Our smartphone is about to get smarter, thanks to artificial intelligence (AI) and machine learning (ML). And that has huge implications for enterprise support for mobility. We at India Training Services were analyzing the inherent risks & educate the enterprise as well as individuals to address security laps in such smart adoption. This is just a summery of our finding in last few months.
Enterprise mobility has long promised to allow workers to be productive wherever they are, to speed up business processes and to improve accuracy and efficiency by putting the most up-to-date data in the hands of workers in the field, says Kevin Burden, vice president of mobility research and data strategy at 451 Research. The addition of AI will help deliver on those promises.
The ways it will do that are multifaceted, with the effects seen in the areas of device management, user experience, security, applications and the very devices themselves. At the same time, new concerns about privacy are sure to arise as AI and ML become ever more efficient at gathering data points.
AI is going to mean new applications and even possibly new device types, primarily because AI will alter and improve the business logic within apps. Applications will be able to take advantage of advanced user interfaces with speech and visual gesture recognition. One element of enterprise mobility that will clearly benefit from AI is the organizational challenges that were created by having a disparate and mobile workforce. Application providers will apply ML to user activity streams, giving organizations insight into how end users spend their time, he says. As patterns of behavior are identified, organizations will be able to improve processes and the user experience.
Easier authentication is one example. Pattern recognition is an AI strength. Because AI can gather huge amounts of such data and recognize anomalies with ease, it can make authentication much more transparent for users..
Some of the more advanced algorithms detect how a user enters text and analyze their gait. Pair those distinctive patterns with information on the user’s active connections and GPS data. The number of layers of multi-factor authentication or constant requirements to enter passwords could be greatly reduced. Take this mobile device management course from India Training Services and learn how to secure devices in your company/ group/ homes without degrading the user experience.
Another AI/ML important improvement will be in speech-to-text capabilities, allowing that technology to replace smartphone data input in some situations. Verticals such as medical and others will use speech for data input for basic tasks such as records and workflow updates on regular basis. The applications will become intuitive in whole new ways: ML will also be integrated more into mobile applications to enable quicker & intelligent decisions, responses and inputs to anticipate user actions, as opposed to requiring users to look for options in windows and drop downs.
It's not just IT will benefit from AI’s and ML’s assistance with device management. The technology can be used to scan all of the devices in an organization and proactively notify the administrator of issues, such as the discovery that 25% of the organization’s Android devices are two versions out of date. Even more helpful for IT organizations that are short of personnel is the potential to automate actions based on the information discovered by AI/ML. The technology will really pay off for IT once the systems can use AI to detect and remediate issues on the fly.
IT is also likely to appreciate many of the AI-fueled user-experience enhancements that are coming to email, contact and calendar tools as vendors add personal-assistant technology. It’s fairly common already for calendars to use AI to tell users when they should leave for an appointment. This is already started in many event management programs.
The advantage to IT isn’t direct, but many IT departments want users to stick to their company-provided email, contact and calendar tools when working, as a way to protect and segregate work data from personal and other needs. The new user-facing convenience features could make using those tools more appealing to users.
While it’s still getting clear day by day that how AI will impact the overall mobility market on a long-term basis, it is certain that the enterprise mobility management space is very crowded, without any real significant differentiation, so vendors will look to AI for new ways to innovate build more cost effective & time saving ways to to get results out of this technologies.
AI and security, perhaps the area with the greatest potential to get a boost from AI, and particularly its pattern-recognition chops, is security. Certainly many vendors are already incorporating AI/ML in their security offerings as a way to boost performance.
One area where vendors already have offerings is ML-based mobile threat detection. For example, major strategic game uses ML in its new immature Threat Defense mechanisms, which employs usage and behavioral analysis to detect suspicious behaviors in mobile apps or networks and then learns from the information it gathers to continuously improve its ability to detect malware and rogue networks.
Many new Mobile developers have integrated deep learning into its endpoint security products that provide what it calls “predictive security.” The company aims to extend this deep learning layer to all endpoints, including mobile ones. It has also introduced an email protection tool that uses the same technology to intercept more threats before they can make it onto the endpoints.
Other vendors see an opportunity to use AI to help IT departments that are stretched thin to make sense of all the data that is gathered by their existing endpoint management tools. Among them is Citrix, whose unified endpoint management offering also manages all devices that enter the workplace, including laptops, mobile phones, tablets and wearable. The Citrix security analytics application monitors those devices and helps IT to apply security policies and ensure that the network remains secure.
Citrix Analytics also performs user-behavior analytics, applying machine learning to categorize users as high, medium or low risks and then adjusting the risk scores as more data comes into the system.
IBM, meanwhile, has developed MaaS360 with Watson, a cloud-based application designed to help IT administrators make sense of the massive amounts of data generated by endpoints and their users, apps and content. It applies cognitive technologies to security, end-user productivity, mobile app management and administration.
Enterprise mobility management users are inundated with more information than they can absorb about apps, configuration/policy best practices, productivity tools, and emerging threats and vulnerabilities, IBM explains. IBM MaaS360 delivers cognitive insights, embedded in the platform, to help organizations wade through the information they’re gathering and distill it into insights and recommendations that are relevant to their business. The core of MaaS360 is IBM Watson technology, which can index and annotate huge volumes of datasets to look for relevant data that applies contextually to each individual client deployment of MaaS360.
A privacy backlash? One dark cloud on the AI/ML front is data privacy.
Users have become more aware of the perils of their personal information ending up in the hands of companies such as Facebook and Google, etc. So the idea of an employer or other company retaining the outputs of their mobile devices, apps and data usage which some calls workplace analytics is sure to meet opposition from some users.
These concerns can’t be ignored, especially given the emergence of strict regulations such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act of 2018. These regulatory concerns could strip the utility from mobile offerings dependent on AI/ML. While user push back on data will not negate the value of AI/ML in mobile offerings, it could impede the collection of data for some or all users and without any data the results will be deteriorated. That, in turn, could make the data less useful for some groups of users or some regions, while still providing value to others.
To improve this, organizations must forthright in discussing what data they collect and how it will be used. May big IT players advises clients to illustrate the outcome and its benefit to users and take pains to note what won’t be collected or done with data. The list of what IT does not do with data should almost always be longer than the list of what it does or can do with data.
Feel free to contact me at ravindrapande@gmail.com in case need any further details.
Enterprise mobility has long promised to allow workers to be productive wherever they are, to speed up business processes and to improve accuracy and efficiency by putting the most up-to-date data in the hands of workers in the field, says Kevin Burden, vice president of mobility research and data strategy at 451 Research. The addition of AI will help deliver on those promises.
The ways it will do that are multifaceted, with the effects seen in the areas of device management, user experience, security, applications and the very devices themselves. At the same time, new concerns about privacy are sure to arise as AI and ML become ever more efficient at gathering data points.
AI is going to mean new applications and even possibly new device types, primarily because AI will alter and improve the business logic within apps. Applications will be able to take advantage of advanced user interfaces with speech and visual gesture recognition. One element of enterprise mobility that will clearly benefit from AI is the organizational challenges that were created by having a disparate and mobile workforce. Application providers will apply ML to user activity streams, giving organizations insight into how end users spend their time, he says. As patterns of behavior are identified, organizations will be able to improve processes and the user experience.
Easier authentication is one example. Pattern recognition is an AI strength. Because AI can gather huge amounts of such data and recognize anomalies with ease, it can make authentication much more transparent for users..
Some of the more advanced algorithms detect how a user enters text and analyze their gait. Pair those distinctive patterns with information on the user’s active connections and GPS data. The number of layers of multi-factor authentication or constant requirements to enter passwords could be greatly reduced. Take this mobile device management course from India Training Services and learn how to secure devices in your company/ group/ homes without degrading the user experience.
Another AI/ML important improvement will be in speech-to-text capabilities, allowing that technology to replace smartphone data input in some situations. Verticals such as medical and others will use speech for data input for basic tasks such as records and workflow updates on regular basis. The applications will become intuitive in whole new ways: ML will also be integrated more into mobile applications to enable quicker & intelligent decisions, responses and inputs to anticipate user actions, as opposed to requiring users to look for options in windows and drop downs.
It's not just IT will benefit from AI’s and ML’s assistance with device management. The technology can be used to scan all of the devices in an organization and proactively notify the administrator of issues, such as the discovery that 25% of the organization’s Android devices are two versions out of date. Even more helpful for IT organizations that are short of personnel is the potential to automate actions based on the information discovered by AI/ML. The technology will really pay off for IT once the systems can use AI to detect and remediate issues on the fly.
IT is also likely to appreciate many of the AI-fueled user-experience enhancements that are coming to email, contact and calendar tools as vendors add personal-assistant technology. It’s fairly common already for calendars to use AI to tell users when they should leave for an appointment. This is already started in many event management programs.
The advantage to IT isn’t direct, but many IT departments want users to stick to their company-provided email, contact and calendar tools when working, as a way to protect and segregate work data from personal and other needs. The new user-facing convenience features could make using those tools more appealing to users.
While it’s still getting clear day by day that how AI will impact the overall mobility market on a long-term basis, it is certain that the enterprise mobility management space is very crowded, without any real significant differentiation, so vendors will look to AI for new ways to innovate build more cost effective & time saving ways to to get results out of this technologies.
AI and security, perhaps the area with the greatest potential to get a boost from AI, and particularly its pattern-recognition chops, is security. Certainly many vendors are already incorporating AI/ML in their security offerings as a way to boost performance.
One area where vendors already have offerings is ML-based mobile threat detection. For example, major strategic game uses ML in its new immature Threat Defense mechanisms, which employs usage and behavioral analysis to detect suspicious behaviors in mobile apps or networks and then learns from the information it gathers to continuously improve its ability to detect malware and rogue networks.
Many new Mobile developers have integrated deep learning into its endpoint security products that provide what it calls “predictive security.” The company aims to extend this deep learning layer to all endpoints, including mobile ones. It has also introduced an email protection tool that uses the same technology to intercept more threats before they can make it onto the endpoints.
Other vendors see an opportunity to use AI to help IT departments that are stretched thin to make sense of all the data that is gathered by their existing endpoint management tools. Among them is Citrix, whose unified endpoint management offering also manages all devices that enter the workplace, including laptops, mobile phones, tablets and wearable. The Citrix security analytics application monitors those devices and helps IT to apply security policies and ensure that the network remains secure.
Citrix Analytics also performs user-behavior analytics, applying machine learning to categorize users as high, medium or low risks and then adjusting the risk scores as more data comes into the system.
IBM, meanwhile, has developed MaaS360 with Watson, a cloud-based application designed to help IT administrators make sense of the massive amounts of data generated by endpoints and their users, apps and content. It applies cognitive technologies to security, end-user productivity, mobile app management and administration.
Enterprise mobility management users are inundated with more information than they can absorb about apps, configuration/policy best practices, productivity tools, and emerging threats and vulnerabilities, IBM explains. IBM MaaS360 delivers cognitive insights, embedded in the platform, to help organizations wade through the information they’re gathering and distill it into insights and recommendations that are relevant to their business. The core of MaaS360 is IBM Watson technology, which can index and annotate huge volumes of datasets to look for relevant data that applies contextually to each individual client deployment of MaaS360.
A privacy backlash? One dark cloud on the AI/ML front is data privacy.
Users have become more aware of the perils of their personal information ending up in the hands of companies such as Facebook and Google, etc. So the idea of an employer or other company retaining the outputs of their mobile devices, apps and data usage which some calls workplace analytics is sure to meet opposition from some users.
These concerns can’t be ignored, especially given the emergence of strict regulations such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act of 2018. These regulatory concerns could strip the utility from mobile offerings dependent on AI/ML. While user push back on data will not negate the value of AI/ML in mobile offerings, it could impede the collection of data for some or all users and without any data the results will be deteriorated. That, in turn, could make the data less useful for some groups of users or some regions, while still providing value to others.
To improve this, organizations must forthright in discussing what data they collect and how it will be used. May big IT players advises clients to illustrate the outcome and its benefit to users and take pains to note what won’t be collected or done with data. The list of what IT does not do with data should almost always be longer than the list of what it does or can do with data.
Feel free to contact me at ravindrapande@gmail.com in case need any further details.
