Endpoint management plays a critical role in protecting and securing company networks. Controlling and securing these endpoints reduces the risk bad actors exploit unpatched vulnerabilities and ensures every endpoint is configured to function optimally. However, traditional endpoint management is very manual and reactionary, which could lead to security gaps and configuration drift across devices.
While tools and platforms can automate much of the manual work, they are very rule-based, meaning they can only do what they’re programmed to do. And while this works for most companies, AI-driven endpoint management is poised to take endpoint security and management to the next level with real-time monitoring and automated responses that learn and adapt to the nuisance and complexity of your system.
What is AI-Driven Endpoint Management?
AI-driven endpoint management in this context means combining artificial intelligence (AI) and machine learning (ML) to automate endpoint security decision-making processes. Leveraging AI and ML in endpoint management can dramatically reduce the risk of security breaches and enhance security measures while cutting down on the manual effort IT makes in securing endpoints, allowing them to work more efficiently.
Many AI-driven endpoint management and security solutions use real-time information, helping companies defend against increasingly sophisticated cyber threats and attacks. As the information updates, so does the system, allowing it to predict, adapt, and respond quickly.
How AI-Driven Endpoint Management Works
Your endpoint management platform installs AI agents on each endpoint. The agents constantly monitor each endpoint in real-time, learning from the data they gather to establish a baseline of “normal” behavior for each endpoint. When the artificial intelligence agent notices that something is “off,” the agent evaluates the new behavior, makes a prediction based on historical data, then takes action without human intervention. Real-time monitoring and autonomous decision-making allow the system to detect emerging problems and remediate them quickly.
Here’s an example.
An AI agent is installed on every Windows laptop and establishes a baseline performance for every endpoint. The agent detects that one endpoint is running something in PowerShell that could be malware. Because the agent knows what is and isn’t “normal” for this endpoint, it evaluates if the command was user-initiated and what the command is doing (say, touching sensitive registry keys), then compares the current activity on this endpoint to its past behavior.
Using all the available data, the agent predicts and decides if the activity is malicious or not and takes action. It may suspend the process, quarantine the advice, notify the security team, and collect and report data. And this all happens autonomously, keeping the IT team for other tasks and defending against emerging threats as they happen.
How Is an AI Agent Different From a Traditional Endpoint Agent?
If the AI agent example sounds familiar, that’s because it’s similar to a traditional agent your endpoints may already have. These agents follow a set of rules or scripts to perform management or security tasks, making their detection and response systems reactive. An AI agent is proactive. It learns, adapts, and adjusts as your needs change and endpoint security threats evolve.
Benefits of AI-Driven Endpoint Management
AI-driven endpoint management offers operational efficiency and security that other endpoint managers can’t. It can increase productivity, fine-tune device performance, and enhance your system’s functionality.
Real-Time Threat Detection
AI-driven endpoint management platforms continuously monitor your system to learn what is and isn’t normal for every endpoint, user, and the entire system, establishing a baseline for activity. When something out of the ordinary happens, the agent evaluates the behavior in context and compares it to the baseline. If the behavior or activity is considered threatening or malicious, the agent deploys countermeasures against the emerging threat, improving the company’s overall security posture and defending every endpoint from cyber threats.
Remote Work Environments
Even if a company isn’t remote, many employees may work remotely, whether on the road or logging in from home after hours. While this increases employee efficiency and productivity, these endpoints are often outside the corporate network and away from direct IT oversight.
An AI-driven endpoint manager eliminates these issues and protects the endpoint by continuously monitoring and responding to suspicious behavior on the endpoint and using machine learning to detect changes in behavior that could indicate a security breach, like a remote user accessing a sensitive system or logging in from an unfamiliar location.
Enterprise Security
Most endpoint management platforms integrate Zero Trust principles to protect against ongoing and emerging threats. This “never trust, always verify” principle means that every access request is treated as risky until proven otherwise.
AI and machine learning enhance Zero Trust principles. Instead of being static and rule-based, it’s dynamic and adjusts, automatically enforcing security policies. For example, the AI agent assesses device posture in real time, scanning for outdated patches or suspicious behavior. If an endpoint fails a security check, the system can isolate it, revoke credentials, or restrict access, without waiting for a human to intervene, defending against revolving cyber threats and improving your security posture.
Centralized Visibility and Smarter Insights
Most endpoint management platforms can manage any and every device that connects to your network. Consolidating device management gives the security operations team visibility into the devices and helps them coordinate operations, security, and user experience, leading to improved efficiency.
When AI is part of that platform, this unified endpoint management is enhanced. While the platform can automate routine tasks and gather data about performance metrics and usage patterns, the AI agent takes this a step further and learns from it, making predictions and offering actionable insights, helping the team make informed decisions and optimize their endpoint security and management strategies.
Streamline Tasks
Most automated endpoint managers eliminate the need for manual intervention by IT. However, AI-driven endpoint management learns how to prioritize and deploy patches across all of your endpoints quickly and efficiently without interrupting workflow.
For example, instead of deploying a patch when it’s available, an AI endpoint manager can identify any high-risk exploits and prioritize patching vulnerable endpoints, while deferring less urgent updates to minimize disruptions across the company. Similarly, the agent can automate resource optimization, like adjusting settings and configurations to reduce system strain.
Challenges of Adopting AI-Driven Endpoint Management
While AI-driven endpoint management can change the way your company manages and secures endpoints, the newness of the technology presents challenges.
Risk of False Positives
While AI and ML can learn, they evaluate behavior statistically, meaning they may lack the context-aware judgment a human might use. The data-driven analysis can lead to false positives, which could result in legitimate processes being interrupted or users being locked out of their devices.
Too many false alarms can erode trust in the agent, create more “noise” for IT, and even interrupt business-critical work. Reducing these false positives is possible, but it requires time to train the agent and the ability for admins to safelist behaviors or override the agent’s decisions.
Lack of Visibility
Many AI agents make decisions based on your security policies and configurations, but how the AI makes those decisions isn’t clear. Rule-based systems are straightforward in that the action follows clear logic, but that’s not always the case with an AI agent. It may use statistics to make a decision, but that decision is based on a complex model that isn’t easy to interpret.
For example, if the agent isolates a device, why did it do it? Was the device behaving abnormally? Did the system detect a known malware signature? Without knowing the “why” behind the action, IT may be unable to verify if the action was appropriate or reproduce the incident to learn more. The lack of visibility can lead to a loss of confidence that the platform is reliable or a need to contact the vendor for more information.
Data Quality
All artificial intelligence and machine learning are only as “smart” as the data it was trained on, and for endpoint security, that data is what they collect from devices, users, and the network. Incomplete, inaccurate, or inconsistent data can lead to an incorrect baseline, which creates endpoint security gaps.
For example, the agent may have limited visibility into unmanaged or off-network devices (like a personal laptop). This limited visibility creates an inaccurate baseline, which could cause the agent to miss an emerging threat because it didn’t know what to look for or was looking in the wrong place.
Deployment and Integration
AI-driven platforms aren’t exactly plug-and-play, and the specifics of your endpoint devices, tech stack, and headcount can impact how easily you can integrate AI into your endpoint management.
Installing AI agents can be difficult when you have an array of endpoint devices. What you install on Windows devices may not work for macOS or BYOD devices, requiring multiple builds of the same agent. Legacy systems may not support or conflict with AI agents, and the agent may need a specific OS or hardware to run.
Even if deployment is simple, training and adoption may not be. Admins must learn new workflows, and end users may need guidance to understand how their device behavior may have changed (like certain apps being blocked or why an endpoint may reboot). And every AI often requires fine-tuning the training and ongoing adjustments. Organizations that don’t have staff with the time and skills to do this training and adjusting may not get the most out of the system.
Privacy Concerns
Though AI isn’t exactly new, companies that adopt AI may find themselves with new privacy and compliance concerns. As noted above, the system is only as good as the data you train the agent on, and the more detailed the data, the better. That data could include application usage, network connections, geolocation data, or user behavior and login history.
Giving the agent as much data as possible improves its real-time monitoring, detection, and response, but also creates privacy and compliance concerns, particularly for organizations in sensitive sectors (healthcare, legal) or that are subject to GDPR or HIPAA regulations. What’s more, even if the data isn’t used to monitor employees, end users may feel that they are being tracked.
