EDR Security: How EDR Supports an Endpoint Security Strategy

What is Endpoint Detection and Response (EDR)?

Endpoint detection and response (EDR) solutions continuously monitor end-user devices to detect and enable security teams to respond to threats like device compromise, malware and ransomware. EDR security solutions record all activities and events occurring on endpoints, providing real-time visibility that can help uncover sophisticated threats.

This is part of a series of articles about endpoint security.

How EDR Enhances Security

An EDR solution continuously monitors all endpoints connected to the network, proactively hunts threats, and launches responses. EDR insights can help block threats and analyze ongoing and past attacks. Typically, EDR solutions let you automate many processes to achieve continuous visibility while ensuring human operators remain productive.

Deep Visibility

EDR solutions provide comprehensive visibility, covering the following areas:

• Deep visibility — Providing insights into the inner workings of the endpoint and inspecting the relationships between network connections, processes, and user behavior.

• Wide visibility — EDR includes centralized management to provide a wide view of the overall security posture. It helps human operators spot patterns across hundreds or thousands of endpoints.

Simplified Incident Response

EDR solutions collect a rich level of detail that can help significantly simplify response and remediation activities in the aftermath of a breach. Traditionally, incident responders spend much time collecting artifacts from endpoints to create a large pool of evidence.

EDR solutions minimize this manual work by collecting and storing these artifacts during normal operations. Centralized EDR consoles and long data retention periods can offer a more accurate picture of a certain security incident.

Automation and Integration

EDR solutions often provide robust automation capabilities and custom integration through APIs. It typically involves installing EDR agents on all endpoints across the organization. As a result, teams can rapidly initiate investigation or response activities at scale.

Reduced Alert Fatigue

As networks face more data, endpoints, and attacks, human operators are bombarded by a rapidly growing amount of security alerts. Receiving so many unfettered alerts can quickly lead to alert fatigue and reduced productivity. Adopting advanced tools that use algorithmic decision-making helps eliminate most false-positive alerts, freeing human operators to focus on real security events and higher-level investigations.

Data Analysis and Threat Hunting

EDR solutions perform real-time analysis to facilitate a rapid diagnosis of threats that do not correspond to pre-configured rules. These analytics engines employ algorithms to look for patterns by correlating and evaluating big data, providing next steps suggestions in response to confirmed threats. A false positive triggers threat cancellation and the solution records this information for future analysis.

Key Considerations When Looking For an EDR Security Solution

Coverage

Each organization has a different network design that meets its unique requirements, business needs, and compliance regulations. A network can include servers and networks that are more disconnected than those working within a traditional network, several cloud vendors integrated into the design, incorporate distributed teams, and allow personal devices. An EDR solution needs to capture the diverse nature of the environment to ensure all endpoints are monitored.

Agent vs. Agentless EDR

Some EDR tools use an agent to achieve visibility into endpoints, while other solutions gather endpoint data using an agentless. Each approach provides unique benefits and disadvantages, including:

• Agent-based EDR — An agent is a software program you install on each device to monitor it. The agent collects data on user activity across all applications, system areas, and web pages and transmits the data collected on each session to a central server for processing, storage, and analysis.

• Agentless EDR — This option does not require installing agent software on endpoint devices. Instead, the EDR solution passively monitors all traffic passing through the network as it flows between users’ client machines and accessed servers.

In many cases, enterprises use both agent-based and agentless EDR models to cover all endpoints.

Operating Systems Support

An EDR agent must be available for certain operating systems, which is why most EDR tools support Windows, Linux, and macOS. However, many EDR vendors do not offer support for Apple iOS and Google Android operating systems, despite the fact many workers use smartphones and tablets to access the corporate network.

If no agent is available for a popular operating system, you need to revert to using different ways to monitor activity and collect data from these unsupported devices.

It also means EDR solutions might not offer support for Internet of Things (IoT) devices since many do not run industry-standard operating systems like Windows or Linux. Additionally, some IoT devices are CPU- and memory-constrained and cannot support EDR agents. You might need an alternative method to an agent to include IoT devices in the network data capture and analysis process.

EDR Security in the Cloud

EDR tools often operate from the cloud but cannot operate in the cloud. However, cloud security posture management is a critical component of a broader security program, especially for organizations using cloud-based servers and workloads, such as containers and serverless workloads. It is typically impossible or impractical to install an agent on cloud-based physical or virtual devices.

Up-to-date Research

Threats change daily as attackers continuously work to improve their tactics, techniques, and procedures (TTPs), creating a dynamic threat landscape that constantly evolves. As a result, tools need to regularly change the models and signatures used to detect the presence of threats.

EDR solutions require frequent updates to gain insights into known threats, including high-quality indicators of compromise (IoCs) and indicators of attack (IoAs). Most tools use machine learning (ML) to analyze network and endpoint activities to identify anomalies that can indicate threats. ML analysis involves using algorithms or models, which require frequent tuning to ensure they continue to produce accurate results when detecting anomalies.