.

The following graphic compares worldwide public cloud service revenue by segment from 2018 to 2022. Please click on the graphic to expand for easier reading.

Comparing Compound Annual Growth Rates (CAGRs) of worldwide public cloud service revenue segments from 2018 to 2022 reflects IaaS’ anticipated rapid growth. Please click on the graphic to expand for easier reading.

Gartner provided the following data table this week as part of their announcement:

  • Business Intelligence, Supply Chain Management, Project and Portfolio Management and Enterprise Resource Planning (ERP) will see the fastest growth in end-user spending on SaaS applications through 2022.  Gartner is predicting end-user spending on Business Intelligence SaaS applications will grow by 23.3% between 2017 and 2022.  Spending on SaaS-based Supply Chain Management applications will grow by 21.2% between 2017 and 2022. Project and Portfolio Management SaaS-based applications will grow by 20.9% between 2017 and 2022. End-user spending on SaaS ERP systems will grow by 19.2% between 2017 and 2022.

Sources: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.5 Percent in 2019 and Forecast: Public Cloud Services, Worldwide, 2016-2022, 4Q18 Update (Gartner client access)

Vodafone’s 2019 IoT Barometer Reflects Robust Growth In The Enterprise

  • 85% of enterprises who develop deep expertise with IoT succeed at driving revenue faster than competitors.
  • 81% of enterprises say Artificial Intelligence streamlines interpreting and taking action on data insights gained from IoT systems and sensors.
  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today.
  • Transport & Logistics and Manufacturing & Industrials saw the most significant increase in adoption between 2018 and 2019.

These and many other fascinating insights are from the 6th annual Vodafone IoT Barometer, 2019.  The entire report can be downloaded here (PDF, 32 pp., e-mail opt-in). The methodology is based on 1,758 interviews distributed across the Americas (22%), EMEA (49%) and Asia-Pacific (29%). Eight vertical markets were included with manufacturing (22%), healthcare and wellness (14%) and retail, leisure, and hospitality (14%) being the three most represented markets.  Vodaphone is making an interactive tool available here for exploring the results.

Key insights from Vodafone’s 2019 IoT Barometer include the following:

  • 34% of global businesses are now using IoT in daily operations, up from 29% in 2018, with 95% of IoT adopters are already seeing measurable benefits. 81% of IoT adopters say their reliance on IoT has grown, and 76% of adopters say IoT is mission-critical to them. 58% are using analytics platforms to get more insights from their IoT data to improve decision making. 71% of enterprises who have adopted IoT expect their company and others like them will start listing data resources on their balance sheets as assets within five years.

  • 95% of enterprises adopting IoT are achieving tangible benefits and positive ROI. 52% of enterprises report significant returns on their IoT investments. 79% say IoT is enabling positive outcomes that would have been impossible without it, further reflecting robust growth in the enterprise. Across all eight vertical markets reducing operating costs (53%) and gaining more accurate data and insights (48%) are the most common benefits. Transitioning an IoT pilot to production based on cost reduction and improved visibility creates a compelling ROI for many enterprises. The following graphic compares IoT’s benefits to enterprises. Please click on the graphic to expand for easier reading.

  • Transport & Logistics and Manufacturing & Industrials saw the greatest increase in adoption between 2018 and 2019. Transport and Logistics had the highest IoT adoption rate at 42% followed by Manufacturing and Industrials at 39%. Manufacturers are facing the challenges of improving production efficiency and product quality while accelerating time-to-market for next-generation smart, connected products. IoT contributes to productivity improvements and creates opportunities for services-based business models, two high priorities for manufacturers in 2019 and beyond.  The following graphic from the interactive tool compares IoT adoption by industry based on Vodaphone’s IoT barometer data over the last six years:

  • 89% of most sophisticated enterprises have multiple full-scale projects in production, orchestrating IoT with analytics, AI and cloud, creating a technology stack that delivers real-time insights. Enterprises who lead IoT adoption in their industries rely on integration to gain scale and speed advantages quickly over competitors. The greater the real-time integration, the greater the potential to digitally transform an enterprise and remove roadblocks that get in the way of growing. 95% of adopters where IoT is fully integrated say it’s enabling their digital transformation, compared with 55% that haven’t started integration. The following graphics reflect how integrated enterprises’ IoT projects are with existing business systems and processes and the extent to which enterprises agree that IoT is enabling digital transformation.

  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today. 57% of all enterprises are using IoT to manage risk and compliance. 53% are using it to increase revenue and cut costs, with 82% of high performing enterprises rely on IoT to manage risk and compliance. The following graphic compares the types of variables enterprises are using IoT to track today and plan to in the future.

  • IoT adoption is soaring in Americas-based enterprises, jumping from 27% in 2018 to 40% in 2019. The Americas region leads the world in terms of IoT usage assessed by strategy, integration, and implementation of IoT deployments. 73% of Americas-based enterprises are the most likely to report significant returns from their IoT investments compared to 47% for Asia-Pacific (APAC) and 45% for Europe, Middle East and Africa (EMEA).
  • 52% of IoT-enabled enterprises plan to use 5G when it becomes available. Enterprises are looking forward to 5G’s many advantages including improved security via stronger encryption, more credentialing options, greater quality of service management, more specialized services and near-zero latency. Vodafone predicts 5G will be a strong catalyst of growth for emerging IoT applications including connected cars, smart cities, eHealth and industrial automation.

 

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning to fine-tune their selling and service strategies. Source: iStock

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:

The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.

  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).

  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.

  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying. Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well

What IoT Leaders Do To Drive Greater Results

  • IoT Leaders are achieving cost and revenue gains of at least 15% or more, while laggards see less than 5%.
  • Pursuing 80% more IoT use cases compared to their peers, IoT Leaders are progressing faster down the learning curve of monetizing their application areas.
  • IoT Leaders anticipate that their IoT use cases will boost their gross profits by 13% over the next three years, three times as much as IoT laggards.

What IoT leaders do to excel and drive greater results compared to their peers is explored in the recent McKinsey report, What separates leaders from laggards in the Internet of Things. The study is based on interviews with 300 IoT executive-level practitioners from companies with more than $500M revenues which are implementing large-scale IoT strategies with projects that have progressed from pilot to production. Enterprises from 11 major industry segments from Canada, China, Germany, and the United States were included in the survey.

McKinsey found 16% of enterprises have IoT programs in production, delivering aggregate cost and revenue impacts of at least 15%. The study also found 16% of enterprises are lagging, attaining aggregate revenue and cost improvements of less than 5%. The following graphic compares companies by the level of financial impact from IoT initiatives:

Nine practices differentiate IoT Leaders from laggards, and the study provides a fascinating look into each based on the survey data. Key insights into IoT Leader’s practice areas is provided here:

  • Leaders are more aggressive about pursuing a greater number, scope, and variety of IoT applications and use cases than their less successful peers. What IoT Leaders learn quickly is how steep the IoT learning curve is, and how it’s essential to run as many IoT pilots as possible to learn more. Leaders discover the first 15 or so IoT use cases typically have a modest payback, with the average payback rising until approximately 30 use cases have been achieved. IoT Leaders anticipate that their IoT use cases will boost their gross profits by 13% over the next three years, three times as much as IoT laggards. The following graphic illustrates the financial impact per IoT use case by the cumulative number of IoT use cases enterprises initiate.

  • Leaders are more willing than their peers to change business processes to unlock IoT’s value. McKinsey found IoT Leaders are three times more likely than their peers to say that managing changes to business processes is one of the three most important capabilities for implementing IoT. CEOs who champion their company’s IoT initiatives make strong contributions in this area, removing barriers and roadblocks quickly to keep IoT programs moving forward.
  • Leaders design, pilot and move to production IoT use cases that rely on advanced endpoints far more than their peers. McKinsey finds that IoT Leaders are more visionary and aggressive than peers in developing applications with advanced endpoints.  Leaders are gaining expertise and mastery of how to creatively use advanced endpoints today, reporting higher levels of satisfaction and positive results.

  • Leaders clearly define how IoT will create value and excel in building effective business cases. McKinsey found that IoT Leaders are 75% more likely than their peers to cite the preparation of a strong business case as a critical success factor for their IoT programs. The study’s respondents who have an IoT vision that includes a strong value proposition, a proven delivery model, and a business model that drives revenue are getting results faster than their peers. 35% of Leaders rate the importance of “strong business case and vision for value creation” as one of the top three success factors versus 20% of laggards. Leaders leave nothing to chance when it comes to defining how IoT will deliver business value either in the form of greater revenue or reduced costs.

  • A CEO’s involvement and support are essential for any enterprise to succeed with  IoT. Based on personal experience with IoT pilots, C-level executives are indispensable in removing barriers and making process-level changes necessary for success. 72% of the surveyed executives agree. A vital catalyst of any enterprise succeeding with IoT is a clear, unequivocal time commitment on the part of the CEO. Enterprises in the Leaders quintile were 2.4 more likely than laggards to report that their CEO serves as the champion of IoT efforts as the following graphic illustrates:

  • Leaders credit strong alignment with IoT strategies and priorities enterprise-wide as a critical factor in their success. IoT initiatives and pilots on their way to production require executives, managers, and frontline workers to learn fresh skills and collaborate across business and functional boundaries in new ways. Enterprises need to have a strong unifying vision of where they’re going with IoT, with the CEO championing the change management required to make sure they succeed.
  • Leaders begin by adding IoT capability to existing products and services first. McKinsey found that Leaders are three times more likely than their peers to make their top priority adding IoT capabilities to existing products. They focus on how to turn the current scale they’ve achieved with suppliers, selling and service networks into a formidable competitive advantage. They’re also more adept at cross-selling and up-selling IoT-enabled products by capitalizing on current customer relationships. The following graphic compares enterprises’ single highest-priority IoT effort:

  • Leaders excel at tapping into, scaling and relying on an ecosystem of partners for innovation versus doing it all themselves. McKinsey finds that IoT Leaders excel at scaling their partner ecosystems faster and more strategically than their peers. IoT Leaders also rely more on partners for the latest technology innovations instead of attempting to create them entirely on their own. They’re also deliberately choosing IoT platforms that support third-party developers and the advanced endpoints as the graphic below shows:

  • Leaders prepare for cyber attacks, so they don’t slow things down. McKinsey found that 30% of enterprises from both IoT Leaders and their peers say that they’ve experienced cyber attacks that have resulted in high to severe damage. 57% of Leaders had been the target of cyber attacks compared to 44% of their peers. The higher number of cyber attacks happening for Leaders is due to the broader threat surface their many pilots, and production-level use cases create. The more distributed and varied IoT use cases are the greater the risk of privileged credential abuse as well. 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.

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.

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