When I first got involved in detection engineering, I saw it the way most practitioners do — writing correlation rules, refining signatures, and responding to alerts. The job felt structured, almost mechanical at times. But over the years, as I spent more time analyzing real-world threats and observing how attackers operate, a persistent thought kept surfacing — is this enough?
I’ve come to realize that sticking to known patterns, signatures, and black-and-white rule sets simply won’t cut it anymore. Threat actors have evolved — they’re smarter, faster, and often operate in the gray areas where our rules fail to trigger. Today, they don’t always use malware or easily identifiable tools. Instead, they blend into legitimate system processes, abuse trusted utilities, and execute attacks in ways that often slip past traditional detections.
This shift made me — and my team — question our approach. Were we just scratching the surface while missing the bigger picture? That’s where our journey towards maturing our detection engineering practice truly began.
One of the biggest realizations was how underutilized telemetry like Sysmon logs can be. Too often, logs are treated as forensic breadcrumbs — something you turn to after an incident has occurred. But what if we flipped that narrative? What if telemetry became our real-time storyteller, continuously painting a picture of everything happening inside the environment?
We started reframing our perspective. Instead of hunting for known attack tools or specific hash values, we began asking deeper, more behavior-centric questions:
- What does credential theft really feel like at the system level?
- How does lateral movement behave if we strip away the tool names and focus purely on actions?
- What traces does a successful post-exploitation stage leave, even if no malware is involved?
Pursuing these questions led us to incorporate adversarial simulation like Atomic Red Team into our workflow. We began running controlled attack chains using well-known frameworks like Metasploit — not just to see alerts fire but to genuinely observe the environment’s response. How do child processes spawn? What registry changes occur? Which files get touched? What network patterns emerge?
These exercises opened our eyes to patterns and behaviors that traditional rules rarely cover. It stopped being just about writing detection rules — it became about understanding the adversary’s journey. And once you start seeing the attack in layers — initial access, privilege escalation, lateral movement, impact — your entire approach to detection changes.
I won’t claim we’ve mastered it — this is a continuous process. But every simulation, every detection failure, every behavioral insight pushes us a little further. It’s no longer about chasing the perfect detection rule but building resilience in how we monitor, detect, and respond.
The way I see it, detection engineering is no longer a backend function — it’s a craft that sits at the heart of cybersecurity operations. It demands curiosity, persistence, and a willingness to break your own assumptions repeatedly.
As we continue this journey, one thing has become abundantly clear — the future of detection engineering lies in thinking like the adversary and designing defenses that can adapt as quickly as the threats we face.
I’ll share more as we refine our approach, but if there’s one takeaway so far, it’s this: evolving threats demand evolving defenders. And that evolution begins with how we approach detection, every single day.