Build trust by taking transparency seriously Unintentionally or purposely misleading customers contributes to their distrust. Unfortunately, some businesses seem to forget about this principle. One company, for instance, launched an experiment to study the effects of manipulating customer’s posts; another company modified customer profiles and ran analytics to determine which profile improved their matching services. Not surprisingly, both attempts backfired with customers, causing ethical debates and negative press. Businesses should be more transparent about what data they collect about customers and how they use it. Some companies are obscure with their practices, including not informing customers how they share data with third parties or how they collect it from data brokers to sell. Other businesses may think that the value of the data derives from keeping it inaccessible to customers, just like credit scoring data. However, this lack of transparency is raising many consumer concerns, and the Federal Trade Commission (FTC) published a report in May 2014 calling on data brokers for more transparency and accountability.17 Follow basic data protection guidelines According to the TRUSTe consumer confidence index, 89 percent of Internet users in the US would avoid doing business with companies that do not protect their privacy.18 Data breaches further complicate the matter—not only in terms of litigation costs, but also reputation damage and customer flight. In order to limit their liability, businesses need to enforce encryption and responsible data management practices that protect customers’ personal data. According to a Ponemon study conducted in October 2014, four out of five IT practitioners acknowledged that their organizations do not use a strict least-privilege data model, where each user or program is allowed the minimal access privilege just to the information and resources that are necessary for a legitimate task.19 Take advantage of privacy-preserving analytics Managing and protecting the increasingly large sets of personal data while running useful analytics on them is not a trivial task. However, businesses do not have to lock up all the data in order to avoid a privacy risk. For instance, TrustLayers offers a platform that seeks to provide privacy intelligence for big data and help businesses to efficiently use personal data while monitoring whether their teams are following privacy policies.

The privacy risk is exacerbated by advances made in data mining technologies. Therefore, companies should consider privacy-preserving data mining techniques, which seek to balance the utility of data acquisition with privacy protection. The risk of leaking sensitive data is limited by modifying the data in such a way so as to perform analytics effectively, while safeguarding sensitive information from unauthorized disclosure and releasing only aggregate data. Various techniques exist for all the steps of the analytics process—from data collection, to data mining, to sharing and delivery of the insights extracted from data. Businesses should explore techniques such as differential privacy and distributed data mining in order to identify the most suitable technique for the application that they need.21, 22 Innovate to appeal to privacy-wary customers To build customers’ trust, businesses are beginning to apply enhanced services that protect their privacy and digital footprint beyond VPN access to their services. Facebook, for example, launched a Tor hidden service.23 The users of the social media service can stay anonymous as their connections go through three extra encrypted hops to random computers around the world, making it difficult for eavesdroppers to observe their traffic or trace it back to their origin. Similarly, Apple decided to relinquish access to customer data on iCloud. Encryption keys created on the customer’s device are used to encrypt the data on iCloud. Apple has no access to these iCloud keychain encryption keys, and therefore is not able to decrypt user data stored on iCloud.24 Businesses should also consider innovative approaches to convince customers to share more data, including providing rewards in return for data sharing, or even offering anonymous services to appeal to more privacy conscious consumers. Global identity validation services such as BeehiveID or ID.me could be used instead of social media logins to allow customers to have more control over their data while ensuring businesses protect their identity. Anonymous credentials represent a powerful solution for preventing even colluding credential issuers and verifiers from identifying and tracking users. These technologies can alleviate trust concerns regarding centralized credential providers that can make a statement about identity on the Internet, as these providers get more visibility into users’ entire online activities. Businesses can also explore emerging techniques to offer anonymous credentials as a basis for constructing untraceable electronic payment systems, or “e-cash.” One example of these techniques is a new protocol named “Zerocash,” which adds cryptographically unlinkable electronic payments to the Bitcoin currency.25 Empower customers with tools Consumers in the UK and US now have access to Internet company ratings based on their data stewardship practices published by Fair Data and the Electronic Frontier Foundation.26 With more information about businesses’ privacy and data protection practices, customers can make better informed choices. They also have more tools at their disposal to help them hide their data or decide which businesses to share it with.

For instance, Ghostery is a privacy tool that helps customers control which businesses can track their web browsing behavior. Meeco is a life management platform that enables people to collect their personal data while being more anonymous. By acknowledging and honoring customers’ desire for greater control over their own privacy and how they trade their data in the emerging Internet of Me, businesses can increase their trust factor with customers. This is just the beginning of a much longer privacy journey the technology community is embarking upon; until these protections become federated and transparent to end users, they are unlikely to be widely used. As such, this space will see many shifts and innovations over the coming years. Conclusion Accenture recommends that businesses be vigilant with their security and privacy practices so that they neither compromise their customers’ experiences nor lose their trust. Following truly proactive and ethical data stewardship practices, and offering enhanced services that are consistent with customers’ expectations of privacy and personalized seamless experiences, will strengthen trust and participation in the digital economy

With the evolution of the IoT and digital industry ecosystems, platform-based businesses will capture more of the digital economy’s opportunities for growth and profitability. Machine-to-machine communications and advanced analytics will leverage digital platforms. Intelligent Enterprises will benefit from the influx of shared, crossindustry data. And advances in processing power, data science and cognitive technology will help businesses prepare for the growing wave of complex cyber-attacks. To take full advantage of these platform capabilities, businesses must increase their focus not only on security, but also on leveraging the platform to augment existing security intelligence. It is critical to understand the potential for misuse of data and functionality on platforms, and to realize they give an adversary more motivation for mayhem. Having greater insight into how edge and core IT devices are behaving can also help businesses protect against increasingly complex and subtle threats.

Understand physical security risks Leveraging a digital platform to make decisions and influence the function of a business’ products and services introduces a high-value target for an adversary. Since these platforms provide insights into the functionality of numerous digital devices and equipment across the business, as well as some degree of command and control over them, the possibility of cyber-physical attacks increases. The consequences of these attacks can range from inconvenient to life threatening. Take connected car services as an example. Recently BMW’s ConnectedDrive system experienced a vulnerability that enabled 2.2 million cars to be unlocked remotely—an open-door invitation to car thieves.13 As the functionality of connected car services improves to include things like engine optimization based on individual driving habits, the risk for abuse of these capabilities increases with the potential for severe physical outcomes. To mitigate these types of intensified physical security risks, businesses should regularly evaluate all of their business platforms for vulnerabilities and monitor them for irregular behavior, apply threat modeling to understand what is possible to accomplish within the platforms, and leverage threat intelligence to understand when adversaries are motivated to accomplish those possibilities. In addition, as new cross-industry digital platforms emerge, businesses can analyze behaviors across these platforms to further mitigate risk or reduce time to detect new threats. Evolve data security intelligently Since businesses are beginning to aggregate data from industrial, operations, management, information technology and security systems into one ecosystem, they must apply new security capabilities to protect company assets. This is especially important in the IoT era. As described earlier, businesses must proactively work to identify security threats within the data being collected from devices. One solution comes from GE’s Predix platform, which collates data from intelligent industrial systems and identifies issues that may necessitate maintenance. Businesses can further leverage the platform’s analytics to identify unusual changes in customer behavior and detect performance changes that may be technology threats. Plan security into the platform Securing digital platforms begins before development work even starts. Businesses can reduce risk by collaborating with potential ecosystem partners to brainstorm possible security challenges across and beyond their industry. Businesses should also identify what types of security-related data the platform can gather, as well as ways the platform can be leveraged to monitor edge and core devices for abnormal activity

Similarly, it is important to look at all available enterprise data, not just what is stored in security products. Determining the value of these data sets could provide insight into where more complex threat activity might originate. For example, business process activity, which normally is monitored outside the scope of security, may be leveraged within data processes to identify behaviors that adversaries could exploit in an attack. Businesses should employ techniques for more subtle evaluation of internal activity, centralize the data into a common platform, and utilize data visualization to understand specific behaviors and quickly pinpoint outliers. Finally, businesses looking to utilize technology and data platforms to operate in the digital business era must emphasize the importance of customer trust. Platform breaches will erode customers’ trust in the safety and reliability of a company’s products and services; data breaches resulting in compromised customer privacy have an equally negative impact. Businesses should proactively embed security and privacy controls into their platforms as a core function, and not rely on best practices or compliance regulation to set the bar. Utilize existing platforms to augment security intelligence The US government has recognized the value of cross-industry collaboration for cyber security in its recent formation of the Cyber Threat Intelligence Integration Center (CTIIC). According to Lisa Monaco, Assistant to the President for Homeland Security and Counterterrorism, prior to the CTIIC there was no single government entity responsible for assessing and sharing cybersecurity threat information, nor for supporting policy makers with timely information. Monaco said, “To truly safeguard Americans online and enhance the security of what has become a vast cyber ecosystem, we are going to have to work in lock-step with the private sector. The private sector cannot and should not rely on the government to solve all of its cyber-security problems. At the same time, I want to emphasize that the government won’t leave the private sector to fend for itself.”14 Similar initiatives are forming in the UK and other geographies that will have enterprises defining the models that work for them.

As digital platforms continue to capture new data and offer innovative ways to catalyze growth, they can also be used to increase security effectiveness. The digital platform can contain a wealth of information—from normal machine-to-machine behavior to standard operating conditions of edge devices. Ideally, businesses should select platforms that provide cyber-threat assessment indicators and share timely information to prevent systemic attacks.

Security DevOps As businesses develop applications on top of these platforms, they are rapidly shifting towards an agile development model termed DevOps.15 Within DevOps, where application development embraces the agility of automation and short sprints to implement new features and fix defects rapidly, there is a disruption to the normal approaches that security uses to identify and mitigate risk within applications. Traditional approaches typically involve a great deal of planning and design, activities that are humanintensive in execution and require final sign-off prior to release of an application. Activities such as code scanning will need to change to be more iterative and automated, leveraging technologies such as Cenzic and Qualys to assess vulnerabilities and risks as the application is developed. DevOps greatly speeds how quickly a digital business can develop and deploy applications, as well as incorporate new features into the services they offer. Security should be baked in from the start and embedded into how the DevOps process functions. To accomplish this, security needs to be low impact to the process, automated to a high degree and intelligent enough to guide developers in understanding risk as they make changes to the application. Conclusion Platform security is a vital capability to operating in the digital ecosystem. In order to thrive, businesses must understand the potential cyber-physical risks of delivering platform-based services and augment existing security efforts with digital platform intelligence. Accenture recommends combining operational and security information across the enterprise—and across platforms—to help businesses respond effectively to the rapidly changing cyber landscape.

Businesses are experiencing exponential growth in data as more devices get deployed at the edge and business processes become increasingly digital— causing their data repositories to reach capacity. For Intelligent Enterprises to fully reap the benefits of software intelligence and embrace a collaborative workforce model of humans and machines (or what Accenture deems Workforce Reimagined), it will be critical to securely process and protect big data. For instance, evaluating and optimizing the performance of human and machine interactions as they work side by side, and “teaching” machines to evolve as the task changes, will all be based on big data analytics. While big data presents a multitude of business opportunities to generate insights and guide actions, it also presents substantive privacy concerns. As part of a strategy to strengthen cyber laws, the US President recently announced a privacy plan for big data, which includes policy recommendations and pending draft legislation to protect consumers’ privacy.10 But despite new compliance requirements, big data breaches are on the rise. Businesses are finding it more difficult to secure big data, especially as traditional database management systems cannot scale enough to handle the data volume, acquisition velocity or data variety–what is often referred to as the three Vs.

The volume challenge Few businesses have mastered the concepts and techniques of effective data protection. To deal with the volume, computations on big data are processed in parallel often using MapReducelike frameworks, where distributed mappers independently process local data during the Map operation, before reducers process each group of output data in parallel. Google originally created Hadoop—the open source implementation of the MapReduce programming model—to store and process public website links; security and privacy were an afterthought. Since security is not inherent, it is difficult to retrofit mappers that perform data analytics with security. In order to secure the computations in these distributed frameworks, businesses must also ensure that the data is secured against potentially compromised mappers. The variety challenge Big data is composed of a variety of data elements, which makes it subject to different regulatory and compliance requirements. For example, an insurance company that collects medical records and financial information about its customers may have to build different data stores for each type of data.11 Since different stakeholders require access to various subsets of data, businesses must use encryption solutions that enable fine-grained access and operations on the data. Today, many organizations still deal with the big data challenge by creating a data lake, a huge repository of raw data in its native format. Such organizations probably need to revisit their data storage practices, segregating that data based on sensitivity level and compliance requirements, and then applying proper security controls. The velocity challenge Businesses do not always know in advance the sensitivity levels of big data because it is being collected in real-time (streaming data) or near real-time. Some data items may not look sensitive on their own, but could reveal private details when combined with other pieces of information; in the aggregate, the data might result in a comprehensive picture that requires protection. To manage the data velocity, businesses should perform data sensitivity analysis more frequently, and apply the right security policy and access controls while the data is fresh. Secure big data processing platforms As organizations build big data repositories and apply big data analytics, various types of data are mixed together, such as business performance and sensor information. When that data combines, it becomes a target. To ensure that only the proper people and algorithms have access, it is vital to secure big data platforms and monitor access through a combination of security controls. More security features are fortunately moving into big data platforms. Hadoop now offers Kerberosbased authentication, which can also be integrated with LDAP and Active Directory for security policy enforcement. Zettaset’s sHadoop was designed to mitigate Hadoop’s known architectural and input validation issues, and improve user-role.

audit tracking and user-level security for Hadoop. sHadoop also gives administrators the ability to establish and store a baseline security policy for all users, who can be compared against current security policy. Finally, sHadoop offers encryption for data at rest and in motion as it gets transmitted between Hadoop nodes. Another option for big data protection is Gazzang (purchased by Cloudera in 2014), which offers a product for end-to-end encryption of data stored and processed in Hadoop environments, data coming from streaming engines such as Apache Sqoop, metadata, and configuration information about a Hadoop cluster. Cloudera is also partnering with Intel on a chip-level encryption initiative called Project Rhino.12 Embed security into data Most businesses choose to build their big data environment in the cloud, where all-or-nothing retrieval policies of encrypted data may push them to store data unencrypted. In these situations, businesses should consider attribute-based encryption to help protect sensitive data and enable fine-grained access controls and encryption. With this technique, the attributes of a secret key are mathematically incorporated into the key itself. When attempting to access an encrypted file, policy checking within the decryption process checks that the policy is satisfied—the cloud does not know the individual file access policies. Sqrrl Enterprise, another big data platform, takes a data-centric security approach: data is embedded with security information that determines access and governance. Fine-grained access control is enabled at the cell level by evaluating a set of visibility labels that are embedded within the data each time a user attempts an operation on that data. Even search indexes, which may constitute a source of data leakage, are secured through termlevel security, ensuring that indexing respects the security policies of the underlying data elements. The platform is built on top of Accumulo, a distributed, hybrid column-oriented, key-value data store originally developed by the National Security Agency, and later submitted to the Apache Foundation. Conclusion Hadoop and other big data platforms are helping businesses analyze and derive insights in entirely new ways. To tap into the full benefit, however, businesses must amplify security measures to protect their information assets and reduce risk. Accenture recommends businesses apply the basic principles of information security to big data platforms, but progressively narrow the perimeter around enterprise data. Taking a data-centric security approach opens the door to processing big data analytics and producing even bigger insights for digital business strategies.

Data Integrity: Making data-driven decisions at Internet of Things scale

As the IoT proliferates, businesses will use data passed between interconnected devices, applications and processes to determine customer context, and then collaborate through platforms to provide the intelligent products and services that customers desire in the Outcome Economy. A connected digital ecosystem, combined with edge computing and smart machine-to-machine communications, will also expand the possibilities for using data collected from IoT devices to drive significantly faster decisions. However, as businesses collect, process and analyze increasingly larger data sets from devices at the edge, they must make sure they can rely on the integrity of that data to make decisions. According to a recent Gartner survey, the annual financial impact of inaccurate and poor quality data on businesses is, on average, $14.2 million. In the world of IoT, this will only be magnified.6 In order to optimize decisions, businesses will need assurance that their edge data is accurate, authentic and complete. This is especially critical as Intelligent Enterprises transition toward using software intelligence, in which applications and tools become smarter using technologies to trigger automatic action and make more informed business decisions. Even in today’s world, entire supply chains can be disrupted if data sent by the production floor, storage warehouses or distribution channels is inaccurate because of anything from malfunctioning sensors to intentional manipulation. Compound this with the scale and speed of IoT, and the ripple effect of bad decisions based on bad data can spread quickly.

Protect data on edge devices Since many edge devices do not have effective authentication, authorization or encryption controls, businesses should evaluate the use of IoT gateways/agents that specialize in providing data assurance. FreeScale, an embedded-solution vendor, has released products that provide strong security controls, including data integrity checks, to IoT devices. The company uses a combination of cryptographic modules, trust and platform assurance technologies, and signature detection to support security requirements of a trusted IoT architecture. Qualcomm is also developing smart gateways to address IoT security requirements by incorporating strong encryption and trusted platform principles. Although the architecture and use cases of these gateways differ, each supports communication with a connected infrastructure and enables new services. Implement assurance that scales The ever-increasing flow of data and customer information needed to fuel the digital business brings with it ever-increasing security and privacy challenges. Sensors and embedded devices enhance the infrastructure’s ability to collect data, and with it the ability to run more complex analytics. As a rite of passage, businesses must demonstrate they can maintain data integrity through every stage of the data lifecycle. And if personal information is being collected from consumers, then effective data retention, usage and sharing policies must be implemented. All along this flowing river of information will be numerous opportunities for third-parties to either accidently, or maliciously, alter the data. The impact of the initial decisions could cascade beyond the local system to an enterprise network or cloud. Businesses should use data-level security approaches that enforce policies through the entire lifecycle—from creation to disposal—as potential solutions to data governance and integrity challenges. Several data-centric security technologies aim to provide data protection enforcement policies across multiple platforms. Voltage Security (recently acquired by HP), Informatica and Protegrity are examples of companies that have developed focused solutions with data-centric capabilities like data classification and discovery, data security policy management, monitoring of user privileges and activity, auditing and reporting, and data protection.7 Low quality and low assurance data adds noise to the decision-making process, increasing the overall cost of extracting insights. As businesses establish infrastructure to collect and process data at speed and scale, they should implement data assurance and audit frameworks that scale to match. Businesses must also consider adding data quality tools designed for big data applications since collecting, processing and maintaining IoT data is a big data exercise. Gartner’s Magic Quadrant for Data Quality Tools provides an insightful snapshot of the current vendor landscape and their tools’ capabilities to handle data as an asset.8

Tie IoT protocols to business models Businesses must also be aware of the data assurance limitations of communication protocols. Higherlevel IoT communication protocols like MQTT, CoAP, DDS, 6LoWPAN, ZigBee, ModBus and WirelessHart offer different security capabilities based on which underlying networking protocol is used. For example, CoAP is built on user datagram protocol (UDP) and, as a result, cannot provide protocol security such as security socket layer (SSL) or transport layer security (TLS). 6LoWPAN is built on IPv6, which has its own set of vulnerabilities. NIST’s Framework for Improving Critical Infrastructure Cybersecurity provides a mechanism for using business drivers to help guide security activities, consider security risks and select an appropriate communication protocol to manage the business risk profile.9 While the framework targets critical infrastructure operators, there are best practices applicable to businesses considering expanding their IoT footprint. As businesses deploy new edge devices and management platforms, they should also take into account data assurance limitations of the communication protocols. In order to select a protocol with the right set of features while mitigating risk, it is important to consider application deployment, infrastructure management and security requirements. Conclusion As businesses look for new ways to gain insight from data, developing and maintaining a data assurance program should be at the center of their IoT strategy. Businesses need a framework that governs data assurance across edge infrastructure and instills a higher level of confidence in datadriven decisions. To maximize the potential benefits of the IoT, Accenture recommends building a data assurance program that directly ties to the business model and enables more informed decisions based on accurate data.

What is the security impact of using IoT edge devices to enable business decisions?

In October 2014, the IoT World Forum Architecture Committee published a seven-layer IoT reference model, in which layer three is edge computing.1 This layer is responsible for facilitating connectivity and analysis between physical devices, applications and business processes. As more businesses embrace this framework, securing the edge computing layer will be critical in enabling trustworthy business decisions. Fundamental processes like the ability of edge devices to authenticate, authorize and discover other devices and services will need to be analyzed through the security lens.

For legacy devices, businesses may choose to retrofit them with new capabilities to make them a part of their connected infrastructure. For example, manufacturing companies increasingly integrate their industrial systems in the field to optimize decision making and production. However, this may make it more difficult to implement authentication, authorization or encryption controls on these modified devices. Fully protecting this range of distributed devices will require businesses to emphasize and extend their security footprint far beyond existing borders.

Prioritize protecting edge devices Unlike traditional computing devices, IoT edge devices are typically embedded sensors and controllers with fixed functions and the ability to perform specific tasks. Smart meters, for example, allow two-way information flow between the electricity utility and customers. Traditionally, these devices are deployed outside the security perimeter and, in some cases, directly connected to the Internet. Since many device developers are not security specialists with a thorough understanding of potential threats, physical protections are not universal features of IoT edge devices. As a result, there are numerous ways to physically tamper with them.

Boost security for edge device infrastructure As businesses delegate increased authority to edge devices, they will need to pay even more attention to fundamental security controls like data protection, auditability, privilege management, vulnerability management, device authentication and network segmentation. The Shellshock vulnerability affected not only Linux-based servers and desktops, but also many IoT devices that used some variants of Linux.2 Exacerbating this issue was the lack of patching or anomalous activity detection capabilities in these devices. To avoid similar challenges, businesses must invest in ecosystem hygiene—integrating techniques to patch and securely update IoT devices and their configurations to reduce the impact of vulnerabilities spreading through the environment.

Establishing trust zones, wherein enterprise resources with similar security requirements are placed in the same network segment, has proven to be an effective risk mitigation technique in various enterprise systems. Businesses can extend this practice to edge infrastructure where devices need to be separated by their inherent capabilities and security features. It will be important to allow edge devices to communicate across different trust zones as network topologies are modified. To enable business decisions at the edge, businesses must ensure that edge device interaction is governed by appropriate authentication and algorithms that can take autonomous actions, and that the actions being performed are authorized. Intel’s IoT Gateway is an example of a solution to extend the capabilities of legacy devices and connect them to a next-generation intelligent infrastructure.3 This platform enables businesses to setup secure connections between devices in different trust zones, as well as build custom applications to manage authentication and authorization. The platform includes security management capabilities for resource-constrained devices, enabling cloud connectivity and more. Yet another way for businesses to boost security is to implement on edge devices foundational security controls like immutable identification and whitelisting of allowable agents and applications. Include system context in security planning As more decisions are made at the edge rather than at the core controller, context-awareness capabilities will underpin real-time decision making. Businesses should make sure intrusion detection and mitigation techniques take into account device behavior, its relationship with other devices and the overall context of services being provided. Is the device providing mission-critical data? Is it passively collecting data, or also responding and actuating? Is it part of a cohort of devices that depend on each other for decision making? Security planning needs to be holistic, taking into account the entire context of the system. Context dependence will drive physical and logical security models. To that end, Cisco is developing a distributed computing infrastructure to support edge analytics, which it calls “fog computing.”4 Using Cisco’s IOx capability, businesses can develop, manage and run applications that are closer to where actionable data is generated, and then delegate authority for pre-specified decision making. They can also build security capabilities using the IOx platform and develop use cases that expand security planning to perimeter and edge devices. Solutions like this will help businesses understand the interactions of devices, profile their activities and respond appropriately.

Manage edge intelligence with new governance model Data governance, communication and privacy models must keep pace with new frameworks and architectures being introduced to build end-toend IoT systems. As edge devices communicate and make decisions based off telemetry from various sources, it will be critical for businesses to maintain supervisory control. The nature of control needed will drive ecosystem requirements—such as determining whether cloud or private network solutions are preferred. Businesses need to architect a hierarchical supervisory controls model that optimizes the right security controls for the right business processes to achieve the full benefit of a flexible infrastructure. Unfortunately, security planning must also anticipate the likelihood of a breach—no organization seems immune from attack. During a cyber-attack, the supervisory control model must balance requirements for resiliency and availability—minimizing downtime—for ongoing device operations. Mocana, a company that focuses on securing non-traditional endpoints, has developed an IoT device framework for protecting edge data and enterprise communications. This framework consists of a range of capabilities— including key management, secure wireless and strong encryption—required for management of a distributed IoT infrastructure. Mocana also provides an API for rapid deployment of secure IoT devices that conform to business requirements and governance models. Another option comes from FogHorn Systems, which is developing an IoT application deployment platform that supports delivery and management of host applications embedded in edge devices.5 Businesses can use the platform to distribute applications from platform-as-a-service (PaaS) to onsite sensor networks. The FogHorn Edge Platform delivers service level agreement (SLA)-sensitive security applications to the edge, which can be triggered based on specific conditions. Conclusion Edge devices will have a profound impact on the security infrastructure, as IoT becomes an integral part of business in the digital ecosystem. Accenture recommends that businesses work to understand and proactively address the security implications of decisions being made at the edge. Managing and safeguarding edge devices, as well as the end-toend set of technologies that enable intelligent decisions, will be essential to future operations.