Over the past year, Ecosystm has conducted extensive research, including surveys and in-depth conversations with industry leaders, to uncover the most pressing topics and trends. And unsurprisingly, AI emerged as the dominant theme. Here are some insights from our research on the Healthcare industry.
Click here to download ‘AI in Healthcare: Success Stories & Insights’ as a PDF
AI is transforming the healthcare industry, offering unprecedented opportunities to improve patient outcomes and streamline operations. However, the successful implementation of AI in healthcare is not without its challenges. Those who can navigate these complexities and harness the power of AI will emerge as industry leaders, driving innovation and shaping the future of healthcare.
Despite the challenges, Healthcare organisations are witnessing early AI success in these 3 areas:
- 1. Diagnostics
- 2. Care Management
- 3. Operational Efficiency & Optimisation
Diagnostics
- Image Analysis. Analysing medical images (e.g., X-rays, MRIs) to detect diseases and abnormalities
- Diagnosis. Assisting clinicians in identifying and diagnosing diseases
- Early Detection. Detecting diseases at an early stage for more effective treatment
“Diagnostics is where our AI journey began – starting with image analysis for eye diseases, evolving to x-ray screening tools, and most recently, investing in digital stethoscopes for our doctors and nurses.” – CLINICIAN LEADER
Care Management
- Clinical Decision Support. Providing clinicians with recommendations and insights to improve patient care
- Personalised Treatment Plans. Personalising treatment protocols based on patient data and genetics
- Chronic Disease Management. Monitoring chronic diseases over multiple years
“Clinical decision support isn’t new, but AI has revolutionised it by enabling the system to send alerts and warnings proactively, rather than only when prompted.” – CLINICIAN LEADER
Operational Efficiency & Optimisation
- Supply Chain Management. Optimising inventory and supply chain processes
- Appointment Scheduling. Automating appointment booking and management
- Workflow Optimisation. Streamlining workflows and improving efficiency of clinical staff
“Patient satisfaction extends beyond clinical outcomes. Aspects like shorter waiting times during appointments, the availability of medications, and automated responses to common queries significantly enhance patient satisfaction. These are areas where we have successfully implemented AI.” – COO
India is undergoing a remarkable transformation across various industries, driven by rapid technological advancements, evolving consumer preferences, and a dynamic economic landscape. From the integration of new-age technologies like GenAI to the adoption of sustainable practices, industries in India are redefining their operations and strategies to stay competitive and relevant.
Here are some organisations that are leading the way.
Download ‘From Tradition to Innovation: Industry Transformation in India’ as a PDF
Redefining Customer Experience in the Financial Sector
Financial inclusion. India’s largest bank, the State Bank of India, is leading financial inclusion with its YONO app, to enhance accessibility. Initial offerings include five core banking services: cash withdrawals, cash deposits, fund transfers, balance inquiries, and mini statements, with plans to include account opening and social security scheme enrollments.
Customer Experience. ICICI Bank leverages RPA to streamline repetitive tasks, enhancing customer service with its virtual assistant, iPal, for handling queries and transactions. HDFC Bank customer preference insights to offer tailored financial solutions, while Axis Bank embraces a cloud-first strategy to digitise its platform and improve customer interfaces.
Indian banks are also collaborating with fintechs to harness new technologies for better customer experiences. YES Bank has partnered with Paisabazaar to simplify loan applications, and Canara HSBC Life Insurance has teamed up with Artivatic.AI to enhance its insurance processes via an AI-driven platform.
Improving Healthcare Access
Indian healthcare organisations are harnessing technology to enhance efficiency, improve patient experiences, and enable remote care.
Apollo Hospitals has launched an automated patient monitoring system that alerts experts to health deteriorations, enabling timely interventions through remote monitoring. Manipal Hospitals’ video consultation app reduces emergency department pressure by providing medical advice, lab report access, bill payments, appointment bookings, and home healthcare requests, as well as home medication delivery and Fitbit monitoring. Omni Hospitals has also implemented AI-based telemedicine for enhanced patient engagement and remote monitoring.
The government is also driving the improvement of healthcare access. eSanjeevani is the world’s largest government-owned telemedicine system, with the capacity to handle up to a million patients a day.
Driving Retail Agility & Consumer Engagement
India’s Retail sector, the fourth largest globally, contributes over 10% of the nation’s GDP. To stay competitive and meet evolving consumer demands, Indian retailers are rapidly adopting digital technologies, from eCommerce platforms to AI.
Omnichannel Strategies. Reliance Retail integrates physical stores with digital platforms like JioMart to boost sales and customer engagement. Tata CLiQ’s “phygital” approach merges online and offline shopping for greater convenience while Shoppers Stop uses RFID and data analytics for improved in-store experiences, online shopping, and targeted marketing.
Retail AI. Flipkart’s AI-powered shopping assistant, Flippi uses ML for conversational product discovery and intuitive guidance. BigBasket employs IoT-led AI to optimise supply chain and improve product quality.
Reshaping the Automotive Landscape
Tech innovation, from AI/ML to connected vehicle technologies, is revolutionising the Automotive sector. This shift towards software-defined vehicles and predictive supply chain management underscores the industry’s commitment to efficiency, transparency, safety, and environmental sustainability.
Maruti Suzuki’s multi-pronged approach includes collaborating with over 60 startups through its MAIL program and engaging Accenture to drive tech change. Maruti has digitised 24 out of 26 customer touchpoints, tracking every interaction to enhance customer service. In the Auto OEM space, they are shifting to software-defined vehicles and operating models.
Tata Motors is leveraging cloud, AI/ML, and IoT to enhancing efficiency, improving safety, and driving sustainability across its operations. Key initiatives include connected vehicles, automated driving, dealer management, cybersecurity, electric powertrains, sustainability, and supply chain optimisation.
Streamlining India’s Logistics Sector
India’s logistics industry is on the cusp of a digital revolution as it embraces cutting-edge technologies to streamline processes and reduce environmental impact.
Automation and Predictive Analytics. Automation is transforming warehousing operations in India, with DHL India automating sortation centres to handle 6,000 shipments per hour. Predictive analytics is reshaping logistics decision-making, with Delhivery optimising delivery routes to ensure timely service.
Sustainable Practices. The logistics sector contributes one-third of global carbon emissions. To combat this, Amazon India will convert its delivery fleet to 100% EVs by 2030 to reduce emissions and fuel costs. Blue Energy Motors is also producing 10,000 heavy-duty LNG trucks annually for zero-emission logistics.
The rapid adoption of technology in India is driving a surge in demand for AI solutions across sectors like finance, education, healthcare, and agriculture. AI is revolutionising these industries by making services more efficient, personalised, and accessible. This growing dependence on AI has created a fertile ground for innovation, propelling India’s emergence as a global hub for AI startups. With over 6,200 AI startups operating in the country, India offers a dynamic and challenging landscape for entrepreneurs seeking to make a meaningful impact.
Fuelling AI Innovation: India’s Strategic Investment
Earlier this year, the government allocated USD 1.3 billion for the India AI Mission, solidifying its commitment to AI. This comprehensive program is designed to catalyse the AI innovation ecosystem within the country. At the heart of this ecosystem’s development lies the expansion of compute infrastructure, a critical resource for AI startups. By providing access to powerful computing resources, the India AI Mission is empowering startups to scale their solutions and compete on a global level.
Beyond infrastructure, the initiative focuses on fostering collaborations between academia, industry, and startups to drive R&D. By creating a supportive environment that promotes knowledge sharing and resource accessibility, the India AI Mission aims to position India as a leader in the AI landscape.
A Spotlight on Indian Startups
Driving Industry Innovation
Healthcare. India’s vibrant AI startup ecosystem is driving innovation in healthcare, with companies leveraging AI to address critical challenges and improve patient outcomes.
- Cancer-Focused AI Startups. Several startups are revolutionising cancer care with AI-driven innovations. Niramai, globally recognised for its innovation, uses AI and thermal imaging for early breast cancer detection, particularly effective in younger women and dense breast tissue. Onward Assist provides predictive analytics for oncology, helping oncologists manage patient data and improve the accuracy of cancer care decisions. Similarly, Atom360 focuses on oral cancer screening with an AI-powered app that offers quick, affordable access to critical information, enhancing oral healthcare in underserved areas.
- AI-Driven Diagnostic Solutions. AI is significantly advancing diagnostics, enhancing accuracy, and reducing misdiagnosis. SigTuple develops AI-driven diagnostic solutions for medical imaging and pathology, improving accuracy and efficiency in disease detection. Endimension Technology, incubated at IIT Bombay, develops algorithms for detecting abnormalities in medical scans, aiming to reduce misdiagnosis and radiologist workload. Tricog Health delivers AI solutions for rapid heart attack diagnosis, reducing diagnosis time and improving outcomes, especially in underserved regions.
Financial Services. Fintechs have been at the forefront of AI-led innovations, offering innovative solutions for insurance, lending, and microfinance. Artivatic uses AI to transform traditional insurance systems into digital, personalised offerings, making coverage more accessible and affordable for a broader range of consumers. ZestMoney leverages AI for digital lending, providing credit to individuals without a credit history through easy EMI plans, and enhancing financial access. Meanwhile, mPokket offers instant micro-loans to students and young professionals, addressing short-term financial needs with flexible loan options and minimal documentation.
Other Industries. Beyond healthcare and financial services, AI startups are driving innovation across various industries, tackling critical challenges. Entropik uses AI to analyse human emotions and behaviour, helping businesses gain deeper insights into consumer preferences for market research and optimising user experiences. In agriculture, Intello Labs applies AI and computer vision to assess the quality of fresh produce, reducing food waste and improving supply chain efficiency. Similarly, AgNext enhances food value chains by offering AI-driven, real-time quality assessments through its SaaS platform, promoting safety and transparency in agribusiness.
Transforming Businesses
Technology for Security & Fraud. AI startups are offering innovative solutions tailored to organisations’ needs. SpoofSense combats deepfakes and identity fraud with advanced facial liveness detection, ensuring secure user verification by distinguishing between real users and spoofed images. Eagle Eye Networks provides cloud-based video surveillance solutions, using AI to offer real-time monitoring and analytics. In the e-commerce space, ThirdWatch uses AI to detect and prevent fraud in real-time by analysing user behaviour and transaction patterns, reducing financial losses for online retailers.
Tech Development. AI startups are empowering organisations to accelerate innovation and enhance productivity. Haptik helps businesses build intelligent virtual assistants, powering chatbots and voice bots across industries to improve customer engagement. DhiWise automates the development process, enabling faster app creation by converting designs into code. Additionally, Fluid AI provides advanced AI solutions like predictive analytics and natural language processing for sectors like finance, retail, and healthcare. Mihup enhances contact centre performance with its conversation intelligence platform, while Yellow.ai enables enterprises to automate customer engagement through its GenAI-powered platform, creating seamless and scalable customer service experiences.
Empowering People
AI startups are empowering individuals by providing personalised services that enhance learning, creativity, and financial management. SuperKalam and ZuAI offer students tailored learning experiences, using AI to create interactive lessons and assessments that adapt to individual learning styles, improving student engagement and outcomes. For creative professionals, Mugafi combines AI with human mentoring to assist writers in generating ideas and developing scripts, enabling them to create intellectual property with greater efficiency. Wright Research empowers individuals to make informed financial decisions through AI-powered investment advice, while Vahan simplifies job searches for blue-collar workers by using AI to match candidates with suitable employment opportunities via WhatsApp.
Promoting ESG
AI startups are driving meaningful change by optimising processes and creating economic opportunities. Ossus Biorenewables enhances biofuel production through AI, reducing waste and increasing efficiency in renewable energy generation, while Ishitva Robotic Systems promotes sustainability by automating waste sorting and recycling, contributing to a more efficient and circular economy. Karya connects rural workers with digital tasks, offering fair wages and skills development by matching them to tasks suited to their abilities using machine learning. In agriculture, KissanAI helps farmers improve crop yields and manage resources effectively through personalised, data-driven recommendations. ElasticRun improves last-mile delivery logistics in rural areas, enabling businesses to reach underserved markets.
Conclusion
Nvidia CEO Jensen Huang noted India’s potential to become the “largest exporter of AI,” signalling vast global opportunities. India’s AI startups are at the forefront of innovation but face hurdles such as fierce competition for skilled talent, navigating complex regulations, and securing funding. With strategic focus on these challenges and the backing of initiatives like Digital India and Startup India, India’s AI ecosystem can seize emerging market opportunities, accelerate tech advancements, and make a substantial impact on the global AI landscape.
India’s digital journey has been nothing short of remarkable, driven by a robust Digital Public Infrastructure (DPI) framework known as the India Stack. Over the past decade, the government, in collaboration with public and private entities, has built this digital ecosystem to empower citizens, improve governance, and foster economic growth.
The India Stack is a set of open APIs and platforms that provide a foundation for large-scale public service delivery and innovation. It enables governments, businesses, startups, and developers to leverage technology to offer services to millions of Indians, especially those in underserved areas.
The India Stack is viewed as a layered infrastructure, addressing identity, payments, data, and services.
Click here to download The India Stack: A Foundation for Digital India as a PDF
Four Pillars of the Digital Stack
The four layers of India Stack include:
- Presenceless Layer. Aadhaar enables remote authentication, providing a digital ID that requires only a 12-digit number and a fingerprint or iris scan, eliminating the need for physical documents. It prevents duplicate and fake identities.
- Paperless Layer. Reliance on digital records, using Aadhaar eKYC, eSign, and Digital Locker. It enables secure digital storage and retrieval, creating a paperless system for verifying and accessing documents anytime, on any device.
- Cashless Layer. Led by NPCI, this aims to universalise digital payments. UPI enables instant, secure money transfers between bank accounts using a simple Virtual Payment Address (VPA), moving transactions into the digital age for transparency and ease of use.
- Consent Layer. Enables secure, user-controlled data sharing through electronic consent, allowing data to flow freely. The Account Aggregator ecosystem benefits most, with AA acting as a thin data aggregation layer between Financial Information Providers (FIPs) and Financial Information Users (FIUs).
The Impact of the India Stack
The India Stack has played a pivotal role in the country’s rapid digitalisation:
Financial Inclusion. Aadhaar-enabled payment systems (AePS) and UPI have significantly expanded financial access, increasing inclusion from 25% in 2008 to 80% in 2024, particularly benefiting rural and underserved communities.
Boost to Digital Payments. The India Stack has fuelled exponential growth in digital payments, with UPI processing 10 billion monthly transactions. This has driven the rise of digital wallets, fintech platforms, and digitisation of small businesses.
Better Government Services. Aadhaar authentication has improved the delivery of government schemes like Direct Benefit Transfers (DBTs), Public Distribution System (PDS), and pensions, ensuring transparency and reducing leakages.
The India Stack: A Catalyst for Startup Success
The India Stack is fuelling startup innovation by providing a robust digital infrastructure. It enables entrepreneurs to build services like digital payments, eCommerce, and financial solutions for underserved populations. Platforms such as Aadhaar and UPI have paved the way for businesses to offer secure, seamless transactions, allowing startups like Paytm and BharatPe to thrive. These innovations are driving financial inclusion, empowering rural entrepreneurs, and creating opportunities in sectors like lending and healthtech, supported by global and domestic investments.
From Local Success to Global Inspiration
The impact of the India Stack’s success is being felt worldwide. Global giants such as Google Pay, WhatsApp, and Amazon Pay are drawing inspiration from it to enhance their global payment systems. Alphabet CEO Sundar Pichai plans to apply lessons from Google Pay’s Indian experience to other markets.
While India Stack has achieved significant success, there is still room for improvement. Strengthening data privacy and security is crucial as personal data collection continues to expand. The Digital Personal Data Protection Act aims to address these issues, but balancing innovation with privacy protection remains a challenge.
Bridging the digital divide by expanding Internet access and improving digital literacy, especially for rural and older populations, is key to ensuring that everyone can benefit from the India Stack’s advantages.
Healthcare delivery and healthtech have made significant strides; yet, the fundamental challenges in healthcare have remained largely unchanged for decades. The widespread acceptance and integration of digital solutions in recent years have supported healthcare providers’ primary goals of enhancing operational efficiency, better resource utilisation (with addressing skill shortages being a key driver), improving patient experience, and achieving better clinical outcomes. With governments pushing for advancements in healthcare outcomes at sustainable costs, the concept of value-based healthcare has gained traction across the industry.
Technology-driven Disruption
Healthcare saw significant disruptions four years ago, and while we will continue to feel the impact for the next decade, one positive outcome was witnessing the industry’s ability to transform amid such immense pressure. I am definitely not suggesting another healthcare calamity! But disruptions can have a positive impact – and I believe that technology will continue to disrupt healthcare at pace. Recently, my colleague Tim Sheedy shared his thoughts on how 2024 is poised to become the year of the AI startup, highlighting innovative options that organisations should consider in their AI journeys. AI startups and innovators hold the potential to further the “good disruption” that will transform healthcare.
Of course, there are challenges associated, including concerns on ethical and privacy-related issues, the reliability of technology – particularly while scaling – and on professional liability. However, the industry cannot overlook the substantial number of innovative startups that are using AI technologies to address some of the most pressing challenges in the healthcare industry.
Why Now?
AI is not new to healthcare. Many would cite the development of MYCIN – an early AI program aimed at identifying treatments for blood infections – as the first known example. It did kindle interest in research in AI and even during the 1980s and 1990s, AI brought about early healthcare breakthroughs, including faster data collection and processing, enhanced precision in surgical procedures, and research and mapping of diseases.
Now, healthcare is at an AI inflection point due to a convergence of three significant factors.
- Advanced AI. AI algorithms and capabilities have become more sophisticated, enabling them to handle complex healthcare data and tasks with greater accuracy and efficiency.
- Demand for Accessible Healthcare. Healthcare systems globally are striving for better care amid resource constraints, turning to AI for efficiency, cost reduction, and broader access.
- Consumer Demand. As people seek greater control over their health, personalised care has become essential. AI can analyse vast patient data to identify health risks and customise care plans, promoting preventative healthcare.
Promising Health AI Startups
As innovative startups continue to emerge in healthcare, we’re particularly keeping an eye on those poised to revolutionise diagnostics, care delivery, and wellness management. Here are some examples.
DIAGNOSTICS
- Claritas HealthTech has created advanced image enhancement software to address challenges in interpreting unclear medical images, improving image clarity and precision. A cloud-based platform with AI diagnostic tools uses their image enhancement technology to achieve greater predictive accuracy.
- Ibex offers Galen, a clinical-grade, multi-tissue platform to detect and grade cancers, that integrate with third-party digital pathology software solutions, scanning platforms, and laboratory information systems.
- MEDICAL IP is focused on advancing medical imaging analysis through AI and 3D technologies (such as 3D printing, CAD/CAM, AR/VR) to streamline medical processes, minimising time and costs while enhancing patient comfort.
- Verge Genomics is a biopharmaceutical startup employing systems biology to expedite the development of life-saving treatments for neurodegenerative diseases. By leveraging patient genomes, gene expression, and epigenomics, the platform identifies new therapeutic gene targets, forecasts effective medications, and categorises patient groups for enhanced clinical efficacy.
- X-Zell focuses on advanced cytology, diagnosing diseases through single atypical cells or clusters. Their plug-and-play solution detects, visualises, and digitises these phenomena in minimally invasive body fluids. With no complex specimen preparation required, it slashes the average sample-to-diagnosis time from 48 hours to under 4 hours.
CARE DELIVERY
- Abridge specialises in automating clinical notes and medical discussions for physicians, converting patient-clinician conversations into structured clinical notes in real time, powered by GenAI. It integrates seamlessly with EMRs such as Epic.
- Waltz Health offers AI-driven marketplaces aimed at reducing costs and innovative consumer tools to facilitate informed care decisions. Tailored for payers, pharmacies, and consumers, they introduce a fresh approach to pricing and reimbursing prescriptions that allows consumers to purchase medication at the most competitive rates, improving accessibility.
- Acorai offers a non-invasive intracardiac pressure monitoring device for heart failure management, aimed at reducing hospitalisations and readmissions. The technology can analyse acoustics, vibratory, and waveform data using ML to monitor intracardiac pressures.
WELLNESS MANAGEMENT
- Anya offers AI-driven support for women navigating life stages such as fertility, pregnancy, parenthood, and menopause. For eg. it provides support during the critical first 1,001 days of the parental journey, with personalised advice, tracking of developmental milestones, and connections with healthcare professionals.
- Dacadoo’s digital health engagement platform aims to motivate users to adopt healthier lifestyles through gamification, social connectivity, and personalised feedback. By analysing user health data, AI algorithms provide tailored insights, goal-setting suggestions, and challenges.
Conclusion
There is no question that innovative startups can solve many challenges for the healthcare industry. But startups flourish because of a supportive ecosystem. The health innovation ecosystem needs to be a dynamic network of stakeholders committed to transforming the industry and health outcomes – and this includes healthcare providers, researchers, tech companies, startups, policymakers, and patients. Together we can achieve the longstanding promise of accessible, cost-effective, and patient-centric healthcare.
In the Ecosystm Predicts: The Top 5 Healthcare Trends in 2022 we have said that 2022 will be the year when we start seeing the second-order impacts of the pandemic and we will see healthcare providers address these impacts. This means an increase in tech adoption and a greater (and in some cases, renewed) interest in tech providers to focus on the Healthcare industry.
Last month, we saw announcements of Francisco Partners acquiring the healthcare data and analytics assets from the IBM Watson Health business unit. This was neither unexpected nor an isolated news – it follows a series of significant Healthcare tech news from last year.
Here are some announcements from 2021 that I feel give us an indication of where the Healthcare tech market is headed.
- Oracle’s acquisition of Cerner
- Microsoft’s continued focus on Healthcare with the Nuance acquisition
- The acquisition of athenahealth by private equity firms
- Examples of the focus on mental health such as the creation of Headspace Health
- The interest of consumer apps in Healthcare as witnessed by Peloton’s corporate wellness programme.
Read on to find out more about these announcements. And let me know what you think is the most interesting Healthcare tech announcement in the last year.
Download “Where is Healthcare Tech Headed?” slides as a PDF.
Cities worldwide have been facing unexpected challenges since 2020 – and 2022 will see them continue to struggle with the after-effects of COVID-19. However, there is one thing that governments have learnt during this ongoing crisis – technology is not the only aspect of a Cities of the Future initiative. Besides technology, Cities of the Future will start revisiting organisational and institutional structures, prioritise goals, and design and deploy an architecture with data as its foundation.
Cities of the Future will focus on being:
- Safe. Driven by the ongoing healthcare crisis
- Secure. Driven by the multiple cyber attacks on critical infrastructure
- Sustainable. Driven by citizen consciousness and global efforts such as the COP26
- Smart. Driven by the need to be agile to face future uncertainties
Read on to find out what Ecosystm Advisors, Peter Carr, Randeep Sudan, Sash Mukherjee and Tim Sheedy think will be the leading Cities of the Future trends for 2022.
Click here to download Ecosystm Predicts: The Top 5 Trends for Cities of the Future in 2022
As we return to the office, there is a growing reliance on devices to tell us how safe and secure the environment is for our return. And in specific application areas, such as Healthcare and Manufacturing, IoT data is critical for decision-making. In some sectors such as Health and Wellness, IoT devices collect personally identifiable information (PII). IoT technology is so critical to our current infrastructures that the physical wellbeing of both individuals and organisations can be at risk.
Trust & Data
IoT are also vulnerable to breaches if not properly secured. And with a significant increase in cybersecurity events over the last year, the reliance on data from IoT is driving the need for better data integrity. Security features such as data integrity and device authentication can be accomplished through the use of digital certificates and these features need to be designed as part of the device prior to manufacturing. Because if you cannot trust either the IoT devices and their data, there is no point in collecting, running analytics, and executing decisions based on the information collected.
We discuss the role of embedding digital certificates into the IoT device at manufacture to enable better security and ongoing management of the device.
Securing IoT Data from the Edge
So much of what is happening on networks in terms of real-time data collection happens at the Edge. But because of the vast array of IoT devices connecting at the Edge, there has not been a way of baking trust into the manufacture of the devices. With a push to get the devices to market, many manufacturers historically have bypassed efforts on security. Devices have been added on the network at different times from different sources.
There is a need to verify the IoT devices and secure them, making sure to have an audit trail on what you are connecting to and communicating with.
So from a product design perspective, this leads us to several questions:
- How do we ensure the integrity of data from devices if we cannot authenticate them?
- How do we ensure that the operational systems being automated are controlled as intended?
- How do we authenticate the device on the network making the data request?
Using a Public Key Infrastructure (PKI) approach maintains assurance, integrity and confidentiality of data streams. PKI has become an important way to secure IoT device applications, and this needs to be built into the design of the device. Device authentication is also an important component, in addition to securing data streams. With good design and a PKI management that is up to the task you should be able to proceed with confidence in the data created at the Edge.
Johnson Controls/DigiCert have designed a new way of managing PKI certification for IoT devices through their partnership and integration of the DigiCert ONE™ PKI management platform and the Johnson Controls OpenBlue IoT device platform. Based on an advanced, container-based design, DigiCert ONE allows organisations to implement robust PKI deployment and management in any environment, roll out new services and manage users and devices across your organisation at any scale no matter the stage of their lifecycle. This creates an operational synergy within the Operational Technology (OT) and IoT spaces to ensure that hardware, software and communication remains trusted throughout the lifecycle.
Rationale on the Role of Certification in IoT Management
Digital certificates ensure the integrity of data and device communications through encryption and authentication, ensuring that transmitted data are genuine and have not been altered or tampered with. With government regulations worldwide mandating secure transit (and storage) of PII data, PKI can help ensure compliance with the regulations by securing the communication channel between the device and the gateway.
Connected IoT devices interact with each other through machine to machine (M2M) communication. Each of these billions of interactions will require authentication of device credentials for the endpoints to prove the device’s digital identity. In such scenarios, an identity management approach based on passwords or passcodes is not practical, and PKI digital certificates are by far the best option for IoT credential management today.
Creating lifecycle management for connected devices, including revocation of expired certificates, is another example where PKI can help to secure IoT devices. Having a robust management platform that enables device management, revocation and renewal of certificates is a critical component of a successful PKI. IoT devices will also need regular patches and upgrades to their firmware, with code signing being critical to ensure the integrity of the downloaded firmware – another example of the close linkage between the IoT world and the PKI world.
Summary
PKI certification benefits both people and processes. PKI enables identity assurance while digital certificates validate the identity of the connected device. Use of PKI for IoT is a necessary trend for sense of trust in the network and for quality control of device management.
Identifying the IoT device is critical in managing its lifespan and recognizing its legitimacy in the network. Building in the ability for PKI at the device’s manufacture is critical to enable the device for its lifetime. By recognizing a device, information on it can be maintained in an inventory and its lifecycle and replacement can be better managed. Once a certificate has been distributed and certified, having the control of PKI systems creates life-cycle management.
Organisations have found that it is not always desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security. So why not create learning processes at the Edge?
What challenges does IoT bring?
Sensors are now generating such an increasing volume of data that it is not practical that all of it be sent to the cloud for processing. From a data privacy perspective, some sensor data is sensitive and sending data and images to the cloud will be subject to privacy and security constraints.
Regardless of the speed of communications, there will always be a demand for more data from more sensors – along with more security checks and higher levels of encryption – causing the potential for communication bottlenecks.
As the network hardware itself consumes power, sending a constant stream of data to the cloud can be taxing for sensor devices. The lag caused by the roundtrip to the cloud can be prohibitive in applications that require real-time response inputs.
Machine learning (ML) at the Edge should be prioritised to leverage that constant flow of data and address the requirement for real-time responses based on that data. This should be aided by both new types of ML algorithms and by visual processing units (VPUs) being added to the network.
By leveraging ML on Edge networks in production facilities, for example, companies can look out for potential warning signs and do scheduled maintenance to avoid any nasty surprises. Remember many sensors are linked intrinsically to public safety concerns such as water processing, supply of gas or oil, and public transportation such as metros or trains.
Ecosystm research shows that deploying IoT has its set of challenges (Figure 1) – many of these challenges can be mitigated by processing data at the Edge.
Predictive analytics is a fundamental value proposition for IoT, where responding faster to issues or taking action before issues occur, is key to a high return on investment. So, using edge computing for machine learning located within or close to the point of data gathering can in some cases be a more practical or socially beneficial approach.
In IoT the role of an edge computer is to pre-process data and act before the data is passed on to the main server. This allows a faster, low latency response and minimal traffic between the cloud server processing and the Edge. However, a better understanding of the benefits of edge computing is required if it has to be beneficial for a number of outcomes.
If we can get machine learning happening in the field, at the Edge, then we reduce the time lag and also create an extra trusted layer in unmanned production or automated utilities situations. This can create more trusted environments in terms of possible threats to public services.
What kind of examples of machine learning in the field can we see?
Healthcare
Health systems can improve hospital patient flow through machine learning (ML) at the Edge. ML offers predictive models to assist decision-makers with complex hospital patient flow information based on near real-time data.
For example, an academic medical centre created an ML pipeline that leveraged all its data – patient administration, EHR and clinical and claims data – to create learnings that could predict length of stay, emergency department (ED) arrival models, ED admissions, aggregate discharges, and total bed census. These predictive models proved effective as the medical centre reduced patient wait times and staff overtime and was able to demonstrate improved patient outcomes. And for a medical centre that use sensors to monitor patients and gather requests for medicine or assistance, Edge processing means keeping private healthcare data in-house rather than sending it off to cloud servers.
Retail
A retail store could use numerous cameras for self-checkout and inventory management and to monitor foot traffic. Such specific interaction details could slow down a network and can be replaced by an on-site Edge server with lower latency and a lower total cost. This is useful for standalone grocery pop-up sites such as in Sweden and Germany.
In Retail, k-nearest neighbours is often used in ML for abnormal activity analysis – this learning algorithm can also be used for visual pattern recognition used as part of retailers’ loss prevention tactics.
Summary
Working with the data locally on the Edge, creates reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data, and increased data privacy.
Cloud and Edge computing that uses machine learning can together provide the best of both worlds: decentralised local storage, processing and reaction, and then uploading to the cloud, enabling additional insights, data backups (redundancy), and remote access.
