Why Proactive Threat Monitoring is Crucial: Unveiling the Invisible Risks

TL;DR: Lack of system visibility leads to undetected threats and increased security risk. Choose a cost-effective threat monitoring solution that offers comprehensive, real-time data aggregation, continuous monitoring, and customizable detections for high-fidelity alerts and a stronger security posture. 

Was there outbound traffic to a non-approved DNS server? Was there a suspicious escalation in account privileges or an unauthorized access to an administrator account? Without visibility into your systems, it’s impossible to know and these threats will go undetected. In this blog post, you’ll learn how to start monitoring for threats to give your security team the visibility to prevent incidents before they happen.

Why is threat monitoring important?

Okta, the giant in enterprise-level identity management, faced one of the aforementioned threats when attackers breached the Okta support system in October, 2023 through unauthorized access to administrator accounts. Clients notified Okta when they detected and blocked breach attempts using compromised credentials. A month later, the gravity of the Okta breach was still being uncovered

This incident reminds us that while attacks like these are common, it’s proactive threat monitoring that consistently identifies and mitigates them. 

Threat monitoring is the process of analyzing systems to search for and identify violations to system security, including indicators of compromise, attack attempts like ransomware or distributed denial of service (DDOS), and failure to comply with regulation. It proactively searches for threats before they escalate into incidents, enabling teams to address threats more efficiently, reduce overall risk, and comply with standards and laws.

Now contrast this to reactive security, which focuses on responding to an attack after it has materialized using measures like forensic analysis and remediation. While reactive security measures need to be in place, security teams need to take their security to the next level with proactive threat monitoring. 

Threat monitoring is built on logs

Logs contain information about your tech environment and the events that are taking place in it, like when a login occurs, or when policies or configurations change. So gaining visibility into your system relies on reviewing and understanding your system’s logs. This process is called log monitoring, where logs are collected, analyzed, and acted upon. 

In threat monitoring, logs from infrastructure, services, apps, and other endpoints are collected and analyzed to detect threats. Threat detections are developed from commonly known tactics described in globally-recognized standards, like the MITRE ATT@CK framework. When a threat is detected, the threat detection system sends an alert to the security team with relevant information to identify, prioritize, and neutralize the threat.

The value of continuous monitoring and log aggregation

Security teams achieve comprehensive threat monitoring when logs from all sources are continuously monitored and the data is aggregated. 

Continuous log monitoring captures events at all times, maintaining uninterrupted coverage. The most effective tools offer threat monitoring in real-time, allowing security teams to act on threats immediately. 

Log aggregation collects logs into a single database. This enables the threat detection system and security practitioners to holistically analyze and correlate the aggregated logs, which uncovers the context to attacks, improving overall response.

Together, these practices ensure complete system visibility, which is crucial to effective threat monitoring and detection. Over time, this data becomes valuable threat intelligence that can be queried to understand current and past attacks, as well as predict and prevent future attacks. 

Threat detection and response solutions

Various solutions are available to meet the needs of threat monitoring, each with different capabilities that help teams identify zero-day attacks, malware infections, ransomware encryption, and data theft. Here are the three most common solutions:

eXtended Detection and Response (XDR) 

An integrated solution, XDR automates the collection and correlation of data across multiple sources—endpoints, networks, email, servers, and cloud workloads—for end-to-end, real-time threat detection and response. XDR uses artificial intelligence (AI) and machine learning (ML) for automated threat response.

XDR is an evolution from Endpoint Detection and Response (EDR) and Network Detection and Response (NDR), both of which have a smaller scope: EDR focuses on endpoints, and NDR focuses on networks.

Managed Detection and Response (MDR) 

MDR is an outsourced service providing threat monitoring, detection, response, and proactive threat hunting by cybersecurity experts. This is a 24/7, fully managed service that leverages human expertise along with technologies like XDR and SIEM.

Security Information and Event Management (SIEM)

SIEM solutions aggregate, normalize, analyze, and correlate log data from system sources to identify and alert on threats, in real-time. SIEM offers a centralized view of an organization’s security information and events that allows teams to gain security insight, comply with regulation, and audit and manage data. Security teams might use XDR/EDR solutions alongside a SIEM. 


Choosing the right threat monitoring solution affects efficiency and effectiveness of security operations. Not only that, but security tools must always measure up to budget constraints. To that end, here’s a breakdown of the use cases for each solution, including their pros and cons, so you can make the right investment for your team: 

Use cases for XDR

Organizations should choose an XDR when security teams need incident detection that includes real-time, automated triage and response for all devices within a system.


  • Reduced alert fatigue through event correlation and more accurate alerts, and automated triage and response for incidents.
  • Improved threat intelligence, investigation, and prevention through aggregated data that is retained, normalized, and correlated to understand current, past, and future attacks. 
  • Improved operational efficiencies through centralized configuration of system monitoring,  reduction in tool sprawl, and real-time automated triage and response to incidents. 


  • Lacking tools to enforce regulatory compliance as the primary purpose is threat detection and response.
  • Implementation complexity, despite the ability to reduce tool sprawl.
  • Different XDR providers may have a narrower scope or quality control for monitoring or response automation, which may lead to increased false positives and alert fatigue.
  • More expensive than a SIEM when requiring additional hardware and software to access telemetry data. 

Use cases for MDR

Organizations should choose an MDR when they lack internal security resources or expertise and need a turnkey solution to manage cyber security. 


  • Eliminates the need for internal security teams, reducing operational complexity.
  • Access to cybersecurity experts and advanced services, which can enhance security efforts and otherwise may not be possible for a small internal security team. 
  • Continuous security monitoring with 24/7 threat response and remediation.


  • Cost of outsourcing can be high.
  • Scalability can be slow when relying on an outsourced service to monitor new sources. 
  • Limited control and customization in cybersecurity efforts.
  • Implementation can remain complex even without direct management of cybersecurity.

Use cases for SIEM

Organizations should choose a modern, cloud-based SIEM when security teams need scalable threat detection and response that enables regulatory compliance and auditing, and the ability to customize detections and correlate events from all sources for high-fidelity alerting, in real-time.


  • Enhanced regulatory compliance through compliance management and auditing.
  • Reduced alert fatigue through customizable detections and increased alert accuracy. 
  • Low/no overhead with cost-effective, modern SIEMs based in the cloud.
  • Improved threat intelligence, investigation, and prevention through aggregated data that is retained, normalized, and correlated to understand current, past, and future attacks. 
  • Improved operational efficiencies by reducing tool sprawl and centralizing threat detection and response. 


  • Legacy SIEM has high operational costs and overhead. Avoid these pitfalls by choosing modern, cloud-based SIEMs.
  • Poorly written or untailored detections can increase false-positives and alert overload. 
  • Implementation limitations and deployment complexity with legacy SIEM.

Detection-as-code: The future of threat detection

Despite the efficacy of these threat detection and response solutions, practitioners face persistent challenges with ease of use, accuracy, and coverage. When any of these challenges aren’t addressed, missed alerts increase, as does alert fatigue, operational inefficiencies, and coverage gaps. In other words, security practitioners are not able to do their jobs effectively.

Modern security teams are choosing SIEMs with detection-as-code to alleviate these challenges. 

Detection-as-code enables teams to write detection rules in a programming language, like Python, and manage them in version control. This gives teams the flexibility to customize detections, cover security gaps, and increase signal-to-noise so that all alerts are accurate and relevant:

  • The deployment process is streamlined. Teams can easily create, update, or rollback detections, using peer-review processes like pull-requests (PRs).
  • Auditing, visibility, and collaboration is improved, enabling any team member with access to the repository to understand, review, and contribute to detections. 
  • Testing and deployment can be integrated into CI/CD pipelines, fully automating work and ensuring consistency.
  • Widely-used programming languages have many built-in tools and libraries that make developing and customizing detection logic that much easier. And using expressive languages like Python make it simple to understand detection logic at-a-glance.

When paired with a modern, cloud-based SIEM, threat detection with detection-as-code is backed by a robust security data lake and high scalability with zero-ops. 

Now that you’ve reviewed the options, which threat monitoring solution is right for your organization? 

Learn from industry peers

Learn how other companies leverage detection-as-code to enhance security operations:

Panther is the cloud-native SIEM built for modern security teams. Request a demo today.

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