There are a few moments in your professional journey that truly shake your perspective, moments that force you to pause, rethink, and rewire your approach. For me, that moment came when we ran our first adversarial simulation exercise against our own detection content.
We’ve always taken pride in the detection logic we build, the use cases crafted, the effort spent testing them, the layers of logic designed to flag malicious behavior. But watching a simulated attacker effortlessly slip past some of our strongest rules was nothing short of humbling. And honestly, that experience turned out to be one of the most valuable lessons for me as a detection engineer.
What the Simulation Taught Me
1. Attackers Don’t Follow Your Use CasesOne of the first hard-hitting realizations was that adversaries don’t care about the use cases we design. We had our detection rules mapped to specific attack scenarios, thinking we were ready for what the attacker might throw at us.
But once the simulation began, the “attacker” did what real-world adversaries do, they chained techniques, abused trusted binaries (LOLBins), escalated privileges creatively, and generally operated in the spaces and patterns we hadn’t considered. That’s when it really hit me: detection engineering isn’t about writing static rules for known behaviors, it’s about thinking like the attacker, constantly predicting what’s next and possible patterns.
2. Behavioral Detection Outperforms SignaturesI had always appreciated the importance of behavioral detection in theory. But nothing drives that point home like seeing signature-based detections fail in real time.
From what I’ve seen in the past, carefully crafted Metasploit payloads have a way of slipping past static detections. The framework is designed to morph payloads, tweak execution paths, and before you know it, your signature-based logic is left scrambling, chasing traces that no longer exist.
What held up, and eventually caught the activity, were the behavioral patterns we had invested time in: suspicious process chains, anomalous token impersonation, unusual registry changes. That was the moment I truly understood, behavior will always outlast static indicators in this game.
3. Detection Engineering is a Team SportPerhaps the most overlooked learning was the collaborative aspect. This wasn’t just a detection engineering exercise, it was a combined effort involving our Threat Intelligence team, the DataOps folks, and even members from Detection Engineering.
We sat together, reviewed logs, analyzed behaviors, and broke down where we failed. And that’s when I realized, building solid detections isn’t just a technical challenge; it requires diverse skill sets. Reverse engineers, threat intel analysts, red teamers, data specialists, everyone brings something critical to the table. Without that mix, our detection coverage remains limited.
How It Changed Our Approach
Post that simulation, our approach to detection engineering shifted fundamentally. Today, we prioritize:
- Designing behavior-driven rules, especially leveraging Sysmon logs.
- Scheduling adversarial simulation exercises, not as a one-off, but as an ongoing practice.
- Exploring community-driven research and threat intelligence to stay ahead of evolving adversarial techniques.
Most importantly, we’ve accepted that detection engineering isn’t a one-time project. It’s a living practice that needs continuous refinement.
My Takeaway
Running that adversarial simulation was a turning point for me, not just professionally, but also in how I now approach every detection challenge.
It made me realize that building detections in isolation, without pressure testing them against real-world attack patterns, is like constructing a fortress without testing the gates. You might sleep well, until someone walks right in.
If there’s one recommendation I’d give to any team working on detections, test. Simulate. Break your own logic. Because until you do, you’ll never know what you’re missing.
And that lesson, I carry forward every single day.