Collecting security alerts is not the problem most organisations face today. The real challenge is deciding which alerts matter and acting on them quickly enough to make a difference. Many businesses are dealing with an overwhelming volume of data, yet still struggle to identify genuine threats in time to respond.

This is where detection engineering is starting to make a real impact. It shifts detection from a reactive activity into something more structured, measurable and aligned to actual risk.

Rather than relying purely on tools to generate alerts, it introduces a more deliberate way of designing how threats are identified and handled.

The challenge with traditional detection

The journey of any alert starts with triage. This is where decisions are made about how quickly something needs to be investigated and who should handle it. If this process is poorly defined, the outcome can be slow response times, missed threats or alerts being passed to the wrong team.

Many organisations invest heavily in detection tools, but these are often built to work across a wide range of environments. As a result, they do not always provide the context needed to identify more specific or targeted activity. This is particularly true when it comes to insider threats or unusual behaviour within known systems, where detection relies on a baseline understanding of how users normally interact with technology.

Frameworks, such as the Cyber Assessment Framework (CAF), highlight the importance of aligning detection capability with the threats an organisation actually faces. Without this alignment, it becomes difficult to justify the data being collected or the cost of bringing in new sources that may provide higher value. In many cases, a lack of governance makes it harder to understand what is needed and why.

Understanding the complexity of detection

Detection engineering brings structure to this problem, but it is not without its challenges. A useful way to understand detection is through the concept often referred to as the pyramid of pain.

At the lower end are simple indicators such as file hashes, IP addresses and domain names. These are relatively easy to detect and are often supported directly by security tools. However, they are also easy for attackers to change. A small modification to a file can generate a completely different hash, allowing it to bypass detection.

At the other end of the spectrum are tactics, techniques and procedures. These describe how an attack is carried out rather than the specific tools used. Detecting at this level is more complex but also more effective, as it is harder for attackers to change their behaviour entirely.

This is where detection engineering starts to add real value. Instead of looking for single indicators, organisations can look for patterns of behaviour. For example, a sequence of actions that would not normally occur together, such as one process launching another followed by unusual network activity. By linking these events, it becomes easier to identify something that needs investigation and introduces confidence into the triage process.

Some organisations are now introducing what could be described as building block detection. Individual rules capture specific behaviours, while higher level logic looks for customisable combinations of those behaviours occurring within a short period. This creates a trail that analysts can follow and reduces the likelihood of false positives.

Using automation to strengthen detection

Automation is a key part of making detection engineering practical. While lower level indicators are often seen as less valuable, they can still play an important role when used correctly.

The key is understanding that this type of intelligence has a limited lifespan. A malicious IP address or file hash may only be relevant for a short period. Automation allows organisations to ingest and act on this information quickly, increasing its value.

For example, known malicious indicators can be used to block traffic, disrupt communication from malware or identify compromised accounts. When combined with enrichment, this can provide useful context for investigations.

Automation also has a role to play after a detection has fired. A well designed detection rule should provide enough information to support decision making and, where appropriate, trigger a response. This might include isolating a device, revoking access or flagging an account for further review.

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To support this, detection rules need to be designed with consistency in mind. Standardising outputs makes it easier to integrate with other systems and automate actions. It is also important to consider when automation should make a more considered approach, particularly for high value accounts or sensitive actions where additional checks may be required.

Embedding process and governance

Detection engineering is not just about writing better rules. It is about building a repeatable process that can be maintained and improved over time.

Each detection should include context. When was it created, what does it look for, how confident is the organisation in its accuracy and what should happen next. This helps analysts understand what they are dealing with and reduces the time needed to respond.

Linking detections to frameworks such as MITRE ATT&CK can also provide valuable context, helping teams understand how an alert fits into a broader attack. This can make it easier to identify related activity and respond more effectively.

Governance plays an important role as well. Organisations need to know which data sources are required, how long data is retained and how often detection rules are reviewed. Tracking metrics such as false positives and relevance over time helps ensure that detection remains effective and aligned to emerging threats.

Building resilience through better detection

Detection engineering is ultimately about improving defensive capability. It allows organisations to move beyond relying on generic alerts and towards a more targeted, risk driven approach.
The most effective detections are those that provide clear context and can be acted on quickly. They explain why an alert has been raised and what should happen next. This reduces uncertainty and allows teams to respond with confidence.

By focusing on high risk areas, such as critical systems or insider activity, and continuously refining detection based on real world experience, organisations can build a capability that evolves alongside the threats they face.

Detection engineering is not a one off exercise. It is an ongoing discipline that requires investment in people, process and technology. Those that embrace it will be better equipped to detect threats earlier, respond more effectively and maintain operational resilience in an increasingly complex environment.