How to Take on the Insider Threat to Cybersecurity

Let’s take a closer look at the problem of insider threats, and discuss ways to minimize their risks.

Insider Threat Types and the Need for Multiple Solutions

What constitutes an insider threat? There are several different types:

• Careless Insider. Among the most common type of threat, this is typically an innocent person who unwittingly exposes the system to outside threats. Often they have inadequate cyber hygiene training, which can lead them to falling victim to email phishing or other scams. 
• A mole. Usually this is an outsider that has gained insider access to a privileged network, and is posing as an employee or partner.
• Malicious insider. This is a person who intentionally abuses legitimate credentials — usually to steal information for financial or personal gain. These people are familiar with the organization’s security policies and procedures, making them particularly dangerous.

Malicious insiders can cause considerable damage. But how can an organization know that it has been exposed to a malicious insider threat? 

Some of the easiest threat indications to track are due to unusual behavior. Activity at unusual times is often a sign. Other causes for concern include unusual volumes of traffic, or transferring too much data across the network — or accessing resources outside of the employee’s job duties.

When it comes to malicious insider threats, a single solution does not provide adequate protection. Malicious insiders can avoid being detected if they are familiar with the organization’s existing security measures. They also may be able to skirt firewall and intrusion detection systems, especially if they can clear the hurdle of an authorized login.

Insider Threat Solutions: What to Consider

Insider threat detection strategy must be diversified, combining several tools. That way insider behavior can be monitored and filtered through multiple alerts, to eliminate false positives.

Insider threat protection tools that utilize machine learning (ML) applications can help analyze data streams and prioritize the most relevant alerts. User behavior analytics can establish a baseline for normal data access activity, and database activity monitoring can help identify policy violations.

The cybersecurity industry offers a range of tools for monitoring how users move through the network, and for protecting data. Whichever solutions you choose, it’s important to remember that they must protect data on premises, in the cloud and in hybrid environments. They must also give security teams visibility into the way in which data is accessed and moved throughout the organization.

To ensure an organization has a truly comprehensive solution, there are several key features to consider:

• Database firewall: The purpose here is to block SQL injection or other threats, while checking known vulnerabilities.
• User behavior analytics: This sets baselines for data access behavior, often using machine learning to detect suspicious activity.
• User rights management: This monitors data access and activities of privileged users for excessive or inappropriate user behavior.
• Data masking and encryption: This makes sensitive data useless to bad actors, even if it is accessed.
• Data loss prevention (DLP): DLP inspects data in motion, at rest on servers, in the cloud, or at endpoints.
• Database activity monitoring: This capability monitors relational databases, data warehouses, big data and mainframes, and creates alerts when policies are violated.
• Alert prioritization: This feature makes it possible to look across all security events and to give priority to the most significant ones. 

Cybersecurity risks from insider threats are only going to become more commonplace and more expensive. By knowing the types of insider threats and how industry solutions can reduce their risk, you will be better equipped to weather the next attack.