Detection engineering & model tuning
An evidence-led approach to investigating model behavior, refining detection logic, and evaluating changes.
Professional context · Methodology overviewUnderstanding the context.
Detection engineering is an analytical discipline: understanding what a signal captures, where it falls short, and how a change behaves in context. This overview connects the experience described in my 2026 resume to a general, nonproprietary methodology. It does not reproduce an internal model or customer investigation.
The question to answer.
Examine detection-model performance issues without assuming that every alert is malicious or that lower alert volume necessarily means better detection.
Professional context.
My 2026 resume documents resolving more than 40 model-performance and tuning issues and independently developing more than five detection models. The approach below is a general methodology, not a reconstruction of specific employer work.
An evidence-led workflow.
Characterize the issue
Separate unexpected model behavior, missing context, and potentially suspicious activity. Define the question before changing the logic.
Research the behavior
Evaluate the relevant attack technique and its observable behaviors. Look for legitimate workflows that could produce similar signals.
Evaluate detection logic
Consider context, signal combinations, coverage, and false-positive trade-offs. Proprietary thresholds and model details are intentionally excluded.
Test representative cases
Use authorized, representative benign and suspicious examples. Compare the baseline and candidate behavior, document limitations, and review unexpected results.
Review and monitor
Record the rationale, obtain appropriate review, and monitor subsequent behavior. Keep changes reversible and avoid unsupported performance claims.
Analytical building blocks.
Listed as methodology inputs; not an assertion that every tool was used in a specific incident.
What can be claimed.
Professional context supplied: 40+ tuning issues resolved and 5+ models independently developed. No confidential performance data, accuracy uplift, or reduction in risk is claimed.