Insider threats can be challenging to deal with. Their prominence demands attention, but they can be hard to identify, and businesses don’t want to interrupt people’s work because of false positives. User and entity behavior analytics offers a solution.

What Is Behavior Analytics?

Behavioral analytics is a subset of machine learning focused on identifying how users or devices tend to act. In a security context, that means setting a baseline for normal behavior to detect suspicious activity.

This kind of analysis is important because it can be difficult — at times, almost impossible — to prevent all account compromise attacks. Considering credential-related issues cause over 60% of cloud compromises, that’s a dangerous gap. Perimeter defenses also fail to stop malicious insiders.

Behavior analytics helps by identifying when a user or device isn’t acting as it should. Security teams can then recognize potential breaches faster for more effective responses.

6 Steps to Prevent Insider Threats With Behavior Analytics

Roughly 30% of CISOs today cite insider risks as one of their biggest security threats, so firms must do more to address them. Here are six steps to deploy behavior analytics to do just that.

1. Set Goals and Expectations

The first step in applying behavioral analytics is the same as any machine learning application. Brands must determine how they want to use these solutions, including what specific issues they’ll address.

Defining the scope of these projects early is important because behavioral analytics can become expensive and complex if enterprises aren’t careful. That’s particularly relevant to SMBs, as they face more internal threats than large companies but have smaller budgets. Highlighting specific optimal use cases early leads to more cost-effective implementation.

Amid this process, teams should also set relevant KPIs and benchmark their current threat detection performance. This planning will make it easier to optimize the behavioral analytics solution later.

2. Generate User and Device Profiles

Once IT teams understand their needs and goals, they can find or develop a behavior analytics system. The first step in actually deploying it is generating profiles for each user and — budgets permitting — other entities, such as known endpoints.

This kind of analytics only works if it has an accurate baseline for normal or permissible behavior. Consequently, security teams should collect as much relevant data on these actions as possible. That includes system logs, identity management data, users’ browsing history, login data and corporate directories.

As with any AI project, data cleansing and organization are crucial to ensure model accuracy. It may be best to automate this stage, as data scientists spend 39% of their time preparing and cleaning data on average. Automated data cleansing solutions will let teams deploy the final behavior analytics engine faster.

3. Identify High-Risk Users and Devices

The behavioral analytics solution is technically ready to deploy at this point. However, it’s best to go further and assign a risk score to each user and entity.

Recognizing high-risk users isn’t necessary for insider threat detection to work, but it makes it more practical. About 25% of insider attacks are accidental, so users can still present a risk even if they’re trusted completely.

Similarly, alert volumes and related fatigue have gotten so high that teams ignore up to 30% of alerts, potentially leading to costly breaches. Assigning a risk value to each incident simplifies triage, letting security professionals know when a situation demands immediate attention.

High-risk users and devices are generally those with more access permissions. Organizations can also assign risk scores to different data sets or applications. The important thing is the analytics model can distinguish between low-level and high-level threats, whatever that means in a specific company’s context.

4. Monitor for Suspicious Behavior

After following these initial setup steps, businesses can start monitoring for suspicious behavior with their analytics model. In general, they’ll work by alerting relevant IT workers when a user or entity’s behavior falls outside of the established baselines.

It’s important to recognize that abnormal behavior doesn’t necessarily suggest a breach. Consequently, security teams may want to set specific parameters for what constitutes a suspicious event, too. This further refinement will reduce alert fatigue and minimize the risk of false positives.

Consider tailoring the model to focus on behavioral anomalies more in line with the unique threats brands are most concerned about. Alternatively, teams may require alerts for all cases involving high-risk profiles while only highlighting low-risk abnormal behavior if it falls far outside the baseline.

5. Inform Users

Once the behavior analytics solution is in place, IT teams should inform users of the new system. Insiders may complicate things if they receive alerts but don’t know about the analytics system, its role and where it may fall short.

As many as 74% of all breaches involve human elements like errors or privilege misuse, and these are often accidents rather than malicious intent. Telling everyone about the new analytics system and how it might affect access privileges will help prevent these events.

Employees should understand that they should be more aware of what they do on work systems to prevent unnecessary alerts from this monitoring solution. Similarly, IT workers should recognize that the solution will be imperfect at first, so they should be patient with it before smoothing the initial bumps.

6. Optimize Over Time

Like any automated system, behavioral analytics requires ongoing optimization. IT teams should continuously measure performance along the same KPIs they used to set their original goals.

Over time, trends will emerge about the system’s alerts. Enterprises can use these insights to refine their definitions of abnormal behavior or expand their normal usage baselines. Similarly, they may need to reassess what constitutes a high-level vs. low-level risk.

The longer firms use these tools, the more they can refine them to boost their reliability. This ongoing approach does mean the solution will take more time and resources, but the eventual return will be greater.

Behavior Analytics Is the Key to Insider Threat Prevention

Insider threats are an increasingly prominent issue as social engineering and errors rise. Behavioral analytics can spot and contain these threats where conventional methods fail.

Any machine learning solution involves some initial costs and complexity that can pose challenges to some teams. However, the results will more than compensate for these issues if businesses implement behavior analytics properly.