Detection Engineering forms the backbone of a modern Security Operations Center (SOC). It ensures proactive threat identification by creating and refining detection logic based on real-world attack patterns. This process is a continuous lifecycle that evolves dynamically with the ever-changing threat landscape.
At The Collective, our Managed Detection & Response service features a dedicated Detection Engineering team responsible for maintaining an up-to-date and effective detection base.
This specialized team effectively bridges Threat Intelligence with SOC Operations to deliver precise detections and unparalleled visibility into the activities on environments they work on thus uncovering hidden blind spots which may have eluded other operational teams. By actively minimizing false positives, they help to significantly reduce analyst fatigue. Furthermore, by treating detection logic as code, they enable robust version control and scalability across a diverse array of clients.
Detection engineering components
Detections are not a static, one-time setup; they are part of a continuous cycle designed to anticipate, adapt to, and outpace malicious threat actors. This necessitates the creation of new rules, meticulous maintenance of existing ones, and the retirement of rules that become irrelevant over time. By combining threat intelligence, automation, and Tactics, Techniques, and Procedures (TTP)-based logic, we ensure we consistently stay ahead of the curve. On top of this, our Detection Engineering team focuses on a number of key components.
Ingestion and visibility
Every good detection rule starts with complete visibility. This is why we onboard and normalize log sources which are from Microsoft-native products as well as other services (e.g.: firewall vendors, web proxies, RMM tooling, etc.). This guarantees clean and structured data, serving as a foundation for detections that are accurate and meaningful to the analysts.
Threat modeling and use case design
Using the MITRE ATT&CK framework as our primary threat modeling reference, we meticulously map out attack scenarios and design detection rules tailored specifically to our customers’ environments. This approach means that many of our detections are not generic rules found in public repositories. Instead, they are built with proprietary logic developed by our own detection engineers. Adversaries are aware of the angles covered by public rules, but they are not privy to the custom defenses we have prepared.
Validation
Before any detection rule goes live and creates incidents for active investigation by the SOC analysts, it undergoes a crucial validation period. During this stage, rules are deployed to trigger alerts, but they do not automatically raise a ticket with the SOC.
This validation phase is essential for gauging the accuracy of a rule and determining how much refinement is required for its logic. This is particularly important for rules that work on historical data.
This process of tweaking the rule before a go-live reduces false-positives thus lowering the risk of alert fatigue among the analysts. Furthermore, it significantly reduces the likelihood of operational impact on customer infrastructure through automated actions such as device isolation.
Development and deployment
Detection rules are not something which is just an abstract, theoretical concept. In order to be effective, queries must be developed to track down suspicious or even malicious behavior. The rules are first built by querying real data across all our customers, weeding out false-positive hits along the way.
Next, a template of this rule is made and pushed to a separate branch in our Azure DevOps repository. This template defines the query the rule is supposed to run, the frequency at which it runs and much more!
After the template is successfully pushed to the separate development branch, it is merged into our testing environment, where the detection engineering team can spot potential mistakes that could have slipped through the cracks. This is paramount to make sure that broken rules do not get pushed to customer environments.
After a testing period on the rule, the template gets merged into the acceptance branch, making its way to the production branch eventually which triggers a CI/CD pipeline that pushes the detection rule to all the customers’ environments where it will alert on behavior it was built to find.
Continuous improvement
Regular reviews and a strong feedback loop from the frontline analysts who work on the incidents are vital for helping the Detection Engineering team revisit and update rules at the right moment. This ensures detection coverage remains sharp and relevant, reduces noise, and ultimately increases confidence in the accuracy of every alert.
Why does this matter for you?
Your business is unique, and your security posture should be too. At The Collective, Detection Engineering is not a one-size-fits-all operation. Every rule we create and every automation we deploy is designed with the customer’s specific environment in mind. This means our detections are not generic templates pulled from a library; they are crafted to reflect the realities of the threats your organization actually faces.
When attackers take one step forward, we take two. Not just in a broad sense, but specifically for our customers. The level of customization implemented by our Detection Engineering team translates into tangible benefits: fewer false positives, faster response times, and a security posture that operates as a seamless extension of your own team.
Your detection range should be precisely tuned to your world, not someone else’s. If you’d like to learn more on how to protect your environment by using custom detection rules, just reach out to our team.