What Is Detection Engineering?

Threat detection is the process security operations teams take to spot these threats in their environments. Detection engineering involves using data from endpoint and other security tools to build detection “rules,” queries designed to spot specific anomalies that could indicate an attack in progress.

Understanding the threat landscape is crucial for developing effective detection rules. Detection authors should take into account attack trends like ransomware, threat actors and their current methods of choice, and an organization’s history with past threats. This knowledge helps to create well-rounded detection rules and to fine-tune them as the landscape evolves.

The Evolution of Detection Engineering

Initially, detection engineering relied on simple signature-based methods that matched known threat patterns. As cyber threats grew more complex, these methods weren’t enough.

Today, detection engineering uses advanced techniques like machine learning to analyze large datasets, identifying patterns and predicting potential threats. This approach allows systems to continuously adapt and improve. By also integrating threat intelligence, it provides real-time insights into emerging threats, enabling proactive defenses. Real-time detection capabilities further enhance this by allowing immediate identification and response, making detection engineering more effective.

Core Components of Detection Engineering

Detection engineering encompasses a number of components to be a robust defense system. It’s critical to have the right authors, the right data, and a central location to store completed detection rules.

Once built, a detection rule is added to a detection library, where new rules can be added, modified, and deprecated. Having a single source of truth for all detections captures the expertise of many detection authors, ensuring a comprehensive and well-rounded defense.

Detection Authors: The Expertise Behind the Rules

A detection author, simply put, is the person or organization that wrote the query that forms the rule. There are many types of detection authors, including internal security operations teams, technology authors, and third-party security providers. A good detection engineering library should include detections from each author type:

  • Internal detection author: Develops rules that are specific to organizational threats and requirements or tailored to the organization’s environment and technologies.
  • Technology vendor author: Provides out-of-the-box detections specific to their technology. These rules are quick and easy to deploy, but are not tuned to the organization’s environment, which can lead to high rates of false positives.
  • Third-party detection author (e.g., an MDR provider): Offers more advanced detections, drawing on the extended expertise of a broader stable of detection engineers.

Data Sources: The Foundation of Detection

Detection engineers need access to diverse data sources from across their environment to build out their queries. There are four main types of data that should be taken into account when writing a detection query.

1. Foundational Security Technologies

The most important datasets you need are those provided by foundational security tools, including:

  • Operating systems
  • Network
  • Identity and access management
  • Endpoint detection and response
  • Email security
  • Business-critical applications
  • Cloud security
  • Industry-specific tools, like operational technology or point-of-sale systems

2. Historical Data

It’s important for detection authors to understand how an organization has addressed and resolved threats in the past. This helps identify patterns from previous incidents and improve the accuracy of new detections and provides a baseline that can help identify anomalies.

3. Threat Intelligence

Threat intelligence is timely, relevant, and actionable information about attack trends and threat actor techniques. Incorporating this data into new detection rules and amending old rules to reflect any new information can help you uncover emerging threats and enrich the fidelity and accuracy of your detections. Some types of threat intelligence are:

  • Indicators of compromise (IoC) threat feeds
  • Industry-specific threat intelligence
  • Threat profiles (threat actors and campaigns)
  • Deep and dark web intelligence

4. Business-Specific Data

To enhance monitoring and protection for high-value targets, it’s crucial to incorporate business-specific data—such as critical assets and executive accounts—into your detection strategy. Below is a list of common business-specific data to include for preventing high-impact breaches:

  • Critical assets
  • Executive usernames
  • Brand assets
  • Registered domains
  • IP ranges
  • Business operational knowledge

Crafting Robust Detection Rules

Crafting detection rules is both an art and a science. These rules are the backbone of any detection engineering strategy, serving as the logic that identifies potential threats. Detection logic involves carefully structuring conditions and criteria that trigger alerts when certain behaviors or anomalies are detected. This requires a deep understanding of both the environment and the potential threat vectors.

An effective approach to crafting these rules is through “detection as code.” This concept treats detection rules like software code, allowing for better version control, collaboration, and testing. By using coding principles to develop detection rules, security teams can streamline updates and ensure consistency across environments, enhancing the overall robustness of the detection strategy.

Given the complexity of modern cyber threats, it’s crucial to leverage a variety of detection authors to ensure that your detection library is well-rounded and prepared to tackle a wide array of threats.

Detection Orchestration

Detection orchestration ties together all components of detection engineering. It involves coordinating the detection library and storage technologies using an orchestrator to ensure seamless integration and operation. The orchestrator acts as the central hub, managing the deployment of detection rules across different environments.

Detection deployment can occur at-source, where security data is generated, or at-storage, e.g., in a SIEM or other data-storage technology. Each approach has its benefits:

  • At-source deployment can provide a much shorter mean time to detect (MTTD).
  • At-storage deployment allows for more comprehensive detections involving multiple source tools.

The choice between these strategies depends on organizational needs and infrastructure capabilities.

Advanced Detection Methods

Staying ahead of evolving threats requires advanced approaches. Leveraging threat intelligence, real-time detection capabilities, and machine learning when creating and tuning detection rules is pivotal in maintaining a proactive defense posture.

Leveraging Threat Intelligence

Threat intelligence is invaluable for proactive defense. By understanding the behaviors and tactics of threat actors, security teams can develop detection rules that anticipate attack vectors. This intelligence is dynamic and so must be integrated into detection processes using both closed sources, like commercial threat intelligence platforms, and open sources, like public threat feeds, to maintain relevance and effectiveness.

The Intersection of Machine Learning, AI, and Detection Engineering

Machine learning and AI analyze vast amounts of data to identify patterns and anomalies that might be missed by traditional methods. This capability results in better accuracy, helping to reduce false positives and enabling security teams to focus on genuine threats.

One of the key benefits of AI is its ability to learn and adapt over time. As new threat vectors emerge, AI systems can evolve, improving their detection efficacy without constant manual updates.

Integrating machine learning into detection processes requires careful management to prevent false positives. However, when implemented correctly, machine learning can significantly enhance detection capabilities by providing more accurate and timely alerts. This ensures that security teams can focus their efforts on genuine threats, improving overall security posture and response efficiency.

AI and machine learning can also leverage historical data to forecast future threats and trends, allowing organizations to take proactive measures against potential vulnerabilities.

Operationalizing Detection Engineering

Comprehensive and accurate deployment of detections is crucial for a resilient and adaptable security posture. A well-engineered detection system that encompasses an organization’s entire data environment enhances incident response accuracy and speed, allowing security teams to focus on genuine threats and reduce containment and response times.

Incident Response and Detection Engineering Synergy

The relationship between detection engineering and incident response is symbiotic. Accurate detection systems improve incident response by reducing false positives and enabling faster threat identification, leading to rapid containment and minimized impact. Automated containment actions, such as isolating infected systems, can further streamline the response process.

Threat Hunting: Refining and Validating Detections

Detection engineering and threat hunting, when integrated effectively, also complement each other to enhance threat identification and response. Threat hunting findings can help detection engineers refine detection rules, improving their accuracy and effectiveness. Simulating attacks or analyzing past incidents allows for validation and testing of detection tools, ensuring they perform optimally.

The Future of Detection Engineering

As cyber threats become more sophisticated, the role of AI and machine learning will become increasingly prominent. These technologies are expected to automate and enhance threat detection processes, providing deeper insights and faster response times.

Human expertise will remain crucial, however, as the strategic oversight and nuanced understanding of security professionals are indispensable for interpreting AI-generated insights and making informed decisions. As such, a collaborative approach that combines human intelligence with technological advancements will be key to maintaining a resilient security posture.

Centralized detection orchestration and management, facilitated by platforms like ReliaQuest GreyMatter, will further streamline detection processes, ensuring that all security tools work in harmony. These platforms will provide a unified view of the security landscape, enabling more efficient management and quicker adaptation to new threats.