1. Introduction
It’s more important than ever to protect an organization’s cyber world but as technology keeps changing, so do the bad guys and their methods. They’re always coming up with new and sneaky ways to get around the defenses.
That’s where detection engineering comes in. Detection engineering acts as a digital watchdog for organizations, responding to known threats and constantly checking for the slightest hint of a potential breach. This approach gives defenders the upper hand in the virtual cat-and-mouse game. [1]
1.1 What is detection engineering?
Detection engineering is all about designing, setting up, and keeping an eye on detection systems that spot and flag any suspicious activity in an organization’s digital space. This discipline is all about understanding the nature of cyber threats, analyzing system and network behavior, and creating rules or algorithms that can identify threat indicators or attack patterns. [2]
The goal of detection engineering is to help organizations quickly and accurately spot cybersecurity threats before they cause serious damage. The process is about coming up with new ways to spot threats, testing them out in the real world and in simulations, making improvements based on how well they work, and adapting to the ever-changing tactics, techniques, and procedures (TTPs) used by attackers. To do detection engineering right, you’ve got to understand the technical and tactical sides of cybersecurity. You also need to know how to work with complex data systems, analytics tools, and security event management (SIEM) platforms. [3]
1.2 Importance and Benefits of Detection Engineering
In today’s world, cyber threats are getting more and more complex and widespread. It’s clear that traditional security measures aren’t enough. Detection engineering is becoming really important for organizations that want to be able to spot and respond to threats effectively.
1.2.1 Importance of Detection Engineering
There are lots of reasons why Detection Engineering is important, not least because it is changing the way organizations approach cybersecurity. Traditionally, security measures have been more about reacting to threats after they happen. On the other hand, detection engineering is all about taking a proactive approach to defending against cyber threats. It helps organizations detect and address malicious activity before it causes any real damage. It’s really important to catch these things early, so we can stop them from getting worse. [15]
Detection Engineering fills in the gaps left by traditional security measures. It makes sure that systems can effectively detect and respond to new and emerging threats.
On top of that, Detection Engineering also cuts down on mean time to response (MTTR), which is a big part of keeping cyber incidents from causing too much trouble. If you catch threats early, you can respond faster and reduce the potential damage and financial losses. Faster response times can be the difference between a minor security incident and a major attack. As they say, time is money. And outage costs are estimated at $1,467 per minute.
On top of helping with day-to-day operations, Detection Engineering also helps organizations meet compliance and regulatory requirements like ISO 27001 and SOC 2. If you can prove you have effective detection mechanisms in place, you can show you’re ready for cyber threats. This helps you avoid legal issues and potential fines. On top of that, a solid detection system helps keep an organization’s reputation clean and builds trust with stakeholders. In today’s digital age, where data breaches can really hurt a company’s reputation, investing in Detection Engineering shows that you’re committed to protecting your data and interests. [1]
1.2.2 Benefits of Detection Engineering
The structured approach of Detection Engineering offers a lot more than just threat detection. Another big plus is that we can cut MTTR by automating the detection process. Automating detection means organizations can catch threats faster and more consistently.
Another thing about Detection Engineering is that it can adapt to the specific environment it’s used in. Vendor-supplied detection rules and signatures are designed to be generally applicable, but Detection Engineering is different. It focuses on finding and addressing detection deficiencies specific to the organization’s environment. This customization makes detection mechanisms more accurate and relevant, which helps security teams avoid false positives and false negatives.
Another great thing about Detection Engineering is that it offers a structured and teachable workflow. The process is set up in a way that lets security teams work together well, share what they know, and keep making improvements to how they detect threats. [14]
Evaluation and testing are key parts of the Detection Engineering process. They help us find and fix bugs and vulnerabilities. This way of working also makes it easier to work with other teams, like pentest or threat hunting teams, and to bring their insights into the wider security strategy for the company. [4]
Importance of Detection Engineering |
| Proactive defense |
| Lower your incident response time |
| Compliance & regulatory requirements |
| Preserving reputation & trust |
| Evolving threat landscape |
1.3 Key Components of Detection Engineering
To stay on top of threats, we first need to know what they are. That way, we can focus on the threats that apply to our environment. So, what exactly is a threat? A threat is the point where hostile intent, capability, and opportunity intersect. [5]
1.3.1 Threat Modeling
Threat modeling is the basis of detection engineering. It involves creating visual representations of an application or infrastructure to identify potential threats and then identify and determine appropriate risk mitigation measures. [13]
There are lots of ways to build threat models, including using flowcharts and attack trees. Flowcharts help us identify possible entry points, while attack trees show all the ways an attacker could try to get into your system. Plus, detection engineers can use risk analysis tools to rate an asset’s vulnerability based on factors like damage, discoverability, exploitability, and impact on affected users.
We need to set some goals. What do we want to achieve? Before you get started with threat modeling, it’s important to figure out what you’re trying to accomplish. At this point, we set some goals. For example, we want to protect the confidentiality of the system, ensure the integrity of data, and be able to deliver services even under attack. It’s also a good idea to decide how much time and money you’re going to spend on this process.
Next, we’ll create a visual representation of what we’re building. Next, we need to document the system components. This documentation process includes different visualizations, such as data flow diagrams, process flow diagrams, and deployment diagrams. These documents help people understand how the system works and how users interact with it.
Next, we need to identify the threats. What could go wrong? It’s important to identify any potential threats to your system and how these could affect it. In this step, we’ll look at the diagrams to see how your assets could be compromised and who might be trying to attack them.
Now, let’s think about how we can deal with these issues. Once you’ve identified the threats, you need to figure out which ones you’re vulnerable to. Once you’ve looked at these vulnerabilities, you can take steps to reduce the risk. For instance, you could remove a specific feature, reduce its functionality, or strengthen the system architecture.
Time to check in and see if we did a good job. Finally, you just need to make sure you’ve covered all the bases when it comes to the threats. Any remaining risks should be clearly documented, and preparations for future threat modeling cycles should be made. It’s important to remember that threat modeling isn’t a one-time thing. You should do it regularly, or at certain stages of the system development process. [6]
1.3.2 Detection Objective
Threat modeling is the first step in Detection Engineering. It starts with identifying threats relevant to an organization. The MITRE ATT&CK framework is a great tool for helping the organization figure out how to detect threats by looking at where there might be weaknesses. There are a few key things to think about when it comes to the detection objective:
- What do I need to keep an eye on?
- Which threat actors, techniques, tools, etc. are relevant to us?
- How can I show it’s relevant to the business?
If we can answer these questions, we can put together a plan to deal with the threats our organization is facing.
1.3.3 Detection Requirements
Next, we need to identify the logs or data sources that our organization already has and those that we need to get to detect the threats we’ve identified. We’ll also look at vulnerability reports and figure out where our security is weak.
At this stage, we need to decide what we’re looking for. To do that, we need to answer a few questions:
- How can I detect X?
- What kind of log records or data sources do I need to do this?
- What’s the best way to detect this?
1.3.4 Detection Implementation
It’s really important to keep on top of false positives and other details when it comes to the specific pieces of content written for detection. So, the next step is to use the answers to the questions above to put together a plan for how an organization will manage detection capabilities on an ongoing basis. As you look over the detections you’ve already written and make any necessary adjustments, keep these questions in mind:
- How can I make detection more automated?
- Is this something that would be better as a dashboard, recorded search, report, or rule?
Since detection engineering is a continuous process, having a supportive culture is essential for stability. [7] [16]
1.4 Detection Engineering Process
It’s an ongoing process that changes as times change.
Step 1: Threat Identification and Intelligence Gathering
The first step is all about understanding the threats. This means gathering intel on new threats, tactics, techniques, and procedures used by cybercriminals. You can use threat publications, industry reports, incident analysis, and more to get your intelligence.
Step 2: Threat Modeling and Risk Assessment
Now that you’ve gathered intelligence, you can assess your organization’s unique vulnerabilities and potential attack vectors. This step helps us figure out which threats are the most relevant and could have the biggest impact on the organization.
Step 3: Developing Detection Strategies
Once you know what threats and vulnerabilities your organization faces, detection engineers will create strategies to detect possible attacks. This step is all about creating detection rules or algorithms that can spot signs of malicious activity in different data sources.
Step 4: Implementation and Integration
Next, you’ll want to put the detection strategies into place within the organization’s security infrastructure. You’ll need to integrate the detection mechanisms with the existing security tools and systems to make sure you’re monitoring everything across the organization’s digital infrastructure.
Step 5: Testing and Validation
It’s important to test how well the mechanisms work and make sure they’re accurate. In this phase, we test the strategies and make any necessary tweaks. We test the systems to make sure they don’t produce false positives or negatives in different scenarios.
Step 6: Monitoring and Alerting
Once the detection systems are up and running, it’s important to keep an eye on them. The systems scan data in real time to look for signs of trouble. If the system spots a threat, it’ll send out an alert so the security team can take a closer look.
Step 7: Incident Response and Mitigation
As soon as an alert comes in, the incident response team will spring into action. Next, they’ll look into the potential threat and take the necessary steps to limit the damage. This is an important step if you want to keep the impact of a security incident to a minimum.
Step 8: Post-event Analysis and Feedback
Once an incident has been resolved, we’ll do a deep dive to understand the attack vectors, tactics used, and how our detection and response mechanisms performed overall. You should use what you’ve learned from this analysis to help you identify threats and develop strategies in the future.
Step 9: Continuous Improvement
The Detection Engineering process isn’t over once the steps above are complete. It needs to be kept up to date with new threats, new technology and what we’ve learned from past incidents. Continuous improvement cycles like this make sure that detection capabilities stay effective and in line with your company’s security needs. [8]
1.5 Detection Engineering Areas
At first, the idea of Detection Engineering was just about spotting random activity. These days, though, it’s expanded to include things like security monitoring, incident response, malware analysis, threat intelligence, and digital forensics.
Security Monitoring: Detection Engineering can help organizations keep an eye on things proactively. This includes workflows like applying Sigma rules, applying IOCs (Indicator of Threat) to logged data, and sharing important info with SIEM (Security Information and Event Management).
Incident Response: Detection Engineering helps organizations respond to incidents more effectively. It can set up YARA rules, strengthen IOCs, and set targets for detecting malicious tools on target systems or disk images.
Malware Analysis: Detection Engineering can help organizations take malware analysis to the next level. It’s useful for extracting IOCs and defining YARA rules.
Threat Intelligence: It can also help you spot corrupted documents and toolkits, which is another way it can strengthen your Detection Engineering threat intelligence.
Digital Forensics: Detection Engineering is a big help in digital forensics. For instance, it can create YARA policies for extracting specific data, such as victim information or details about extracted data. It can also help security experts put together a list of keywords that are relevant to the case. [9] [10]
1.6 Detection Engineering Tools and Technologies
Cybersecurity professionals use a variety of tools and technologies to effectively detect and prevent threats.
Security Information and Event Management (SIEM)
SIEM systems let cybersecurity pros collect and analyze data from IT infrastructure across the company. These systems are really important for spotting potential security threats and incidents. They also help make security operations more effective with features like real-time monitoring, event log management, and incident response.
Machine Learning and Behavior Analytics
Cybersecurity pros can spot security threats more easily with machine learning algorithms. These algorithms learn from past data to spot unusual behavior and flag anything that’s out of the ordinary. This helps us find vulnerabilities faster and more effectively.
Log Management Solutions
Logs are a great source of info for cybersecurity pros. Log management solutions collect, store, and analyze log data from servers, apps, and security devices. This helps security pros catch and deal with incidents faster.
Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR)
EDR tools are used to spot and deal with cyber threats on endpoint devices. XDR takes this a step further, giving cybersecurity pros more comprehensive visibility across broader layers of the network. These tools are great for defending against particularly complex and multi-layered threats.
Threat Intelligence Platforms
Security pros use threat intel platforms to learn about the tactics, techniques, and procedures (TTP) used by cyber attackers. These platforms help keep an eye on known threats and make sure the organization’s defense strategies are up to date. So, you can set up a defense mechanism that’s ready to deal with potential threats.
These tools and technologies are essential for effective detection engineering in cybersecurity. Using the right tools is a great way to keep organizations safe from cyber threats. [8]
1.7 What is the Difference Between Threat Hunting and Detection Engineering?
In the realm of cybersecurity, detection engineering and threat hunting are often mistaken for one another, yet they serve distinct purposes. The table below highlights the main differences between the two disciplines, focusing on key features that set them apart. [11]
| Feature | Detection Engineering | Threat Hunting |
| Threat Awareness | Focuses on known threats | Targets unknown threats |
| Use of Infrastructure | Enhances detection mechanisms using existing security tools | Leverages tools to seek hidden threats |
Focus | Centers on detecting specific artifacts | Focuses on suspicious behaviors |
| Process | Balances detection with minimizing false positives | Accommodates non-malicious results that may show suspicious behaviors |
| Automation | Designed for automation | Requires careful interpretation by skilled threat hunters |
[12]
References
[1] https://www.splunk.com/en_us/blog/learn/detection-engineering.html
[2] https://medium.com/@rcxsecurity/what-is-detection-engineering-and-why-do-i-need-it-e430bb8b28b3
[4] https://www.uptycs.com/blog/threat-research-report-team/what-is-detection-engineering
[5] https://www.sans.org/blog/purple-teaming-threat-informed-detection-engineering/
[7] https://www.crowdstrike.com/cybersecurity-101/observability/detection-engineering/
[8] https://www.appsecengineer.com/blog/what-is-detection-engineering
[9] https://www.wallarm.com/what/detection-engineering
[10] https://cyb3rops.medium.com/about-detection-engineering-44d39e0755f0
[11] https://medium.com/@zendannyy/detection-engineering-or-threat-hunting-331d77f672c0
[13] https://www.xcitium.com/detection-engineering/
[14] https://panther.com/cyber-explained/detection-engineering-benefits/
[15] https://socprime.com/blog/what-is-detection-engineering/[16] https://seculyze.com/resources/microsoft-sentinel-consultancy/blue-team/detection-engineering/