From shrink wrapped to ready-to-roll, managing the entire device life cycle from procurement to end of life, you can automate it all with the Mobile Device Management (MDM) solutions harnessing the power of artificial intelligence. In the realm of device management, the amalgamation of artificial intelligence with MDM solutions has transformed the future of the workplace into a new era of cost optimization, enhanced productivity, persona-based user experience and proactive device management strategies. This new approach is enabling organizations to better manage and secure their devices such as laptops, desktops, smartphones, tablets across, Apple, Windows, Android or any other operating systems.
Let’s explore how AI and MDM fusion is reshaping the landscape of device management.
AI-based threat detection and prevention
In the past, MDM solutions had to rely on manual processes to detect and block threats. However with the rise of AI, MDM solutions are now able to detect unknown threats and respond to the same in real time. The combination of AI, Generative AI and MDM strengthens the security measures by analyzing and identifying potential vulnerabilities in real-time. By leveraging these technologies, you can understand the nature of threats with rich event data including user and host information, details on malware and the threat signature. These insights enable fast-track incident resolution with contextual data specific to the malicious activity detected. This proactive approach ensures implementation of robust security protocols to safeguard against threats and breaches.
MDM augmentation by machine learning and intelligence
MDM uses machine learning that enables organizations to block zero-day phishing attacks, crypto jacking and malicious domains in real-time. This periodically scans a device to inspect it for any signs of attack and allows end users to receive device security notifications and perform security inspections.
MI:RIAM (Machine Intelligence: Real-time Information and Analytics Machine) is Jamf’s (MDM vendor purpose-built for Apple device management and security) advanced AI and machine learning threat intelligence engine that evaluates unknown domains in real-time to block likely threats. This engine learns about new threats based on insights collected on all enrolled devices, as well as wireless and cellular traffic and network connections. As an example, in 2021, MI:RIAM identified, blocked and protected customers from over 120,000 zero-day phishing domains, whether they came by email, SMS, third-party apps or browser. Information gathered can include OS versions, security update versions, system parameters, and device configuration details, as well as modification of system libraries and attempts at privilege escalation. Using advanced data science, MI:RIAM for Jamf collects information from 425 million sensors worldwide as input for its algorithms to provide real-time insights into the latest threat intelligence and active risks.
Detection criteria for advanced threat detection
The AI ML engines for MDM can monitor wireless and cellular network traffic for suspicious or malicious behavior such as spoofed certificates and connections to known phishing domains. For each analyzed domain, it looks for a specific set of criteria commonly used by suspicious domains, including non-unicode characters, legitimate brands in an unknown domain and suspicious keywords.
It automatically analyzes all applications that can introduce risk to the user and their environment. It uses a set of detection criteria such as dangerous permissions (ex: applications requesting access to your camera, contacts, network access), suspicious developer profiles, anomalous characteristics, malicious code patterns and many more parameters. Depending on the number of matching criteria for each analyzed app or domain, it assigns a risk score that will then trigger an alert.
There are AI-coupled MDM solutions that can do a deep forensic scan by gaining extended visibility into your mobile fleet from anywhere, with rich mobile endpoint telemetry. This further reduces manual investigation time from weeks to minutes. These AI-coupled MDM solutions look for common signs of a compromised phone such as a sudden reboot, battery drain, problem accessing mic and camera, no updates etc. It can automatically construct a timeline of suspicious events that shows when and how a device was compromised.
Enhanced authentication and conditional access
AI powered MDM solutions can be trained to analyze unique user behavior patterns such as swipe patterns or keystroke dynamics, fingerprint, iris, palm, or face recognition that uses deep learning, classification algorithms, or neural networks to verify biometric information. This information can be leveraged to enhance the MDM authentication features.
Additionally, these MDM solutions can use machine learning algorithms to identify anomalies that deviate from security compliance policies. For example, if a user is taking multiple screenshots before leaving the organization, it is detected as suspicious and that user’s access to all sensitive data is immediately restricted and his screenshots are deleted.
Persona-based user experience
AI algorithms, when combined with the MDM device management platform, enable persona-based device configurations for different user groups. This fusion allows for tailored custom settings and preferences to enhance user experience and productivity.
Automated troubleshooting and support
Integrating AI with MDM empowers administrators with automated troubleshooting capabilities. AI-driven systems can proactively resolve device issues, reducing downtime and improving the overall efficiency of device management. AI algorithms complement MDM automation capabilities by further streamlining routine tasks. This fusion optimizes device management workflows to reduce manual intervention and increasing operational efficiency.
Automated scripting for device management
Generative AI-powered macOS scripting tool empowers IT admins to use natural language to create scripts and manage device hardware fleets with little scripting knowledge. It lets admins ask for and receive ready-to-use scripts for thousands of commands such as “check the battery health status”.
Predictive maintenance and remediation
By harnessing the power of Gen AI algorithms, MDM providers can analyze device usage patterns, anticipate potential performance issues and recommend remediation workflows or optimization methods. This predictive maintenance approach aids in self-healing before any performance issues even impacts the device or the user.
Data-driven decision making
AI algorithms, coupled with device management solutions, generate actionable insights by analyzing vast amounts of data. These algorithms monitor events across the operating system whether it is a process creation, system event, download, file transfer, screenshot, synthetic click or keylogger. AI-driven MDM solutions also provide insights into device usage patterns.
For example, AI can identify which apps are being used the most, which app or webpage consumes the maximum data, and which ones have suspicious code patterns, allowing admins to make informed decisions about application usage policies as well as device deployment, security protocols and resource allocation.
Synergy between AI and Mobile device management marks the beginning of evolution in device management and security
The future of the device management landscape
The synergy between AI and MDM marks the beginning of a continuous evolution in device management and security. At present, this is only inclined towards accelerating processes or providing insightful recommendations. But the future is about enabling a self-driving vehicle for MDM, with the adoption of Gen AI algorithms.
This partnership between Gen AI and MDM opens the doors for future innovations, potentially revolutionizing how organizations manage and optimize device ecosystems.
The seamless integration of Gen AI-driven capabilities with a MDM’s specialized device management platform represents a leap forward in device management strategies. It automates the entire device lifecycle and lays the foundation for a ticketless service desk future, where devices are managed proactively with unprecedented precision and foresight, and ultimate perfection.
